Forthcoming and Online First Articles

International Journal of Autonomous and Adaptive Communications Systems

International Journal of Autonomous and Adaptive Communications Systems (IJAACS)

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International Journal of Autonomous and Adaptive Communications Systems (57 papers in press)

Regular Issues

  • A Hole Repair Algorithm For Wireless Sensor Networks Based On Virtual Attractive Force Constraint   Order a copy of this article
    by Ting Hu 
    Abstract: There are some problems in the traditional algorithm, such as long running time and poor coverage effect. In this paper, a new algorithm based on virtual attractive force constraint is proposed. Based on the virtual attractive force model of intensity-based virtual force algorithm with boundary forces (IVFA-B), aiming at the particularity of ideal distance between heterogeneous network nodes, this paper analyzes the relationship between the perception radius of two heterogeneous nodes and the optimal distance between nodes when realizing the maximum coverage in grid. By combining the best distance and the best distance threshold of virtual force algorithm, the adaptability of heterogeneous network is provided. At the same time, the node moving probability is introduced into the node’s moving distance formula to repair the hole in wireless sensor network node. The simulation results show that the proposed algorithm can achieve better coverage effect and reduce the running time effectively, which proves that the proposed algorithm has better application performance.
    Keywords: Virtual attractive force constraint; Wireless sensor; Hole repair of network node.
    DOI: 10.1504/IJAACS.2023.10034566
  • Integrated Radar Radio: Enabling technology for Smart Vehicle of Smart Cities
    by MITHUN CHAKRABORTY, Debdatta Kandar, Bansibadan Maji 
    Abstract: The growing technological development in the field of information and communication technology has evolved the futuristic concept of smart cities, wherein the objects, embedded with high speed processors and memory, would be intelligent in the sense that they are capable to communicate among each other and can take decision. The smart cities will ensure increased road safety, traffic mobility, sustain environment and economic development. To ensure these features smart vehicle becomes an integral component of the smart cities. The smart vehicles should be equipped with simultaneous intelligent sensing and communication technologies at the back end to enable for increased road safety, traffic mobility etc. This requires the joint operation of radar and communication without interference. The aim of the paper is to develop an integrated radar radio platform without interference between the radar and the radio, facilitating smart vehicles. The concept substantiated here for integrated radar radio
    Keywords: OFDM; radar radio; UWB; IV; IVC; V 2 V; V 2 I; FMCW; ICI; mmW.

  • Performance of RPL under various mobility models in IoT   Order a copy of this article
    by Spoorthi Shetty 
    Abstract: The Internet of Things is a system used primarily for communication where various devices are connected for the collection, analysis and execution of the task required The main challenge in IoT device is, they are resource-constrained Hence, usage of an effective data transmission routing protocol plays an vital role in IoT It is identified from the research that, IPv6 Routing Protocol for Low Power and Lossy Networks (RPL)is an effective routing protocol for static IoT network Along with static network, it is necessary to evaluate the effectiveness of the RPL for different mobility models The energy consumption of the Reference Point Mobility Model (RPGM) is compared in this document with the Column Mobility Model (CMM) for RPL at distinct concentrations of salability using Cooja simulator with Contiki operating system By the extensive experimental analysis, it is identified that the CMM is more energy efficient than the model of RPGM model.
    Keywords: Reference Point Group Mobility Model; Column Mobility model; Internet of Things; Routing Protocol for Low power and Lossy networks.
    DOI: 10.1504/IJAACS.2023.10040423
    by VIMAL SHANMUGANATHAN, Kaliappan M, Vijayalakshmi K, Muthulakshmi S, Selva Ishwarya 
    Abstract: In our routine life, we tend to stumble upon several birds. Bird-watching may be an in-style hobby that offers relaxation in way of life. Infinite individuals look at the class of various bird species while visiting bird sanctuaries., to make the bird watchers easy tool for developed where we can assist birders to acknowledge 60 bird species however we tend to can not ready to acknowledge the kind of that bird species. To beat this downside we tend to stumble upon an answer of building a package as a project. From DCNN formula may be foreseen at 88. We can notice additional correct and stable prediction of the image exploitation random formula in Jupyter notebook.
    Keywords: image recognition; random forest algorithm; internet of birds; deep learning; DCNN.
    DOI: 10.1504/IJAACS.2023.10042235
  • Research On Parallel Association Rules Mining Of Big Data Based On Improved K-Means Clustering Algorithm   Order a copy of this article
    by Li Hao, Tuanbu Wang, Chaoping Guo 
    Abstract: In order to overcome the problems of time-consuming, low precision and redundant rules in association rules mining of big data, a parallel association rule mining method based on improved K-means clustering algorithm is proposed. This paper introduces the matter-element theory of extension, combines matter-element theory and database, and constructs the matter-element relation database model of extension, to realize the mining of parallel association rules of big data on the basis of extension. Redundant algorithm and equivalent transformation are used to eliminate redundant association rules. The experimental results show that the proposed method has high mining efficiency, high mining accuracy and high rule association, which proves that the proposed method has better application performance.
    Keywords: K-means clustering algorithm; Association rules; Data mining; Redundancy algorithm; Equivalence transformation.
    DOI: 10.1504/IJAACS.2023.10042660
  • Heuristic Positioning Method Of Intrusion Nodes In Sensor Networks Based On Quantum Annealing Algorithm
    by Yun ZHAO, Ziwen CAI, Tao HUANG, Bin QIAN, Mi ZHOU 
    Abstract: In order to overcome the problems of low positioning accuracy and long time-consuming in traditional heuristic positioning methods, a new heuristic positioning method of intrusion nodes in sensor network based on quantum annealing algorithm is proposed. This method analyzes the result graph of sensor network and node system, selects the multi-communication radius method to communicate and broadcast among each sensor node, at the same time, refines the number of hops of nodes, and selects the weighting factor to calculate the average hopping moment of unknown nodes. On the basis of the above, through quantum tunneling effect, combined with quantum annealing algorithm, the heuristic positioning of intrusion nodes in sensor network is completed. The simulation results show that the proposed method can effectively improve the positioning accuracy and reduce the running time. The maximum positioning time is only 0.2min.
    Keywords: Quantum annealing algorithm; Sensor network; Intrusion node; Heuristic positioning.

  • Research on Abnormal Data Recognition Method of Optical Network Based on WIFI Triangular Location
    by Bingchen Lin 
    Abstract: In order to overcome the problems of low recognition accuracy and poor reliability of traditional optical network abnormal data identification methods, a new optical network abnormal data recognition method based on WiFi triangulation positioning is proposed. Time series analysis method is used to analyze the channel model of optical network to obtain the temporal characteristics of abnormal data in optical network. Hyperbolic frequency modulation decomposition method is used to detect the time domain characteristics of abnormal data, and the total energy of abnormal data in time and frequency domain is obtained. The abnormal data signal model is established by the energy density characteristics of abnormal data, and the specific position of abnormal data in the abnormal data signal model after filtering is identified by using WiFi triangle positioning algorithm. The experimental results show that the accuracy of the method is higher than 95%, and the recognition performance is good.
    Keywords: WiFi; triangulation; channel model; total time-frequency energy; energy density characteristics.

  • Response Efficiency Optimization of Data Cube Online Analysis for Network user's behavior   Order a copy of this article
    by Hui Zhang, Su Zhang, Xiaoling Jiang 
    Abstract: Data cube plays an important role in online analysis and processing of multi-dimensional data warehouse. Aiming at the problem of long response time and poor compression performance of data query in current methods, the optimization performance of the method is reduced, and a response efficiency optimization method for online analysis data cube based on formal concept lattice is proposed. Firstly, the data of network user's behavior is analyzed and combined with the access frequency of network user. Secondly, the time-varying and stability of data warehouse are analyzed in detail. Finally, slicing and dicing operations in online analysis are analyzed. The experimental results show that the proposed method has a shorter query response time and can quickly retrieve data encoding with better compression performance when the number of fact tables and dimension tables is increasing.
    Keywords: Network user's behavior; Data cube; Online analysis; Response efficiency optimization.
    DOI: 10.1504/IJAACS.2023.10044612
  • IoT Based Vehicular Accident Detection Using Deep Learning Model   Order a copy of this article
    by Ishu Rani, Bhushan Thakre, K. Jairam Naik 
    Abstract: With increase of population and running valuable time, the demand for cars has skyrocketed creating an unprecedented condition in spite of traffic risks and road collisions. The crashes are growing at an unprecedented pace hence it causes death. Now, when Machine Learning has taken over, the previously complex problems have become feasible, and the real-life applications of these artificial ML models have been very promising. In this article, a learning model that learns over an image dataset, thereby classifying never before seen images and data has been proposed. It aims at classifying the real-time accidents based on the level of damage. For that an ANN is utilized to train the model and to learn the similarities among images and accident data. The proposed solution is efficient as it was tried to improve the efficiency of existing model using certain literature mentioned, augmenting different extractions and leaning techniques.
    Keywords: Vehicles; Accident detection; Classification; Accuracy; Deep Learning; IoT; Training model; Image polarity.
    DOI: 10.1504/IJAACS.2025.10046127
  • Twitter Sentiment Analysis using Ensemble Classifiers on Tamil and Malayalam Languages   Order a copy of this article
    by Gokula Krishnan V, Deepa J, Pinagadi Venkateswara Rao, Divya V 
    Abstract: The proliferation of social network is generating a huge amount of texts and drawing attentions Sentiment Analysis (SA) extracts useful information from such data Maximum researches on SA have been done on the English language, but others main languages such as Tamil and Malaya requests obligation too It is pivotal to work on Tamil and Malaya social posts because it is the most spoken language by native speakers and heavily used in social media Although such a crowd, modest work has been done on different languages SA This paper proposes to automatically classify the overall polarity of sentiments expressed in Tamil and Malaya tweets posts by Twitter users in three classes: Positive, Negative and Neutral, and determine a fruitful approach to solve this problem Two samples of Tamil and Malaya languages are collected and later divided into two different types of corpuses Each sample in both corpuses is annotated
    Keywords: English Language; Malaya; Polarity; Tamil; Twitter; Sentiment Analysis; Long-Short Term Memory; Sentiment Analysis; Ensemble Classifiers.
    DOI: 10.1504/IJAACS.2023.10046016
  • A multi-level autopoietic system to develop an artificial embryogenesis process   Order a copy of this article
    by Rima HIOUANI, Nour Eddine DJEDI, Sylvain Cussat-blanc, Yves Duthen 
    Abstract: This paper presents a new model for the self-creation of an artificial multicellular organism from one cell, which is inspired by “The Autopoietic System Theory” at different levels. This theory has been proposed to define the universal self-organization and the self-creation of living systems, the use of this concept allows the development of the artificial organism as a closed organization, and it has been widely used to understand the living systems and their capabilities such as self-organization, self-creation, autonomy, evolution, reproduction... We proposed MLAS “Multi-level Autopoietic System" beside the self-organization to embody this autopoietic system. However, in contrast to the proposed system by Varela, we set it up according to various levels (organs autopoietic machine, tissues autopoietic machine, and cells autopoietic machine). Inside the level of cell autopoietic machine, we proposed the second contribution in this paper, which is a Boolean Artificial GRN with an epigenetic part; lead the cells to create its history during evolution.
    Keywords: Autopoietic System; self-organization; self-adaptation; Artificial Gene regulatory network; evolution; diversity.
    DOI: 10.1504/IJAACS.2024.10047330
  • Unsupervised learning of local features for person re-identi?cation with loss funciton   Order a copy of this article
    by Lunzheng Tan, GuoLuan Chen, Rui Ding, Xia Limin 
    Abstract: Many methods for person re-identi?cation focus on making full use of local features, which typically requires either a comprehensive manual labeling or complex pretreatment. This paper proposes a novel loss function, termed feature channels dropout and de-similarity loss, which drives the autonomous learning of discriminative local features in Convolutional Neural Networks. The proposed loss function consists of two components. The first is a feature channels dropout component designed to compel each feature channel to be discriminative. A novel channel-dropout function and a cross-channel-element-max function are applied in this component. The second component is a de-similarity component that uses Pearson correlation coe?cient to constrain feature channels and ensure they differ from each other. This component is conducive to diverse local features mining. Extensive experiments on three large-scale re-identification datasets demonstrate that the feature channels dropout and de-similarity loss achieves superior performance compared with state-of-the-art methods.
    Keywords: Person re-identi?cation; Local feature; Unsupervised learning; Loss function.
    DOI: 10.1504/IJAACS.2024.10046623
  • Fingerprint Liveness Detection Approaches: A SURVEY   Order a copy of this article
    by Mingyu Chen, Chengsheng Yuan, Ying Lv 
    Abstract: In contemporary society, with the popularity of smart wearable devices, people are more inclined to use convenient and efficient identity verification based on biometrics. Human fingerprints are one of the most commonly used biometric factors, which have the advantages of uniqueness, convenience and security. Compared with traditional password authentication, fingerprint authentication system has extremely high security, but it is still vulnerable to fingerprint spoofing attacks. Counterfeiters can imitate user fingerprints by using various human body simulation materials, thereby realizing illegal authentication and infringing user rights and interests, so liveness detection is quite necessary. According to the fingerprint image and biometric information obtained by the sensor, the fingerprint liveness detection (FLD) can distinguish whether the fingerprint is from a real person. This paper reviews the development history and the latest progress in the field of FLD. Both hardware and software based state-of-the-art methods are thoroughly presented to help researchers to carry out further research.
    Keywords: Fingerprint Liveness Detection; Biometrics; Understand; Software; Hardware.
    DOI: 10.1504/IJAACS.2024.10046755
  • Face Forgery Detection with Cross-Level Attention
    by Yaju Liu, Jianwei Fei, Peipeng Yu, Chengsheng Yuan, Haopeng Liang 
    Abstract: Currently, face videos manipulated using deep learning models are widely spread on social media, which violates personal privacy and may disturb social security. In this study, we start by discovering the essential differences between real and fake faces. To extract Multi-scale artifacts and increase the perceptual field of the downsampling layer, we introduce atrous spatial pyramid pooling (ASPP). Considering the drawback that ASPP does not use all pixels for computation and may lose information, we design a Cross-Level Attention(CLA) module to interact with the output of the ASPP block with the backbone. Our proposed attention mechanism allows the network to focus on locally manipulated areas without destroying other features of the model. Experimental results on the large publicly available facial manipulation database Faceforensics++ show that our method effectively improves detection accuracy and generalization, and confirms that great detection performance is achieved even for compressed images.
    Keywords: Face forgery detection ASPP Attention mechanism.

  • Analysis and optimization of RON loss via compound variable selection and BP neural network
    by Yunshu Dai, Jianwei Fei, Fei Gu, Chengsheng Yuan 
    Abstract: The loss of octane in gasoline refining process can cause huge economic losses. Reducing the loss of octane has high practical significance. However, octane loss involves many operations in gasoline refining process, which are coupled with each other and have a highly nonlinear relationship with octane loss. Therefore, the analyze and optimization of octane loss is a high-dimensional nonlinear programming problem. Therefore, this paper proposes a compound variable selection scheme. Based on the selection of independent variables by outlier filtering and high correlation filtering, the representative operations are selected by random forest and grey correlation analysis, and the octane loss is predicted by BP neural network and XGBoost algorithm. To optimize the octane loss, an operation optimization scheme based on fast gradient modification is proposed. Based on the octane loss prediction network, the main operations are gradually fine-tuned to reduce the octane loss.
    Keywords: RON loss optimization; variable selection; XGBoost; BP neural network.

  • A Survey on Neural Network-based Image Data Hiding for Secure Communication   Order a copy of this article
    by Yue Wu, Peipeng Yu, Chengsheng Yuan 
    Abstract: Data hiding has always been a hot research topic in the field of information security, and has attracted more and more attention from the academic community. At the same time, the rise of deep learning has also injected new development directions into the field. According to the characteristics of data hiding for images, many scholars have made corresponding improvements to the neural network and achieved many creative results. This review summarizes the main methods and representative research results of data hiding for images based on neural network. The principles and methods of neural network-based steganography and watermarking are introduced in detail, Finally we discuss problems of existing research and point out the direction for further research.
    Keywords: Data hiding; Steganography; Image watermarking; deep neural network.
    DOI: 10.1504/IJAACS.2024.10048413
  • Research and design of in-loop virtual simulation system of tread winding control software based on MCD
    by Mingxia Chen, Jijing He, Haitao Zheng, Hanyu Shi 
    Abstract: To improve the efficiency of industrial equipment design and debugging, virtual debugging technology is used to save the cost of industrial equipment debugging and reduce the risk of physical debugging. In this paper, an MCD-based tread winding virtual simulation system is presented, and the Software-In-Loop Virtual Debug of this system is used to study the application effect of fuzzy PID control algorithm in winding control devices. The simulation results show that compared with the traditional PID control and open-loop control, under the closed-loop control formed by the PID control algorithm with fuzzy control, the speed of the roller head is closer to the expected speed, the operation is more stable, the operation trajectory is more smooth, and a good control effect is achieved. The feasibility and effectiveness of the MCD-based tread winding virtual simulation debugging scheme are verified, and an idea is provided for the design of industrial equipment.
    Keywords: in-loop virtual simulation system; MCD; tread winding control software.

  • Augmented Data Control in Cipher Security using Functional Procedures
    by Raghvan M, Krishnmoorthy Prabu 
    Abstract: Data security, integrity and confidentiality are the main challenges of today’s digital world Even the highly secured data can be easily broken down by a simple hacking algorithm Though, data protection raises in terms of exponential growth, on the other side there is also a tremendous growth to break down the protection The scheme for data protection attracts more researchers and still the research is going on In the first part of this paper, we present a brief study of various techniques which supports data security, and the applications corresponding to the methods of cryptography.
    Keywords: Cryptographic techniques; Genetic Algorithm; Data security; Cloud database.

  • The Security Storage Method Of Dynamic Data In Internet Of Things Based On Blockchain   Order a copy of this article
    by Li Sun  
    Abstract: This paper proposes a method based on dynamic storage chain to overcome the problem of large amount of data in the Internet of things. In this method, ECC and D-H are used as encryption tools of the whole architecture to realize encrypted communication between lot devices. Combined with the blockchain technology, the IOT node access and the IOT dynamic data are stored to promote the dynamic data between IOT nodes to be stored in the offline storage structure, so as to achieve the purpose of secure storage of IOT dynamic data. The experimental results show that the average data storage time is 0.40s, the maximum root mean square error is 0.06, and the cost is controlled within 34500 yuan, which can effectively realize the security of IOT nodes.
    Keywords: Blockchain; Internet of things; Dynamic data; Security storage.
    DOI: 10.1504/IJAACS.2023.10049022
  • Unsupervised Clustering Algorithm For Database Based On Density Peak Optimization   Order a copy of this article
    by Xiaochuan Pu, Wonchul Seo, Ning QI 
    Abstract: In order to improve the clustering effect of traditional unsupervised clustering algorithm for database, an unsupervised clustering algorithm based on density peak optimization is designed and proposed. K-nearest neighbor is used to set a new method to measure the sample density and sample distance, and a decision diagram of sample distance relative to sample density is drawn. The selected sample is the initial cluster center, and the number of clusters is automatically determined. In order to further improve the clustering results, the improved K-means algorithm and particle swarm optimization algorithm are introduced to optimize the convergence process of the algorithm. In order to verify the effectiveness of the proposed algorithm, a simulation experiment is designed. Experimental results show that the proposed algorithm is effective and feasible.
    Keywords: Density peak optimization; Database; Unsupervised clustering; Initial cluster center; K-means algorithm; Particle swarm optimization algorithm.
    DOI: 10.1504/IJAACS.2023.10049212
  • Exposing deepfakes in online communication:detection based on ensemble strategy   Order a copy of this article
    by Jie Xu, Guoqiang Wang, Tianxiong Zhou 
    Abstract: In recent years, deepfake techniques appeared in people's lives. As a product of deep learning, it can generate realistic face-swapping videos. Due to high fidelity, deepfake is often used to produce porn videos and guide public opinion, so as to pose a great threat to social stability. Previous studies have been able to get better detection accuracy. This paper aims to improve the detection ability of existing schemes by using the ensemble learning scheme from the perspective of model learning. Specifically, our scheme includes feature extraction, feature selection, feature classification and combination strategy. The experimental results on several datasets demonstrate that our scheme can effectively improve the detection ability of the model.
    Keywords: deepfake detection; ensemble strategy; online communication; video forensics; deep learning.
    DOI: 10.1504/IJAACS.2022.10049685
  • Nonintrusive Power Load Feature Recognition Based On Internet Of Things Technology   Order a copy of this article
    by Jing Liu, Di Zhao 
    Abstract: In order to overcome the problems of low recognition efficiency, low information credibility and low recognition accuracy existing in the existing nonintrusive power load feature recognition methods, a nonintrusive power load feature recognition method based on the Internet of things technology is proposed. With the support of Internet of things technology, the feature parameters of power consumption information are obtained by Fourier transform method, and the feature parameters are fused according to the correct time sequence to realize the recognition of power consumption equipment. Based on the detection results, a nonintrusive power load feature recognition model is constructed by C4.5 decision tree algorithm, and the nonintrusive power load feature recognition model is realized by using the nonintrusive power load feature recognition model. The experimental results show that the proposed method has high recognition efficiency, high information reliability and high recognition accuracy.
    Keywords: Internet of things technology; Fourier transform; Parameter feature; C4.5 decision tree algorithm; Load feature recognition.
    DOI: 10.1504/IJAACS.2023.10051271
  • Optimal Load Balancing Strategy-based Centralized Sensor for WSN-based Cloud-IoT Framework using Hybrid Meta-heuristic Strategy   Order a copy of this article
    by Yogaraja G. S. R, Thippeswamy M. N, Venkatesh K 
    Abstract: This paper is to implement a load balancing centralized server to control the Wireless Sensor Networks (WSN) connected with IoT and cloud. The WSN gathers data pertaining to diverse applications and transfer it to centralized server in the cloud through IoT channel. Sever controls the routing of each node in the WSN through optimal load balancing strategy. A hybrid meta-heuristic algorithm with Forest-Cat Optimization Algorithm (F-COA) is introduced for accomplishing centralized load balanced strategy in communication system. The fundamental constraints used in the proposed models are clustering parameters like distance between nodes, energy, and delay, load balancing parameters like response time, turnaround time, server load, and QoS parameters like resource utilization, execution time, and throughput. The experimental results present the superior performance through multi objectives optimization when compared to the other approaches in terms of different constraints.
    Keywords: Internet of Things; Cloud Computing; Wireless Sensor Networks; Optimal Routing; Forest-Cat Optimization Algorithm; Load Balancing; Multi-Objective Function.
    DOI: 10.1504/IJAACS.2024.10051757
  • Automated Anomaly Detection and Multi-label Anomaly Classification in Crowd Scenes based on Optimal Thresholding and Deep Learning Strategy   Order a copy of this article
    by Harshadkumar S. Modi, Dhaval A. Parikh 
    Abstract: This paper plan to develop the anomaly detection and multi-label anomaly classification in crowd scenes using the enhanced deep learning strategy. The two main phases of the proposed model are the anomaly detection and the multi-label anomaly classification. In the first phase of anomaly detection, pre-processing of frames is done by the Histogram Equalization, and patches are extracted from the video frames. The extracted patches are further subjected to the Convolutional Neural Network for obtaining the movement score and appearance score of the frame. The extraction of movement score and appearance score helps to know the deep insight of the object behavior in the video, which thus helps to detect whether the objects are anomaly or not. For detecting that, a threshold is fixed for the movement score and appearance score.
    Keywords: Automated Anomaly Detection; Multi-label Anomaly Classification; Optimal Thresholding; Convolutional Neural Network; Enhanced Recurrent Neural Network; Elephant Herding-Grey Wolf Optimization.
    DOI: 10.1504/IJAACS.2024.10051758
  • Research On A New Multipath Transmission Optimization Algorithm For Multichannel Wireless Sensor Based On Optimized Clustering And Multi-Hop   Order a copy of this article
    by Ting Hu, Shaohui Zhong 
    Abstract: In view of the poor data transmission accuracy in traditional wireless sensor transmission methods, a multipath transmission optimization algorithm for multichannel wireless sensor based on optimized clustering and multi-hop is proposed. The internal structure and node structure of multichannel wireless sensor network are analyzed firstly, a multichannel routing algorithm for wireless sensor according to the internal structure characteristics of multichannel wireless sensor is built, and the cluster head and number of multichannel wireless sensors are determined and optimized with optimized cluster and multi-hop; then, ant colony algorithm is introduced into multipath search of multichannel wireless sensor; finally, by using path coding and decoding, the corresponding fitness function is constructed to optimize the multipath transmission of multichannel wireless sensor. The simulation results show that the proposed method can reduce the data transmission delay, the accuracy of multipath transmission is up to 98 %, and the network energy consumption is low.
    Keywords: Optimized clustering and multi-hop; Multichannel wireless sensor; Multipath transmission; Path coding.
    DOI: 10.1504/IJAACS.2023.10051765
  • Deepfake detection and localization based on illumination inconsistency   Order a copy of this article
    by Fei Gu, Yunshu Dai, Jianwei Fei, Xianyi Chen 
    Abstract: The rapid development of image synthesis technology has encouraged the spread of some fake news, making people gradually lose trust in digital media. The compression in the process of image propagation brings a major challenge to the existing face forgery detection method. In this paper, we propose a multi-task Deepfake detection method according to the motivation of illumination inconsistency between tampered and non-tampered areas. Specifically, we trained a Siamese network as a feature extractor to estimate the illumination, then distinguish the face image and predict the forged region through a U-shaped network. Our method has achieved great accuracy in classification tasks and can still maintain a good performance in compressing data. In addition, we can also show the intensity of tampering while locating the forged area.
    Keywords: Deepfakes; illumination estimation; Siamese network; UNet.
    DOI: 10.1504/IJAACS.2024.10052496
  • ASER analysis of DF relay assisted communication systems with diversity receiver at destination subject to Nakagami-m fading channels   Order a copy of this article
    by RAJKISHUR MUDOI, Darilangi S. Lyngdoh 
    Abstract: In recent years, relay assisted communications have been extensively used for low power and long-distance transmission of information. The average symbol error rate (ASER) performance of a decode and forward (DF) relay assisted communication method is analysed using maximal ratio combining (MRC) as well as selection combining (SC) receiver at the destination node. All links of the wireless system are influenced by Nakagami-m fading distribution. The closed-form representation of ASER is derived using the MGF based approach for coherent as well as noncoherent modulation techniques. The results show an improvement in the ASER performance with the MRC receiver compared to the SC receiver at the destination node. The ASER performance improves with an enhancement of the fading parameter. The mathematical expressions are supported using computer simulations that give the correctness of the results.
    Keywords: ASER; Decode-and-Forward; Maximal Ratio Combining; MGF; Nakagami-m fading; and Selection Combining.
    DOI: 10.1504/IJAACS.2024.10052604
  • Depth Information Acquisition and Image Measurement Algorithm Using Microarray Camera   Order a copy of this article
    by Jiancheng Zou, Peizhou Yan, Zhengzheng Li 
    Abstract: Compared with the binocular stereo vision system, the multi-eye stereo vision system can obtain more information and calculate the depth information of the scene more accurately. Based on the 3
    Keywords: microarray camera; Markov random field; graph cut; depth information; image measurement.
    DOI: 10.1504/IJAACS.2023.10054719
  • A Discrete Salp Swarm Algorithm for Vehicle Routing Problem with Time Windows   Order a copy of this article
    by Chen Huajun, Yanguang Cai 
    Abstract: The emphasis on this paper is about implementing an algorithm for vehicle routing problem with time windows (VRPTW), which is a key class of vehicle routing problem. Due to its NP-hard feature, it is quite difficult to solve it within acceptable calculation time. As a result, many metaheuristics have been developed to find suboptimal solutions to VRPTW. To settle the VRPTW, a hybrid discrete salp swarm algorithm (HDSSA) is put forward. To prove the property of this algorithm in solving solutions, the algorithm is applied to Solomon benchmark. Compared with existing methods, HDSSA provides relatively better results.
    Keywords: DSSA; VRPTW; Local search; Optimal solution; Random exchange.
    DOI: 10.1504/IJAACS.2024.10054799
  • Performance Modeling of Aloha Wireless Networks under the Influence of Varying Traffic Load Characteristics   Order a copy of this article
    by Ridhima Mehta  
    Abstract: Provisioning of simultaneous distributed channel access by multiple competing nodes in wireless communication system necessitates heterogeneous features to be satisfied for efficient network operation. These unique functions incorporate augmented utilization of scarce capacity and shared channel resources, and data delivery reliability across the network by mitigating packet collision frequency. In this work, we employ pure and slotted Aloha protocols for multiple-access resolution in wireless networks amidst the impact of diverse traffic characteristics induced by disparate inter-arrival times. Through rigorous simulation experiments, various network metrics are estimated and analyzed including collision multiplicity, collision rate, throughput, number of successfully received frames, and uplink channel utilization. The effects of miscellaneous traffic load profiles on the execution of random access-based channel coordination schemes is investigated via object-oriented discrete-event simulation modeling and genetic optimization. Furthermore, the efficient performance of our model is significantly compared with the previous works in terms of throughput and channel utilization.
    Keywords: Aloha networks; Channel utilization; Collision rate; Genetic algorithm; Throughput; Traffic load.
    DOI: 10.1504/IJAACS.2023.10054861
  • H? Model Reduction of 2D Discrete.time T.S Fuzzy Systems   Order a copy of this article
    by Abderrahim El-Amrani, Bensalem Boukili, Ahmed El Hajjaji, Ismail Boumhidi 
    Abstract: This paper considers the problem of H? model reduction design for two-dimensional (2D) discrete Takagi-Sugeno (T-S) fuzzy systems described by Roesser model, over nite frequency (FF) domain. The problem to be solved in the paper is to nd a reduced-order model such that the approximation error system is asymptotically stable, which is able to approximate the original T-S fuzzy system with comparatively small and minimized H? performance when frequency ranges of noises are known beforehand. Via the use of the generalized Kalman Yakubovich Popov (gKYP) lemma, new design conditions guaranteeing the FF H? model reduction are established in terms of Linear Matrix Inequalities (LMIs). To highlight the effectiveness of the proposed H? model reduction design, a numerical example is given to illustrate the effectiveness and the less conservativeness of the proposed approach.
    Keywords: Model reduction Multidimensional Systems Roesser Models Finite Frequency H? Performance.
    DOI: 10.1504/IJAACS.2023.10054899
  • Design Of Online Monitoring Method For English Education Resource Allocation In The Internet Of Things Learning Environment   Order a copy of this article
    by Chunzi Zhang  
    Abstract: Aiming at the problems of long response time and poor data de-noising effect in the traditional online monitoring method for resource allocation, this paper proposes an online monitoring method for English education resource allocation in the Internet of things learning environment. The denoising method based on wavelet time domain is used to denoise English education resources in the Internet of things learning environment. After denoising, English teaching resources are allocated by wavelet neural network. According to the time threshold, the time window used in the allocation of English teaching resources is determined. The simulation results show that the proposed method has the advantages of short response time, better data denoising effect, low monitoring error rate and reliability.
    Keywords: Internet of things learning environment; English education resources; Allocation; Online monitoring; Wavelet time domain; Time window.
    DOI: 10.1504/IJAACS.2023.10054900
  • A Low-rank LBP using Local Differential Polarization for Fingerprint Liveness Detection   Order a copy of this article
    by Chengsheng Yuan, Mingyu Chen, Yue Wu 
    Abstract: Local Binary Pattern (LBP), as a descriptor of local texture features, is used to extract local texture features of a fingerprint image. However, features extracted by LBP descriptor have a lot of noise, resulting in identification accuracy is lower. This paper proposes a low-rank LBP (LLBP) algorithm based on Robust Principal Component Analysis (RPCA), which overcomes the limitation of disturbance of some texture features. In addition, there are many blank areas that do not contain information around the fingerprint image, and these useless areas will also have a certain impact on the extraction of fin-gerprint features. Therefore, we propose a Local Differential Polarization (LDP) Algorithm to eliminate the influence of blank areas. Finally, we conducted an experiment with data sets of LivDet 2011 and LivDet 2013 to test our model. The results prove that classification performance of this method is superior to other algorithms.
    Keywords: Fingerprint Liveness Detection; Low rank; LBP; RPCA.
    DOI: 10.1504/IJAACS.2023.10054978
  • Memetic Grey Wolf Optimizer Algorithm for Solving the Cumulative CVRP   Order a copy of this article
    by Cai Yanguang, Gewen Huang, Yuanhang Qi, Helie Huang, Yunjian Xu 
    Abstract: The cumulative capacitated vehicle routing problem (CCVRP) is a variation in vehicle routing problem that aims to minimize the waiting time of all clients. This paper proposes a memetic grey wolf optimizer algorithm (MGWOA) to solve this problem. A bidirectional conversion strategy based on grouping and combining is proposed to realize the conversion between grey wolf positions and vehicle routing groups. The neighbourhood search optimization strategy with roulette wheel selection and the continual optimization strategy for optimal solution based on routing reconstruction are proposed to optimize the routing group. The experimental results show that: the MGWOA proposed can effectively solve the CCVRP problems; the solving accuracy and stability of the proposed algorithm was verified by comparison with five other meta-heuristic algorithms; the bidirectional conversion strategy, the neighbourhood search optimization strategy, and the continual optimization strategy proposed improve the convergence speed and the convergence accuracy of the MGWOA.
    Keywords: vehicle routing; memetic; grey wolf optimizer; bidirectional conversion; neighbourhood search.
    DOI: 10.1504/IJAACS.2024.10054979
  • An optimal matching algorithm of e-commerce recommendation information based on matrix decomposition   Order a copy of this article
    by Liming Wang, Wenxue Liu, Huichuan Liu 
    Abstract: Aiming at the problems of data sparsity and cold start in traditional e-commerce recommendation information matching, an optimal matching algorithm of e-commerce recommendation information based on matrix decomposition is proposed. On the basis of e-commerce behavior, the optimal user behavior is obtained by decomposing the user's behavior and the user’s preference, and then the optimal user behavior is obtained by decomposing the user’s behavior and the user’s preference. The experimental results show that: compared with the traditional optimal matching algorithm of recommendation information, the proposed optimal matching algorithm of e-commerce recommendation information based on matrix decomposition has higher AUC value and lower RMSE value, and the performance of recommendation information matching is better.
    Keywords: Matrix decomposition; e-commerce; recommendation information; matching algorithm; Hamming distance.
    DOI: 10.1504/IJAACS.2024.10054980
  • Detection of False Data Attack in Sensor Networks Based on APIT Location Algorithm   Order a copy of this article
    by YuGuang Ye 
    Abstract: In order to overcome the big error problem of false data location in traditional false data attack detection methods for sensor network, this paper proposes a new false data attack detection method for sensor network based on APIT location algorithm. The range of false data attack in sensor network is detected, and the target node is selected. Its vertex signal strength is used to compare with the signal strength of neighbor node. The APIT location algorithm is used to determine whether the attacked node is in the triangle, and all the overlapping area centroids of available small areas are the positions of false data attack nodes, to complete the detection of false data attack. The experimental results show that the accuracy of detecting false data attacks in sensor networks is over 99%, and the network security and communication performance are improved.
    Keywords: APIT location algorithm; Sensor network; False data; Attack detection; Overlapping area.
    DOI: 10.1504/IJAACS.2024.10054981
  • Energy Harvesting based Performance analysis in Nakagami-m fading channels   Order a copy of this article
    by Nandita Deka, Rupaban Subadar 
    Abstract: : Energy harvesting (EH) is an emerging technology to harvest energy from the transmitter's radio frequency (RF) signals to the receiver. In this paper, a novel closed-form expression for the outage probability (OP) and average bit error rate (ABER) based on energy harvesting are derived over Nakagami-m fading channel. Moreover, we assume the power splitting (PS) harvesting technique in our proposed system. The power splitting receiver separates the received signal into information transmission and energy harvesting receiver with a power splitter factor. Numerical results are also presented to analyze the impact of various system parameters, such as the power splitter factor and shaping parameter of the considered fading channel.
    Keywords: Nakagami-m fading; RF signals; Energy harvesting; PS factor; Outage probability; ABER.
    DOI: 10.1504/IJAACS.2024.10055334
  • Performance estimation of rotation antenna with directional selectivity in IEEE 802.11 wireless networks   Order a copy of this article
    by Ridhima Mehta  
    Abstract: Spatially separated antenna devices in wireless communication system determine the effectiveness of the radio network performance. Non-uniform directional antenna mounted for wireless node localization radiates energy along one particular direction more than others. In this paper, the performance evaluation of a software controlled wireless antenna system is presented. The specific type of wireless network application with frequency reconfigurable rotation antenna incorporating the directional selectivity characteristic is employed in the context of IEEE 802.11 wireless networks. The wireless antenna system is tested in terms of various queuing related metrics of average buffer length, mean queuing time and packet arrival rate. In addition, the quality-of-service (QoS) performance attributes of wireless communication network are estimated including the packet interference rate, average round trip delay and application throughput. Furthermore, the efficient performance of our model is significantly compared with the previous works in terms of throughput and delay parameters.
    Keywords: Delay; IEEE 802.11 Wireless Network; Rotation Antenna; Throughput.
    DOI: 10.1504/IJAACS.2024.10055459
  • Optimal SVM Classifier based Cross-layer Design in Ad-hoc Wireless Network   Order a copy of this article
    by Ridhima Mehta  
    Abstract: The rapid advancement of wireless technology and routing devices has led to expeditious evolution of the ad-hoc type of networking. The infrastructure-less dynamic network with the error-prone wireless medium in the resource-constrained ad-hoc communication system poses several challenges for efficient routing and design optimization. In this paper, an optimal cross-layer design architecture for ad-hoc wireless network is developed based on the supervised categorization algorithm. Specifically, the Support Vector Machine (SVM) classification scheme is employed to evaluate the margin and error associated with the disparate features of a wireless communication system. This technique ensures that the margin obtained with the computed linear separating plane is maximum from the labeled training samples belonging to two different categories of a two-class problem. The contemplated networking attributes considered for the integrated application of cross-layer information exchange and binary SVM models include the throughput, persistence probability, and transmit power associated with the directed wireless links.
    Keywords: Ad-hoc network; Cross-layer design; Persistence probability; Power; SVM; Throughput.
    DOI: 10.1504/IJAACS.2024.10055462
  • Applications, Merits and Demerits of WSN with IoT- A Detailed Review   Order a copy of this article
    by Mantripragada Yaswanth Bhanu Murthy, Anne Koteswararao 
    Abstract: This article provides an in-depth survey of WSN and IoT. It explores the diverse applications of IoT and WSN in healthcare, agriculture, transportation, automation, etc. The paper provides the various merits and demerits of IoT and WSN technologies. It also investigates the research works exploiting both IoT and WSN technologies for distinct applications and describes the various advantages of integrating these technologies. The paper provides a comparative study of explored applications based on common performance metrics, publication year, technologies used and results achieved. Through exploring the diverse applications, strengths and weaknesses of IoT and WSN systems, this paper offers the thorough knowledge on IoT and WSN technologies to readers for encouraging better and more applications exploiting WSN with IoT.
    Keywords: IoT; WSNs; Applications; Smart devices.
    DOI: 10.1504/IJAACS.2024.10055464
  • SNGPLDP: Social Network Graph Generation Based on Personalized Local Differential Privacy   Order a copy of this article
    by Zixuan Shen, Jianwei Fei, Zhihua Xia 
    Abstract: The social network graph (SNG) can display valuable information mined from the massive data Its generation needs vast amounts of users’ data However, with the increasing awareness of personal privacy protection, conflicts arise between generating the SNG and protecting the sensitive data therein To balance the problem, some SNG generation schemes are proposed by using local differential privacy (LDP) techniques In this way, the users can upload the perturbed data to the server with the privacy protected, and the server can generate an approximate SNG using the perturbed data However, the existing schemes do not consider the personalized privacy requirements of users This paper proposes an SNG generation scheme by designing a personalized LDP (PLDP) method, named SNGPLDP. Experiments performed on four real datasets show the effectiveness of SNGPLDP in providing PLDP protection with general graph properties. Moreover, the proposed scheme achieves higher network structure cohesion.
    Keywords: Personalized Local Differential Privacy; Social Network Graph; Randomized Response.
    DOI: 10.1504/IJAACS.2024.10055601
  • DSHS: A Secure Decentralized Smart Healthcare System using Smart Contract
    by A.N.U. RAJ, Shiva Prakash 
    Abstract: Social distancing has been implemented to stop the COVID-19 outbreak, which is currently a major public health concern on a global scale. Telemedicine is used by medical professionals to monitor their patients, especially those with chronic diseases. However, various implementation-related risks including data breaches, access restrictions within the medical community, inaccurate diagnosis, etc are faced by traditional telemedicine. We proposed the enhanced decentralized smart healthcare system (DSHS) to reduce the risks associated with traditional telemedicine healthcare solutions that utilize blockchain-based smart contracts to monitor, supervise, and carry out transactions. An immutable Modified Merkel tree structure is used to hold the transaction for viewing and accessing revocation contracts on a public blockchain, updating and sharing patient health records with all entities. Performance evaluation is done on Ethereum Platform. The simulation results show that proposed framework outperforms existing telemedicine solutions by enhancing transparency, boosting efficiency, and reducing average latency in the system.
    Keywords: Telemedicine; Ethereum; Blockchain; Smart Contract; Patient Health Record; Modified Merkle tree.

  • Antenna Performance Enhancement Using Inter-Coupling Effect Reducing Mechanisms   Order a copy of this article
    by Gebrehiwet Gebrekrstos Lema  
    Abstract: Recently, thinning an antenna has resulted in to attractive antenna radiation characteristics enhancement. This performance enhancement using thin antenna array is achieved because the inter-coupling effects of the array elements are reduced. Though the thinning both reduces the weight of the antenna and enhances the radiation characteristics, iterative algorithms can further enhance the performance and hence, in this research, an optimizer algorithm and inter-coupling reducing mechanisms are applied. The excitation weights of the individual array elements are thinned by turning some of the elements turned off while some of the elements turned on. The purpose of the thinning is to enhance the antenna performances like reduced SLL, high directivity, reduced power consumption and flexible radiation pattern. The SLL attenuation mechanism is applied to reduce the SLL in addition to the SLL reduction using the thinning and beamforming. Hence, in this paper, the three techniques (thinning, beamforming and SLL attenuation) are proposed to be integrated to enhance the antenna radiation characteristics. In general, the proposed combined method has resulted in to much better SLL reduction, directivity improvement and power wastage reduction.
    Keywords: Antenna design; beam forming; antenna array; side lobe; directivity.
    DOI: 10.1504/IJAACS.2024.10055765
  • Secure Framework for Data Transmission and Amalgamation of the Medical Device in IoMT
    by Rajkumar Gaur, Shiva Prakash 
    Abstract: The various application of IoT, one of the IoMT, is used in medical health care and medical monitoring techniques such as healthcare. It examines medical reports (EMR), online cases, primary level control, patient supervision, and fundamental problems. Urgent care can most affect support due to the current lack of a hospital or procedure and the possible long clinical medical centre. Due to the instant transfer of patient information, security is a crucial problem. Then the various attack moves and constructs are challenging in a device and secure information. So, our proposed architecture and security scheme is essential for the Internet of Medical Things. The architecture and scheme minimize resources, cost, and service the security system secures the information of patients and hospitals. Also, analyse the information integrity, confidentiality, and non-repudiation in the data transmission for IoMT applications. The next discusses the future challenges and implementation of the innovative healthcare system.
    Keywords: IoMT; domain; data flow; secure; hospital services; e-health.

  • Classification of Insect's Acoustic Signals Using a Hybrid Approach: Mel-Frequency Hilbert-Huang Transformation
    by Rekha Kaushik, Jyoti Singhai 
    Abstract: Insects present in stored grain, wood, soil, plants, and environment have distinctive set of acoustic features. This paper developed an insect detection and classification system using their sound dataset. A novel approach has been proposed based on the combination of features: Mel-frequency Cepstral Coefficient and Hilbert Huang transform named Mel-frequency Hilbert Huang Transform (MFHTT) for acoustic feature extraction. The proposed method integrates the ability of Principal Component Analysis to reduce the dimensions and de-correlate the coefficients for insect sound classification. Support Vector Machine, K-Nearest Neighbour, Random Forest, Na
    Keywords: Acoustic sensing; Classification algorithms; Feature extraction; Hilbert- Huang transformation; Insect; Mel-frequency Cepstral Coefficient.

  • Privacy-Preserving Image Retrieval Based on Additive Secret Sharing   Order a copy of this article
    by Zhihua Xia, Qi Gu, Lizhi Xiong, Wenhao Zhou 
    Abstract: The rapid growth of digital images motivates individuals to upload their images to the cloud server. To preserve privacy, image owners would prefer to encrypt the images before uploading, but it would limit the efficient usage of images. Plenty of schemes on privacy-preserving content-based image retrieval (PPCBIR) tries to seek the balance between security and retrieval ability. However, compared to the works in content-based image retrieval (CBIR), the existing schemes are far deficient in both accuracy and efficiency. In this paper, inspired by additive secret sharing technology, we propose a series of secure computation protocols and show their application in PPCBIR. The experiments and security analysis demonstrate the efficiency, accuracy, and security of our scheme.
    Keywords: Privacy-preserving Image Retrieval; Additive Secret Sharing; Pre-trained CNN; Secure PCA.
    DOI: 10.1504/IJAACS.2024.10055815
  • An Abnormal Behavior Recognition of MOOC Online Learning Based on Multidimensional Data Mining   Order a copy of this article
    by Meng Qu 
    Abstract: To solve the problems of low recall rate, low recognition rate and long time-consuming of the traditional MOOC online learning abnormal behavior identification method, an abnormal behavior recognition method of MOOC online learning based on multidimensional data mining is designed. The CFSFDP algorithm is used to mine MOOC online learning multidimensional data, the Lagrangian function is used to improve the SVM, and the improved SVM is used to classify the collected data. A neural network structure based on multi-head self-attention mechanism is constructed, and the feature vector of each class of MOOC online learning data is extracted by this network, and the abnormal behavior of MOOC online learning is identified according to the feature vector. The experimental results show that the recall rate of the method in this paper is always above 93%, the average recognition rate is 95.9%, and the maximum recognition time is only 0.4s
    Keywords: Multidimensional data mining; MOOC; online learning; abnormal behavior recognition; multi-head self-attention mechanism; neural network structure; recognizer.
    DOI: 10.1504/IJAACS.2024.10055816
  • Proxy-CPM: A collaborative high definition map update for autonomous and connected vehicles   Order a copy of this article
    by Anis BOUBAKRI, Sonia Mateli 
    Abstract: Autonomous cooperative driving systems allow autonomous vehicles to operate safely. However, these driving systems are limited by the difficulty of retrieving, in a timely manner, the data needed for traffic. HD maps act as an additional sensor for the purpose of informing autonomous vehicles in advance by changes in the traffic environment. So you have to update the HD map. In our solution we have proposed a service that allows to notify the vehicles by the updates of the traffic environment in order to minimize the dangerous situations.
    Keywords: HD map; Edge computing; Autonomous vehicles; Connected vehicles; Collaborative perception.
    DOI: 10.1504/IJAACS.2024.10055954
  • Bi-LSTM with Attention Pooling Based on Joint Motion and Difference Entropy for Action Recognition   Order a copy of this article
    by Lunzheng Tan, Chunping Huang, Xia Limin, Jiaxiao Li 
    Abstract: Human action recognition is one of the most challenging tasks in computer vision due to its complex background changes and redundancy of long-term video information. To tackle these issues, we propose a novel action recognition framework called Bi-LSTM with Attention Pooling based on Joint motion and difference Entropy (JEAP-BiLSTM). Firstly, we extracts critical points of motion flow field as the key points of optical flow field, then compute the motion and difference entropy maps of the key points’ optical flow as short-term features. On this basis, we then use Bi-LSTM to extract video long-term temporal features from forward and backward simultaneously. In order to solve the problem of background change, we introduce attention pooling to the extracted features to highlight the region of interest. Experiments demonstrate that the proposed JEAP-BiLSTM outperforms state-of-the-art action recognition methods.
    Keywords: Action recognition; Attention mechanism; Entropy map; Bi-LSTM.
    DOI: 10.1504/IJAACS.2024.10056109
  • Performance analysis of antenna selection based MIMO systems subject to Fisher-Snedecor F fading channels   Order a copy of this article
    by Hubha Saikia, Rajkishur Mudoi 
    Abstract: The antenna selection is a prominent technique which decreases the number of radio frequency (RF) links in a multiple-input-multiple-output (MIMO) scheme. This article analyses the outage probability (OP), capacity as well as bit error rate (BER) of MIMO system with antenna selection subject to independent and identically distributed (i.i.d.) Fisher-Snedecor F fading channels. Two types of systems namely transmit antenna selection (TAS) connected with maximal ratio combining (MRC) receiver as well as joint transmit and receive antenna selection system are evaluated. Analytical expressions are derived in terms of infinite series representation. The OP, BER and channel capacity are illustrated for different values of fading parameters as well as shadowing parameters. All the obtained statements are endorsed by Monte-Carlo simulation data.
    Keywords: Bit error rate; Fisher-Snedecor F fading; Transmit antenna selection; Outage probability; Maximal ratio combining.
    DOI: 10.1504/IJAACS.2024.10056127
  • A novel copy-move detection and location technique based on tamper detection and similarity feature fusion   Order a copy of this article
    by Guangyang He, Xiang Zhang, Fan Wang, Zhangjie Fu 
    Abstract: Copy-move is a tampering method that moves a part of the image to another area. Since the colour and brightness of the images before and after being tampered are roughly the same, it is laborious to be recognised by the human eye. To address the problem of weak feature extraction capability in current copy-move tampering detection models, this article proposes a new image copy-move detection method. This method effectively extracts noise and edge information from the tested image through multi-angle feature fusion technology, and further improves the detection performance on image tampering edges by combining dilated convolutions and attention mechanisms. In addition, the model embeds tampering detection features into similarity features, enabling similarity detection to focus on specific areas, which effectively improves the detection efficiency and accuracy of model. Compared with existing copy-move detection methods, this method has strong robustness to various attacks while achieving good detection accuracy.
    Keywords: Deep learning; Image tampering localisation; Edge features.
    DOI: 10.1504/IJAACS.2024.10056233
  • Research on a New Encryption and Decryption Algorithm for Privacy Data in Wireless Sensor Network Based on Dynamic Key   Order a copy of this article
    by Hongli Deng, Tao Yang 
    Abstract: In order to overcome the low security of privacy data in wireless sensor network, this paper proposes a new encryption and decryption algorithm for privacy data in wireless sensor network based on dynamic key. In this method, the dynamic key pool update mechanism is introduced to effectively avoid node leakage. At the same time, the authentication and update mechanisms are further introduced to increase the reliability of information sources. The integrity of data is artificially destroyed by introducing data disturbance. At the same time, the privacy data of wireless sensor network is encrypted according to the new key distribution algorithm in the process of data exchange. The experimental results show that the key leakage probability is always below 0.10%, and the execution efficiency is significantly higher, up to 99%. The cost of the four algorithms is the lowest, with an average of 194600 yuan. The effectiveness and superiority of the proposed method are verified.
    Keywords: Dynamic key; Wireless sensor network; Privacy data; Encryption and decryption algorithm.
    DOI: 10.1504/IJAACS.2023.10056271
  • A discrete bat algorithm for collaborative scheduling of discrete manufacturing logistics   Order a copy of this article
    by Chen Huajun, Yanguang Cai 
    Abstract: In this paper, a collaborative scheduling of discrete manufacturing logistics (CSDML) model is established for a single factory with multiple customers, considering multi-vehicle, delay, time window and capacity constraints. Based on the basic principle of bat algorithm, discrete bat algorithm(DBA) is proposed to solve the CSDML problem. A coding and decoding scheme is proposed to map the continuous domain to the discrete domain, the objective function is defined, and a local search strategy is adopted to enhance the search effect of the algorithm. Compared with DBA with random search and DBA without search, the proposed algorithm can get better experimental results.
    Keywords: discrete manufacturing; logistics transportation; discrete bat algorithm; collaborative scheduling.
    DOI: 10.1504/IJAACS.2024.10056274
  • An improved salp swarm algorithm for collaborative scheduling of discrete manufacturing logistics with time windows   Order a copy of this article
    by Chen Huajun, Yanguang Cai 
    Abstract: Considering the constraints of time windows and capacity in the case of single factory and multiple customers, a collaborative scheduling of discrete manufacturing logistics with time windows (CSDMLTW) model is established. The problem includes discrete manufacturing process and logistics transportation scheduling process. In the discrete manufacturing process, parallel machine scheduling(PMS) is considered. Logistics transportation scheduling considers vehicle routing problem with time windows (VRPTW). In this paper, improved salp swarm algorithm (ISSA) is proposed to solve CSDMLTW problem based on the basic principle of salp swarm algorithm. Compared with simulated annealing algorithm, genetic algorithm and particle swarm optimization algorithm, the results are relatively better. Experimental results verify the feasibility of solving this problem.
    Keywords: parallel machine scheduling; vehicle routing; discrete manufacturing; collaborative scheduling.
    DOI: 10.1504/IJAACS.2024.10056281
  • Energy Efficient Techniques in 5G Communication: A Survey   Order a copy of this article
    by Gracelin Sheena B, N. Snehalatha 
    Abstract: Fifth Generation (5G) technology is a huge demand in the communication scenario due to the advanced features. The 5G communication shifts the wireless signal to the frequency range of 30 to 300 gigahertz (GHz) and minimizes the wavelength from centimeter to millimeter. Hence, it generates large bandwidth and reduces the traffic congestion in network. In this survey, 50 research papers are reviewed based on the beamforming techniques used to enable the data rate in network. 5G mobile communication methods are classified based on the beamforming methods, like phased array, network slicing, millimeter wave, Filter Bank Multi Carrier (FBMC), and wideband approach. Moreover, the challenges faced by the existing techniques are explained in the gaps and issues section. The analysis based on the classification, toolset, and the performance metrics are discussed. The future dimension of the research is based on the gaps and issues identified from the existing research works.
    Keywords: Beamforming; mobile communication; millimeter wave (mm wave); Phased array antenna; dual band antenna.
    DOI: 10.1504/IJAACS.2024.10056426
  • Industrial Internet of Things for Smart Factory: Current State and Problems   Order a copy of this article
    by Jianli Guo 
    Abstract: The current research on smart factory emphasizes the emergence of new characteristics of manufacturing processes, such as small batches and personalization. With regards to personalization and customization, the studies commonly focus on three aspects: software-defined industrial wireless sensor network technology, edge computing, and production line coordination mechanism for an intelligent manufacturing plant. The paper aims to investigate and summarize the current state-of-the-art. In particular, it discusses the critical problems of efficient data transmission, collaborative edge-cloud, and production line mechanisms. The presented review of the industrial Internet of Things for smart factory serves as guidance for future studies.
    Keywords: Industrial Internet of Things;Smart Factory;Software-Defined Networks;Edge Computing.
    DOI: 10.1504/IJAACS.2023.10056427
    by Shwetha N, Manoj Priyatham M, Gangadhar N 
    Abstract: In digital communication, the transmitted signal may be dispersive causing the information not to be transmitted as same Due to distortion, the communication channel is affected by Inter-Symbol Interference (ISI) An adaptive channel equalization concept is used to reduce the effects of ISI in digital communication The equalization process is considered as an optimization issue to minimize the mean square error (MSE) between the transmitted signal and the output of the equalizer Therefore, metaheuristic algorithms are widely adopted to enhance the function of adaptive channel equalizers In this paper, five different hybrid metaheuristic algorithms are introduced to optimize the Finite Impulse Response (FIR) channel for reducing the effects of ISI Accordingly, a bio-inspired Emperor Penguin Optimization (EPO) algorithm is individually hybridized with different algorithms like Tunicate Swarm Algorithm (TSA), Bald Eagle Search (BES), Jellyfish Search Optimization (JS), Manta ray foraging (MRF) and Chimp optimization algorithm (ChOA) The main role of these algorithms is to optimise the weights or coefficients of the equaliser for reducing the effect of ISI. Finally, the performance of each algorithm in channel equalisation is assessed, it is observed that EPO incorporated with both manta ray foraging and tunicate swarm algorithm have obtained relatively better equalisation results than other hybrid optimisation algorithms.
    Keywords: Adaptive Channel Equalization; Hybrid Optimization; FIR Filters; Mean Square Error; Inter Symbol Interference and Metaheuristic Algorithm.
    DOI: 10.1504/IJAACS.2024.10056434