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 (31 papers in press)

Regular Issues

  • 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
     
  • DSHS: A Secure Decentralized Smart Healthcare System using Smart Contract   Order a copy of this article
    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.
    DOI: 10.1504/IJAACS.2025.10059646
     
  • 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   Order a copy of this article
    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.
    DOI: 10.1504/IJAACS.2025.10061163
     
  • 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
     
  • An improved salp swarm algorithm for collaborative scheduling of discrete manufacturing logistics with multiple depots   Order a copy of this article
    by Chen Huajun, Yanguang Cai 
    Abstract: Aiming at the situation of a single factory, multiple depots and multiple customers, considering storage, time windows and capacity constraints, a collaborative scheduling of discrete manufacturing logistics with multiple depots (CSDMLMD) model is established. This problem includes discrete manufacturing process, depot storage process and logistics transportation scheduling process. Based on the basic principle of salp swarm algorithm, an improved salp swarm algorithm (ISSA) is proposed to solve the CSDMLMD problem. It is compared with simulated annealing algorithm, genetic algorithm and particle swarm algorithm, and relatively good results can be obtained. The experimental results presented in this paper verify the feasibility of solving this problem.
    Keywords: open shop scheduling; vehicle routing; discrete manufacturing; collaborative scheduling.
    DOI: 10.1504/IJAACS.2025.10059647
     
  • Detection of Primary User Emulation Attack using the Share and hunt optimization based deep CNN classifier   Order a copy of this article
    by Asmita A. Desai Asmita A. Desai, Pramod B. Patil 
    Abstract: In this research, the share and hunt optimization-based deep classifier is developed for accurate PUEA detection, which improvise the efficiency of utilization. The detection of primary user emulation attacks and enhancing the primary user performance is done by the three-layered approach in the cognitive radio network. The proposed method investigates the malicious user actions in the CR network, which prevents interference involved in the primary user. The performance of the proposed three-layered approach using the share and hunt optimization based deep CNN classifier is evaluated using the parameters, such as detection rate, delay, and throughput, and the analysis is performed using the Rayleigh and the awgn channel in the CR environment. The detection rate and the throughput of the attack detection are highly accurate and the delay is rapid for the developed method. In imminent, the protection of the CR network is highly improved with other enhanced approaches.
    Keywords: Cognitive Radio Network; Deep CNN; PUEA detection; Optimization; Secured Spectrum sensing.
    DOI: 10.1504/IJAACS.2025.10061537
     
  • A Theoretical Framework for Key Factors Affecting Adoption of E-procurement   Order a copy of this article
    by Zhang Guozheng, Li Peng, Donghui Li, Fang Wang, Yongdong Shi 
    Abstract: We proposed a theoretical framework to identify key factors influencing e-procurement adoption. The framework combines the theory of planned behavior and the model of technological acceptance, commonly used in B2B analyses. Through a literature review, we developed the model, drawing insights from a leading B2B e-commerce company in China. Data from 518 completed questionnaires were analyzed using structural equation modeling. The study found that behavioral attitudes, subjective norms, and behavioral control are crucial for predicting e-procurement technology usage. Internal and information barriers are important for corporate clients integrating new procurement platforms. The study provides suggestions to solution providers to overcome barriers and promote electronic procurement. The paper concludes by discussing the study's limitations and future research directions.
    Keywords: Planned behavior theory; technology acceptance model; e-procurement adoption; internal barriers; information barrier.
    DOI: 10.1504/IJAACS.2025.10059648
     
  • Reversible Data Hiding in Encrypted Images Based on Histogram Shifting and Prediction Error Block Coding   Order a copy of this article
    by Zhilin Chen, Jiaohua Qin 
    Abstract: To reduce prediction errors and create more room for embedding data, the paper proposes a reversible data hiding in encrypted images scheme based on histogram shifting and prediction error block coding. Firstly, the histogram of the prediction error image is shifted according to the signs of prediction errors. Next, the prediction error plane is partitioned into uniformly sized blocks, and these blocks are labeled as three types: an all-zero block, a block containing only one 1, and a block containing more than one 1. These three types of blocks are compressed using labeling, binary tree coding, and Huffman coding, respectively. To better compress the label map, an improved extended run-length coding is proposed. Finally, the image is secured by encryption and the secret data is hidden within it. The experimental results indicate a significant improvement in the embedding rate of the scheme compared to other schemes.
    Keywords: RDH; reversible data hiding; prediction error; Huffman coding; encrypted images; extended run-length coding.
    DOI: 10.1504/IJAACS.2025.10061182
     
  • Survey on Sport Video Analysis and Event Detection   Order a copy of this article
    by Suhas Patel, Dipesh Kamdar 
    Abstract: In recent years, sports video analysis has gained prominence in areas such as sports coaching, player tracking, and event detection. This survey focuses on two main approaches: handcrafted features and deep learning methods. Handcrafted feature-based methods like SIFT, HOG, and SURF show promise in sports video analysis, but have limitations in handling complex actions and require manual parameter tuning. In contrast, deep learning methods, including CNNs and LSTMs, offer automated feature learning and high accuracy in action recognition and event detection. This survey offers insights into the latest techniques, their performance, and future research possibilities. By reviewing research on handcrafted features and deep learning in sports video analysis, it provides a comprehensive understanding of state-of-the-art techniques and research gaps. Sports video analysis can extract crucial information from large video datasets, including action recognition, event detection, and team behavior analysis. Advanced computer vision and machine learning automate analysis for valuable insights.
    Keywords: Sports Video Analysis; Event Detection; CNN; RNN; VGG-16,Hand Crafted Features; Deep Learning.
    DOI: 10.1504/IJAACS.2025.10059628
     
  • E-commerce Live Streaming Impact Analysis Based on Stimulus-Organism Response Theory   Order a copy of this article
    by Donghui Li, Li Peng, Zhang Guozheng, Fang Wang, Yongdong Shi 
    Abstract: The rise of live e-commerce has not only ushered in a new consumption paradigm but has also improved people's leisure activities. The research on impact factors in e-commerce live streaming has practical implications. Based on Stimulus-Organism-Response(S-O-R) framework, this paper introduces two latent variables, perceived trust, and flow experience, to model a mechanism on how product value and celebrity value influence consumers' purchase intention in e-commerce live streaming, and then validate the result with structural equation model. For verifying the hypothesis, this study applies a questionnaire survey and random sampling. Statistical methods are used to conduct the credibility and efficacy test of the Model, significance analysis of the model, and effects between variables in the model. The empirical result shows that product value and celebrity value both have a significant direct impact on consumers' purchase intention in e-commerce live streaming.
    Keywords: E-commerce live streaming; SOR theory; product value; celebrity value; perceived trust; flow experience theory.
    DOI: 10.1504/IJAACS.2024.10059668
     
  • ASER with QAM techniques for SISO communication system over Fisher-Snedecor F fading channels   Order a copy of this article
    by Rajkishur Mudoi 
    Abstract: The average symbol error rate (ASER) applying various quadrature amplitude modulation (QAM) techniques is analyzed for single input and single output (SISO) system. QAM schemes are more useful to increase bandwidth efficiency for 5G and beyond wireless transmission systems. The channel of the system is influenced by Fisher-Snedecor F composite distribution. This distribution is commonly used to model fading channels due to its high accuracy and mathematical conformity. Various QAM schemes like hexagonal QAM, cross-QAM, square QAM and rectangular QAM are employed for ASER derivations. ASER expressions are acquired with regard to the Fox H-function which is the most general function and Prony approximation for Gaussian Q-function is utilized. Computer simulation is achieved to verify the certainty of the analyzed ASER equations.
    Keywords: Fisher-Snedecor F fading; ASER; Quadrature amplitude modulation (QAM); Prony approximation.
    DOI: 10.1504/IJAACS.2025.10061540
     
  • Robust Watermarking Algorithm for Screen-Shooting Images Based on Pattern Complexity JND Model   Order a copy of this article
    by Jia Peng, Jiaohua Qin, Xuyu Xiang, Yun Tan, Dashan Qing 
    Abstract: The popularity of smart devices has made it more convenient for users to take screen shots, but it has also made it easier to take clandestine shots, resulting in compromised and untraceable information. Therefore, this paper introduces a screen-shooting robust watermarking algorithm based on the pattern complexity just noticeable difference (PC-JND) model. This approach involves the utilization of local binary patterns (LBP) for block filtering based on texture complexity in the original image. Stable feature blocks are selected and processed using the speeded-up robust features (SURF) algorithm to extract key feature points, defining them as the watermark embedding regions. Finally, the watermark is embedded in the integer wavelet domain's HH sub-band, guided by the PC-JND model. Experimental results demonstrate that this algorithm not only significantly improves the visual quality of images in a shorter embedding time but also exhibits enhanced robustness against screen captures from various angles.
    Keywords: screen-shooting; robust watermarking; LBP; SURF; JND.
    DOI: 10.1504/IJAACS.2025.10062271
     
  • Security Vulnerability Analysis and Formal Verification of Smart Contracts: A Review   Order a copy of this article
    by Monika Bishnoi, Rajesh Bhaitia 
    Abstract: Since the evolution of bitcoin, Blockchain technology has shown promising improvement and application prospects. However, blockchain gained momentum when Vitalik Buterin launched the Ethereum platform in July 2015. It includes smart contracts (SCs), a program to automate, enforce, and verify a set of rules for a transaction to be valid. Since some SCs handle millions of dollars, their security becomes critical. In the past, some hackers have exploited the vulnerabilities in Ethereum SCs and have caused significant losses to the community and users. However, to use blockchain to its full potential, we need SCs; otherwise, it is just a third-party free, decentralized system for transferring money. This paper focuses on the security aspect of SCs in terms of security vulnerabilities and tools developed to discover and locate these vulnerabilities. To prove the correctness of SCs this study also focuses on formal verification techniques used to model and verify SCs.
    Keywords: Blockchain; Smart Contracts; Security vulnerability; Security analysis; Formal methods; Formal verification.
    DOI: 10.1504/IJAACS.2025.10062281
     
  • Optimal Microstrip MIMO Antenna design: An optimisation based approach   Order a copy of this article
    by Shaktimayee Mishra, Asit Kumar Panda, Agarwal Arun 
    Abstract: One of the most exciting features of 5G is the MIMO antenna. MIMO technology can increase data transfer speeds while also providing multi-method resistance to fading. This device has demonstrated the ability to improve communication spectral efficiency across a broad range of applications. For that reason, we have developed an optimal design technique for microstrip MIMO antenna, in this work, which uses Modified Shark Smell Optimization to optimally select the antenna design parameters. Our proposed Modified Shark Smell Optimization is a reliable and less vulnerable optimization, which improves the gain and efficiency of the antenna by choosing the optimal design parameters. Further to enhance the accuracy of our optimal design technique, we have used Cauchy's mutation in our work. The measured and simulated results were compared with the conventional algorithms, which show that our proposed MSSO based design technique provide better results in terms of antenna gain and so on.
    Keywords: Microstrip MIMO antenna; Modified Shark Smell Optimization; Optimal parameter selection; Cauchy’s mutation; Mutual coupling; Return loss; Antenna Efficiency.
    DOI: 10.1504/IJAACS.2025.10063295
     
  • Cyber Security Automation and Managing Cyber Threats in Network through Smart Techniques: An Intelligent Approach for Future Gen. Systems   Order a copy of this article
    by Rohit Rastogi, Vaibhav Sharma, Tushar Gupta, Vaibhav Gupta 
    Abstract: Cybersecurity has become a major concern in this digital era. Since, the cyberattacks and their types are increasing at an immense rate, it is not humanly possible to monitor, identify and take actions against the attacks. With the current automation systems majorly relying on supervised learning algorithms where they have already seen the type of attacks to monitor and manage the attacks, these systems have been rendered inefficient by zero day attacks. The immense potential of AI and utilise it to its full potential in the field of cybersecurity. If correctly applied, Artificial Intelligence can help to detect and deal with the cyberattacks more efficiently and can help protect users that are not very security conscious and are not aware about the dangers of these security breaches. The authors have decided to utilise machine learning algorithms like decision trees and knowledge discovery in database (KDD) to detect zero day attacks as well as handle other common cyberattacks.
    Keywords: Supervised Learning; Unsupervised Learning; KDD (Knowledge Discovery in Database); phishing; smashing; DDoS.
    DOI: 10.1504/IJAACS.2025.10063962
     
  • DDoS attack detection and prevention model using Pipit Flying Fox optimization-based Deep Neural network   Order a copy of this article
    by Anuja Sharma, Parul Saxena 
    Abstract: The software-defined network (SDN) remains the futuristic model that helps to satisfy the new application demands of future networks. However, the control panel of SDN is the prime target of destructive attacks, especially distributed denial of service (DDoS). The restrictions in the conventional techniques such as reliability to network topology, low accuracy, and hardware dependencies manifest the need for effective DDoS detection. Hence, the research develops a DDoS attack recognition and prevention model aid with an optimised deep learning network. The significance relies on the pipit flying fox (PPF) optimisation, which selects the optimal hyperparameters, minimises the errors, and accelerates the learning speed. The experimental results are reported as the specificity, sensitivity, and accuracy of 98.5551%, 92.4951%, and 98.4951% respectively for 80% of training. Further, the values are obtained as 98.6397%, 86.0997%, and 98.09972% for specificity, sensitivity, and accuracy respectively at K-fold 10 which exceeds other competent techniques.
    Keywords: SDN; DDoS attack; security; attack detection; Deep learning; optimization.
    DOI: 10.1504/IJAACS.2025.10064035
     
  • Joint 5G NR Polar Code-Convolutional Code design for Massive MIMO-UFMC system   Order a copy of this article
    by Smita Jolania, Ravi Sindal, Ankit Saxena 
    Abstract: Polar codes (PC) are the major contender in fifth generation-New Radio (5G-NR) for error control in the physical downlink control channel (PDCCH) The work proposes a novel concatenated error correction technique of PC with convolutional codes (CC) and is experimented under 5G simulation constraints. This research paper develops a simulation model of Universal Filtered Multicarrier (UFMC) modulation based massive multiple-input multiple output (MIMO) technique targeting for short burst transmissions. The UFMC uses sub-band filtering with reduced out of band emission (OOBE) and enhanced spectral efficiency. An analytical framework of the novel PC-CC-UFMC system to effectively correlate the flexible design parameters for different wireless channels is implemented to enhance Bit Error Rate (BER) performance. The results shown in paper, a gain in the required Signal to Noise Ratio (SNR) for same BER is reduced by approximately 5dB for increase in antenna from 64 to 256.
    Keywords: Polar codes; New Radio; convolutional codes; Massive MIMO; UFMC.
    DOI: 10.1504/IJAACS.2025.10064049
     
  • Deepfake Detection Based on Single-Domain Data Augmentation   Order a copy of this article
    by Qian Feng, Zhifeng Xu 
    Abstract: Deepfake has posed a serious threat to personal privacy and social stability The related research on deepfake detection has gained sufficient high accuracy on various datasets, while the generalisation performance is still insufficient Most of the existing methods are aimed at analysing and detecting specific traces and distortions generated by a specific forgery algorithm. However, these detection algorithms typically experience a significant decline in accuracy when detecting forgery videos generated by other algorithms This paper proposed a Deepfake detection scheme based on Single-Domain Data Augmentation, and considered the most difficult situation in the deepfake detection generalization problem: How to generalise to a variety of unknown forgery data when only the real data is known We proposed the Universal Forgery Generation (UFG) and Adversarial Style transfer algorithm (AST) to augment forgery data and improve generalisation ability The experimental results show that our scheme is superior to many existing schemes.
    Keywords: Deepfake detection; Domain generalisation; Style transfer.
    DOI: 10.1504/IJAACS.2025.10064478
     
  • ADMET Property Prediction Model Based on Feature Selection and Data Mining Techniques   Order a copy of this article
    by Gu Junlin, Xu Yihan, Sun Juan, Liu Weiwei 
    Abstract: Breast cancer has posed a significant threat to women's health in recent years, and the search for compounds that can antagonize ER? activity will play an important role in breast cancer treatment. ADMET properties are important indicators of compound efficacy, and existing research has used machine learning techniques to fit collected data, but with some performance limitations. In this paper, we use data mining techniques to establish a biologically active-ADMET property prediction model. Firstly, important features were obtained through feature selection techniques, and 23 feature variables that have an impact on ADMET properties were selected. Then, LightGBM and genetic algorithms were used for biological activity prediction tasks, and the R2 value on the validation set reached 0.75, achieving good performance. Finally, based on the BP neural network, the ADMET-UMLP model was constructed, proposing a U-shaped structure to fully utilize the underlying feature information. The model performed well on the validation set, with AUC values exceeding 0.9 in the classification prediction of Caco-2, CYP3A4, hERG, HOB, andMNproperties, and a prediction of 0.98 AUC value for Caco-2, demonstrating good predictive performance.
    Keywords: ADMET; LightGBM; machine learning; prediction.
    DOI: 10.1504/IJAACS.2025.10064499
     
  • Dual-scale Dual-rate Video Compressive Sensing for Edge Surveillance Device   Order a copy of this article
    by Yue Lu, Zhang Xiang, Chengsheng Yuan 
    Abstract: Classic video compression method suffers from long encode time and requires large memories, making it hard to deploy on edge devices, thus video compressive sensing which requires less resources during encoding is getting more attention. We propose a dual-scale dual-rate video compressive sensing algorithm for surveillance video compression. Proposed method extracts and compresses foreground area and reference frame separately using dual-scale compressive sampling, then using reversible neural network to reconstruct original frames. Finally we test compressive sampling and ROI extraction network in proposed method on edge device and reconstruction network on server. The experiments show that proposed method can fast compresses frame and extracts foreground area on edge computing devices, achieves higher reconstruction quality.
    Keywords: Video compressive sensing; reversible neural network; surveillance video; siamese network; edge computing; neural processing unit; RK3399 Pro.
    DOI: 10.1504/IJAACS.2025.10064730
     
  • Analysis of secrecy performance under double shadowed -   Order a copy of this article
    by Damepaia Lato, Rajkishur Mudoi 
    Abstract: In this paper, the physical layer security (PLS) under the double shadowed - fading channel is investigated. Being a composite fading channel model, it is a realistic representation of the propagation environment in which wireless signals experience a complex interplay of different phenomena. The mathematical statements of the secrecy outage probability (SOP) and the probability of non-zero secrecy capacity (PNSC) have been investigated by considering one legitimate receiver and one eavesdropper listening to the source transmitting confidential information. Based on the obtained mathematical expressions, the secrecy performance metrics are analysed and the results are plotted for both the SOP and the PNSC. It can be observed that as the signal-to-noise ratio (SNR) of the legitimate user increases, the SOP reduces and the secrecy capacity increases for the double shadowed - distributions.
    Keywords: Composite fading channel; Double shadowed ?-? fading channel; Secrecy capacity; Physical layer security; Secrecy outage probability.
    DOI: 10.1504/IJAACS.2025.10064742
     
  • Adaptive channel equalisation using different hybrid metaheuristic algorithms in digital communication   Order a copy of this article
    by N. Shwetha, Manoj Priyatham, N. Gangadhar 
    Abstract: An adaptive channel equalisation concept is used to reduce the effects of inter-symbol interference (ISI) in digital communication. The equalisation process is considered an optimisation issue to minimise the mean square error (MSE) between the transmitted signal and the output of the equaliser. Therefore, metaheuristic algorithms are widely adopted to enhance the function of adaptive channel equalisers. In this paper, a bio-inspired emperor penguin optimisation (EPO) algorithm is hybridised with five different algorithms to optimise the finite impulse response (FIR) channel for reducing the effects of ISI. The main role of these algorithms is to optimise the weights or coefficients of the equaliser to reduce the effect of ISI. Finally, the performance of each algorithm in channel equalisation is assessed, and it is observed that EPO incorporated with both manta ray foraging and tunicate swarm algorithm has obtained relatively better equalisation results than other hybrid optimisation algorithms.
    Keywords: adaptive channel equalisation; FIR filter; MSE; mean square error; inter symbol interference; digital communication; TSA; tunicate swarm algorithm; EPO; emperor penguin optimisation.
    DOI: 10.1504/IJAACS.2024.10056434
     
  • ASER analysis of DF relay-assisted communication systems with diversity receivers at destinations 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) receivers at the destination node. All links in the wireless system are influenced by Nakagami-m fading distribution. The closed-form representation of ASER is derived using the moment generating function (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 confirm the correctness of the results.
    Keywords: ASER; average symbol error rate; decode-and-forward; MRC; maximal ratio combining; MGF; Nakagami-m fading; selection combining.
    DOI: 10.1504/IJAACS.2024.10052604
     
  • Energy-efficient techniques in 5G communication   Order a copy of this article
    by B. Gracelin Sheena, N. Snehalatha 
    Abstract: Fifth generation (5G) technology is in huge demand in the communication scenario due to its advanced features. The 5G communication shifts the wireless signal to the frequency range of 30-300 GHz and minimises the wavelength from centimetre to millimetre. Hence, it generates a large bandwidth and reduces traffic congestion on the network. In this survey, 50 research papers are reviewed based on the beamforming techniques used to enable the data rate in the network. 5G mobile communication methods are classified based on 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 performance metrics is discussed. The future dimension of the research is based on the gaps and issues identified in the existing research works.
    Keywords: beamforming; mobile communication; millimeter-wave (mm-wave); phased array antenna; dual-band antenna.
    DOI: 10.1504/IJAACS.2024.10056426
     
  • Deepfake detection and localisation 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 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; image manipulation detection; image forensics; face spoof detection; convolution neural network; artificial intelligence security; Deepfake detection; face forensics; deep learning.
    DOI: 10.1504/IJAACS.2024.10052496
     
  • An abnormal behaviour 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 the long time-consuming nature of the traditional massive open online learning (MOOC) online learning abnormal behaviour identification method, an abnormal behaviour recognition method of MOOC online learning based on multidimensional data mining is designed. The clustering by fast search and find of density peaks (CFSFDP) algorithm is used to mine MOOC online learning multidimensional data, the Lagrangian function is used to improve the support vector machine (SVM), and the improved SVM is used to classify the collected data. A neural network structure based on a 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 behaviour 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; massive open online learning; online learning; abnormal behaviour recognition; multi-head self-attention mechanism; neural network structure; recogniser.
    DOI: 10.1504/IJAACS.2024.10055816