International Journal of Advanced Intelligence Paradigms (13 papers in press)

Regular Issues

• Big data secure storing in cloud and privacy preserving mechanism for outsourced cloud data  
  by Dr B. Renuka 
  Abstract: Big data is a buzz word in this decade it gets tremendous concentration in these days by the researchers because of the characteristics and features. And also big data gives lot of challenges to the world that is storage, processing and security. In any technology security is the prime concern in this manuscript, we map to misuse new complications of enormous information regarding security, further more, confer our thought toward viable and insurance protecting enlisting in the immense data time. Specifically, we at first formalize the general building of gigantic data examination, recognize the relating security necessities, and present a capable and assurance sparing outline for immense data which is secured in cloud.
  Keywords: Privacy Preserving; Security; Big data; Cloud Computing; outsourced data.
  
  
• A Novel Variant of Bat Algorithm Inspired from CATD-Pursuit Strategy & Its Performance Evaluations  
  by Shabnam Sharma, Sahil Verma, Kiran Jyoti 
  Abstract: This paper presents a novel nature inspired optimization technique, which is a variant of Standard Bat Algorithm. This optimization technique is inspired from the pursuit strategy of microchiroptera bats and their efficient way of adaptation according to dynamic environment. Here dynamic environment describes different movement strategies adopted by prey (target), during their pursuit. Accordingly, bats have to adopt different pursuit strategies to capture the prey (target). In this research work, a variant of Bat Algorithm is proposed considering the pursuit strategy Constant Absolute Target Detection (CATD), adopted by bats, while targeting preys moving erratically. The proposed algorithm is implemented in Matlab. Results obtained are validated in comparison to Standard Bat Algorithm on the basis of best, mean, median, worst and standard deviation. The results demonstrate that the proposed algorithm provides better exploration and avoid trapping in local optimal solution.
  Keywords: Bat Algorithm; Constant Absolute Target Detection (CATD); Computational Intelligence; Echolocation; Meta-heuristic; Nature-Inspired Intelligence; Optimization; Pursuit Strategy; Swarm Intelligence.
  DOI: 10.1504/IJAIP.2021.10030248
   
  
• Wireless Smart Automation Using IOT Based Raspberry Pi  
  by Vasu Goel, Akash Deep, Madireddy Vivek Reddy, Yedukondala Rao Veeranki 
  Abstract: In this paper we propose a smart door lock system and lighting system for home automation. This door lock system and lighting system is controlled by Radio Frequency Identification (RFID) reader which is programmed by Raspberry Pi to detect the input swipe through our university combo card or a RFID tag and wirelessly sends the signal to the Espruino (ESP) Wi-Fi module and Node Microcontroller Unit (MCU) which in turn activates the lighting system and door lock system. The mainstream application of the system will be in hostel rooms or in our homes wherever door locks are there so that doors can be opened anytime we want without disrupting our work or getting up from our places in case of any injury with a swipe of card
  Keywords: Internet-Of-Things; Raspberry pi; Radio-Frequency Identification; Home automation; MQTT.
  DOI: 10.1504/IJAIP.2019.10026853
   
  
• Data Mining Techniques and Fuzzy Logic to Build a Risk Prediction System for Stroke  
  by Farzana Islam, M. Rashedur Rahman 
  Abstract: Nowadays, by using different computational system medical sector predict diseases. These systems not only aid medical experts but also normal people. In recent years stroke becomes life threatening deadly cause and it increased at global alarming state. Early detection of stroke disease can be helpful to make decision and to change the lifestyle of people who are at high risk. There is a high demand to use computational expertise for prognosis stroke. Research has been attempted to make early prediction of stroke by using data mining techniques. This paper proposes rule based classifier along with other techniques. The dataset is collected from Dhaka medical college, situated in Dhaka, Bangladesh To build a more accurate and acceptable model the system uses different classification methods likely- Decision tree, Support vector machine, Artificial neural network and fuzzy model. K-means, EM and fuzzy C-means clustering algorithm are used to label the dataset more accurately. Fuzzy inference system is also built to generate rules. ANFIS provides the most accurate model.
  Keywords: stroke; decision tree; SVM; MLP; artificial neural network; support vector machine; fuzzy model; FIS; ANFIS; data-mining; fcm; clustering; EM clustering; k-means; Bangladeshi dataset; fuzzy rule.
  DOI: 10.1504/IJAIP.2021.10054275
   
  
• An optimized fuzzy edge detector for image processing and their use in modular neural networks for pattern recognition  
  by Isidra Espinosa-Velazquez, Patricia Melin, Claudia Gonzalez, Frumen Olivas 
  Abstract: In this paper, the development of a fuzzy edge detector optimized with the metaheuristics: Genetic Algorithms and Particle Swarm Optimization is presented, based on the sum of differences method, using as inputs the absolute values of the difference from the pixels in the image. The Pratts figure of merit metric was used to know the performance of the proposed fuzzy edge detector. A modular neural network was designed for the recognition of faces in benchmark images and comparisons were made with different works carried out with other fuzzy edge detection systems. The main contribution of this research work is the development of a new fuzzy edge detector method optimized.
  Keywords: fuzzy logic; fuzzy edge detector; optimization; GA; genetic algorithm; PSO; Particle swarm Optimization; Neural networks.
  
  
• PEBD: Performance Energy Balanced Duplication Algorithm for Cloud Computing  
  by Sharon Priya Surendran, Aisha Banu W 
  Abstract: With the increasing demand of cloud data, efficient task scheduling algorithms are required with minimal power consumption. In this paper, the Performance-Energy Balanced Duplication (PEBD) scheduling approach is proposed for energy conservation at the point of task duplication. Initially, the resources are preprocessed with the Manhattan distance based Fuzzy Clustering (MFC).Then resources are scheduled using a Novel duplication aware fault tolerant based League-BAT algorithm and faults expected during job executions can be handled proactively. The fault adaptive firefly optimization is used for minimizing faults and it keeps information about resource failure. Consequently, the optimization ensures that performance is improved with the help of task duplication with low energy consumption. The duplications are restricted and they are strictly forbidden if they provide significant enhancement of energy consumption. Finally, enhanced compress & Join algorithm is used for efficient compression processing. It considers both schedule lengths and energy savings to enhance the scheduling performance with less power consumption. The performance of energy consumption and makespan for the proposed approach is increased with 6% and 0.5 % respectively
  Keywords: Manhattan distance; Fuzzy clustering; Resource scheduling; Duplication; fault tolerance; energy conservation.
  
  
• A Community Based Trusted Collaborative Filtering Recommender Systems Using Pareto Dominance Approach  
  by Anupama Angadi, Satya Keerthi Gorripati 
  Abstract: Recommender System algorithms provided clarification to information overload problem suffered by netizens. The Collaborative Recommender Filtering approach takes the user-item rating matrix as an input and recommends items based on the perceptions of similar neighbours. However, sparsity issue in the rating matrix leads to untrustworthy predictions. However, the conventional Collaborative Recommender Filtering method chooses ineffective descriptive users as neighbours for each target user. This hints that the recommendations made by the system remain inaccurate. The proposed approach addresses this issue by applying a pre-filtering process and integrates community detection with Pareto dominance, which considers trusted neighbours from the community into which the active user pertains and eliminates dominant users from the neighbourhood. The results on the proposed framework showed a noteworthy improvement in all the accuracy measures when related to the traditional approaches.
  Keywords: Community Detection; Recommender Systems; Sparsity; Pareto dominance; Cold Start; Trust propagation;.
  
  
• Web Server Workload Prediction using Time Series Model  
  by Mahendra Pratap Yadav, Akanksha Kunwar, Ram Chandra Bhushan, Dharmendra Kumar Yadav 
  Abstract: In distributed systems, multi-tier storage systems and cloud data-centers are used for resource sharing among several clients. To fulfill the clients request, the cloud providers share it's resources and manage the workload, which introduces many performance challenges and issues. One of the main challenges is resource provisioning in virtual machine (VMs or Container) since VMs are subjected to meet the demand of users with different profiles and Quality of Service (QoS). This proactive resource management approach requires an appropriate workload prediction strategy for real-time series data. The time series model exhibits prominent periodic patterns for the workload that evolves from one point of time to another with some short of time in random fluctuation. In this paper, a solution for the prediction of web server load problem has been proposed, which is based on seasonal ARIMA (Autoregressive Integrated Moving Average Model) model. ARIMA is a forecasting technique which predicts the future value based on its inertia. In seasonal ARIMA, seasonal AR and MA are used to predict the value xt (CPU workload time series) with the help of data values and errors at time lags that are multiple to the span of seasonality. We have evaluated our proposed method using real-world web workload data.
  Keywords: Cloud Computing; Elasticity; Auto-scaling; Time Series; Machine Learning.
  DOI: 10.1504/IJAIP.2022.10034175
   
  
• Load-balanced multilayered clustering protocol to maximise the lifetime of wireless sensor networks  
  by Rohan Kumar Gupta, Arnab Nandi 
  Abstract: This paper introduces an innovative clustering protocol for load balancing in wireless sensor networks (WSNs). In the proposed protocol, square shape clusters of equal area are arranged in a multilayer fashion, and the base station is at the centre of the network. The equal area of square clusters offers a nearly equal number of member nodes in each cluster that leads to comparable energy consumption at cluster heads for transmitting and receiving data from member nodes. This paper also introduces a new routing approach in which hop selection is based on the difference of angle between the source and destination cluster heads with respect to a particular point. The efficiency of the proposed protocol concerning network lifetime and energy consumption is evaluated and compared with low-energy adaptive clustering hierarchy (LEACH), enhanced-modified LEACH (E-MODLEACH) and least distance clustering (LDC). The proposed protocol's efficiency is also evaluated for different optimisation algorithms.
  Keywords: WSN; wireless sensor network; clustering protocol; load balancing; network lifetime; GWO; grey wolf optimiser; PSO; particle swarm optimisation; GSA; gravitational search algorithm; LEACH; low-energy adaptive clustering hierarchy; E-MODLEACH; enhanced-modified LEACH; LDC; least distance clustering.
  DOI: 10.1504/IJAIP.2025.10069936
   
  
• Case-based reasoning methodology for eLearning recommendation system  
  by Swati Shekapure, Dipti D. Patil 
  Abstract: Increasingly, eLearning has become a leading development trend in the industry. It has been observed that traditional learning methods have turned to modern and innovative learning. Due to a revolution in technology, everyone started learning by using the internet. They have been using online material for gaining instructions. So, while they procure the learning they admit certain records, which are not significant to answer all their exploratory questions. Ultimately, there was a huge delay while scrutinising the essential material on the internet, so there was an extremity to customise the search by acquiring certain information of a user to improve the search quality and save time. The recommended eLearning system is a case based system using a case-based reasoning approach and a distinct classification algorithmic rule to categorise the students' learning interest. This system assembles student's learning preferences from a distinct discussion and systematically categorises that characteristic into a learning standard.
  Keywords: adaptive system; case-based reasoning; case-based library; eLearning; K nearest neighbour; learning style; learning objects; learning path; recommendation system; retrieval process.
  DOI: 10.1504/IJAIP.2022.10035296
   
  
• An efficient implicit Lagrangian twin bounded support vector machine  
  by Umesh Gupta, Deepak Gupta 
  Abstract: In this paper, an efficient implicit Lagrangian twin bounded support vector machine based on fuzzy membership is proposed with the dual formulation in order to reduce the sensitivity of noise and outliers. Here, the fuzzy membership values are determined according to distribution of the samples. We adopt the quadric and centroid fuzzy-based approach for LTBSVM and propose quadric based fuzzy membership approach and centroid based fuzzy membership approach for LTBSVM. The problems make strongly convex by using L2-norm of the vector of slack variable. Also, the solution of the problem is obtained through simple linear convergent iterative approach. Further, comparative performance analysis of proposed approach with state of art approaches have been done on standard real life with artificial datasets. This analysis announces that proposed approaches are effective in terms of generalisation performance and computational speed to other approaches. Our proposed approaches statistically validate and verify based on various parameters.
  Keywords: TSVM; twin support vector machine; twin bounded support vector machine; Lagrangian function; iterative approaches; fuzzy membership.
  DOI: 10.1504/IJAIP.2025.10069937
   
  
• An efficient memory based differential evolution for constrained optimisation  
  by Raghav Prasad Parouha 
  Abstract: Differential evolution (DE) and its diverse variants are prominently inflated by unfitting operators like mutation and crossover. Basically, DE is doesn't commit to memorise the finest effects attained in early part of the preceding peers. In this paper, a new DE (named as ^mbDE) based on memory mechanism is offered for constrained optimisation problems (COPs). It contained new mutation and crossover (so-called as swarm mutation and swarm crossover) created by particle swarm optimisation (PSO) circumstance. The performances ^mbDE has been tested over 22 CEC'2006 and 18 (10 and 30-dimensional) CEC'2010 COPs. The empirical results confirm the superiority of mbDE over many contemporary algorithms.
  Keywords: differential evolution; PSO; particle swarm optimisation; mutation; crossover; elitism; COPs; constrained optimisation problems.
  DOI: 10.1504/IJAIP.2025.10068846
   
  
• Document summarisation using recurrent neural network  
  by K. Vijayakumar, J. Dafni Rose 
  Abstract: Automatic summarisation refers to summarising a document using software and it helps to reduce large text documents to a short set of words or a paragraph that delivers the main meaning of the full text. The extracted features from the documents are used for the automatic summarisation process and remain a successfully proven approach but it leads to drawbacks with respect to structure, redundancy, coherence. Existing methods for single document summarisation usually make use of only the first sentence or fixed number of words from the beginning it is contained in the specified document. The proposed system mainly aims at generating a summary of at least a minimum length unlike the existing system that generates empty summary if it could not find the keyword present in the input document which meets the attention weight beyond a threshold.
  Keywords: text; summarise; document; recurrent; neural network; text processing; automatic summary.
  DOI: 10.1504/IJAIP.2025.10069935