International Journal of Advanced Intelligence Paradigms (19 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.
  
  
• DEVELOPMENT OF ELECTRONIC PEST CONTROL IN PADDY FIELDS  
  by Ram Chandran.M, Vishnu Priya.R. 
  Abstract: Worldwide, agriculture is considered as the backbone of a countrys economy. The contribution of agriculture to Indias economy is steadily declining with Indias economic growth. Still, agriculture plays a significant role in the broadest economic sector. The major objective of the present work is to repel the pests that affect the agricultural fields especially paddy field. The pests that mainly affect the paddy fields are small insects, grasshoppers, and moths. Initially, we capture the images of the field and process these images using MATLAB software where we developed codes that find out whether the small insects are high or low. If the insects are high, then an appropriate pesticide is sprinkled over the field which is controlled by a PIC16F877A microcontroller. Further, we collected the frequency sensitivities of grasshoppers and moths to repel them. The time period of the day at which the grasshoppers and moths are maximum is obtained from previously published paper. Using this information we generate these frequencies at appropriate timings. As a result, the paddy field is free from grasshoppers and moths.
  Keywords: Frequency bandwidth; grasshoppers; image processing; moths; and pest control.
  
  
• SVM-based Multiple Instance Learning Approach to Select the Best Answer in CQA Sites  
  by Tirath Prasad Sahu, Naresh Nagwani, Shrish Verma 
  Abstract: A community question answering (CQA) site is an online platform where a user posts a query and receive the best answer among multiple answers posted by others. In most of the CQA site, the best answer is selected manually among multiple answers for a particular question. In the manual process, answers are voted against a question and an answer with highest votes is generally selected as the best answer. Since CQA sites are engaged in receiving the questions frequently, it becomes tedious for the asker or community to select the best answer for every posted question. This paper proposes a support vector machine (SVM)-based multiple-instance learning (MIL) technique for the selection of the best answer among all answers posted for of a particular question in a CQA site. The MIL aims to learn answers (multiple-instance) of a question (a bag) using SVM. The prediction of best answer for the question is derived from the maximum instance margin problem of MIL in supervised classification. It is shown that performance parameters ROC-AUC, PRC-AUC, and G-mean for the proposed model are significantly better than the existing traditional model in prediction of the best answers.
  Keywords: Support vector machine; Community question answering; multiple instance learning; classification; topic modelling; activeness; expertise.
  
  
• Who Will be My Dearest One? An Expert Decision  
  by ARUP ROY, SOUMYA BANERJEE 
  Abstract: Recommendation systems assist in finding the right things to the right users. The counterpart matchmaking recommendation system connects on users, enhances the social relationship, saves time, minimizes the risks involved in offline suggestions, and encourages collaboration. Matching implies the ability to recommend potential partners for target users. It matches the profiles based on the preferences provided by the users. In the present era, people are highly involved with their busy schedule. Thus smart recommendation system will be highly demanding service for natives in smart cities. Smart cities, a fully planned and digitized city where people like to adopt online services. Demographic Filtering is the widely used technique for matchmaking recommendation systems. In this research, a novel demographic filtering-based matchmaking framework that precisely identifies the users profiles to provide the top-n recommendations is proposed. The matchmaking is accomplished using the K-means and Ant colony hybrid. Support vector regression is also employed to enhance the performance and make the decision more precise and realistic.
  Keywords: Ant Colony Optimization; Demographic Filtering; K-Means Clustering; Recommendation Systems; Support Vector Regression.
  
  
• Template Based Approach for Question Systematization  
  by Urmila Shrawankar, Komal Pawar 
  Abstract: Main reason behind questioning is to gather information needed or seek explanation about certain topic. But correct information can be gathered only with a specific error free question. Various applications pursuit for error free standard question. The issue of Statement construction is more concentrated than Question construction. This project work particularly concentrates on error free Question construction using text systematization. Template based approach is used for carrying out this process. Question Template is the basic idea behind Template based approach. Templates are manually designed through coding. This is accompanied by Dictionary approach and powerful Natural Language Processing technique like POS Tagging. This technique follows Maximum Entropy based algorithm. Different error parameters are considered for the correction. This work focuses domain specific WH-type questions of English along with imperative questions. This work has different applications namely, to set exam question papers, to help English learners to study interrogative constructs properly, to produce intermediate output for complex systems like question-answering system.
  Keywords: POS tagger; question templates; systematization; template based approach; WH-questions.
  DOI: 10.1504/IJAIP.2019.10027084
   
  
• 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
   
  
• Adaptive hybrid transmit power control algorithm for wireless body area networks  
  by M. Raj Kumar Naik, P. Samundiswary 
  Abstract: Transmit power control (TPC) technique is considered as a key solution for WBAN for low-power operations, which can be proactive or reactive depending on the channel conditions. While reactive approaches involve control packet overhead and additional delay, the proactive approaches are prone to prediction errors and involve prediction delay. Hence, a hybrid technique is needed which combines the advantages of both these techniques for all type of channel conditions. In this paper, an adaptive hybrid TPC (AHTPC) algorithm for WBAN is developed. In AHTPC, if the received signal strength indicator (RSSI) and packet delivery ratio (PDR) values fall outside the range of some lower and upper bounds, the reactive transmission power control algorithm (RTPC) is executed. If the difference of consecutive RSSI samples becomes larger, then proactive transmission power control algorithm (PTPC) is executed. From the simulation results, it is shown that AHTPC algorithm outperforms existing proactive and reactive techniques.
  Keywords: wireless body area networks; WBAN; power control; adaptive; hybrid; channel condition.
  DOI: 10.1504/IJAIP.2025.10074309
   
  
• Median relative intersection of confidence intervals for bandwidth estimation in mean shift clustering technique  
  by Prasad Kaviti, Valli Kumari Vatsavayi 
  Abstract: Mean shift algorithm is a non-parametric iterative algorithm widely used in segmentation, clustering, object tracking, etc. However, tuning the bandwidth parameter and selection of kernel with its convergence is required. This paper proposes a modified mean shift in terms of bandwidth selection and its adequate kernel selection. Mean shift equipped with median relative intersection of confidence intervals (MRICI) for multispectral image clustering is proposed. Initially different kinds of bandwidth estimators like static, Silverman, Scott, ICI and MRICI are evaluated and are considered four classes of kernels Gaussian, Epaenchnikov, flat, biweight with general convergence. Later different combinations of the four classes of kernels and different bandwidth estimators of mean shift are evaluated. Results show an improvement in intracluster similarity based on silhouette measure for MRICI bandwidth estimation using the Gaussian kernel of mean shift when compared to other combinations of mean shifts.
  Keywords: mean shift clustering; kernels; bandwidth; confidence intervals; multispectral images.
  DOI: 10.1504/IJAIP.2025.10074306
   
  
• Enhanced gradient boosting - a novel method to improve performance of XGB technique  
  by Kolluru Venkata Nagendra, Maligela Ussenaiah 
  Abstract: Gradient boosting algorithm (Friedman, 1999) was produced for high prescient capacity. Its selection was restricted to minimise errors for the previous trees; only one decision tree was created. To build small size models, it takes large amount of time. To overcome these drawbacks, extreme gradient boosting (XGBoost) (Chen and Guestrin, 2016) was developed. It decreases the model building time as well as increases the performance. The experimental results demonstrate that enhanced gradient boosting (EGB) algorithm perform better than the remaining algorithms like XGB, gradient boosting (GB) etc in the context of class imbalanced dataset. The EGB algorithm works as same as XGB and also works on balanced data with high accuracy. EGB works well on both balanced and imbalanced data. The results obtained show that the area under curve obtained through EGB is higher than the area under curve obtained through XGB.
  Keywords: machine learning; boosting; gradient boosting; enhanced gradient boosting; EGB; extreme gradient boosting; XGB; multithreading.
  DOI: 10.1504/IJAIP.2025.10074307
   
  
• Handwritten north Indian script recognition using machine learning: a survey  
  by Reya Sharma, Baijnath Kaushik, Naveen Kumar Gondhi 
  Abstract: The handwritten script recognition is an interesting and significant area of research due to the existence of wide variety of challenges in handwritten Indian scripts. Intensive research work is available on the recognition of scripts like Chinese, Roman, Arabic and Japanese. But the research work done on Indian scripts is still at its infancy, therefore in this paper a review has been presented on the recognition of various handwritten North Indian scripts. Variety of techniques associated with feature extraction and classification of handwritten North Indian scripts are precisely discussed in this work. An attempt has been made with this survey to address and highlight significant results obtained so far in this field and these results are represented in tabular form so as to provide a clear idea by looking the data at once. This survey also provides beneficial future directions for research in handwritten North Indian scripts by analysing the existing difficulties and steps needed for the development of North Indian scripts OCR.
  Keywords: handwritten North Indian scripts; OCR; Devanagari; Bangla; Gurmukhi.
  DOI: 10.1504/IJAIP.2025.10074308
   
  
• An empirical analysis of software maintainability metrics: object-oriented approach versus traditional  
  by Gokul Yenduri, N. Veeranjaneyulu 
  Abstract: Software is a great blend of creativity and engineering, which plays a major role in different fields. Software is pre dominantly developed using object-oriented approach. Software quality is foremost of all because it has a vast influence on software development life cycle (SDLC). There are many factors influencing quality where maintenance is most important of them. Maintainability of software can be measured using different metrics. In recent times object-oriented (OO) approach has become salient in building scientific and business applications but structural approach has its intensification in embedded applications. It is significant to find impact of metrics on each other when different programming languages are considered because they play a significant responsibility in predicting software maintainability. This research empirically analysed the dependency of various metrics values obtained from software which are similar in both structured (C) and object oriented programming (Java) using CCCC and HM tool. Further, the relationships between structured and object oriented programming is found out by comparing the different techniques such as data visualisation, correlation in terms of maintainability.
  Keywords: software quality; metrics; software development life cycle; SDLC; maintainability.
  DOI: 10.1504/IJAIP.2025.10074310
   
  
• A complete analysis of integrated vehicle health management for aircraft - with pros, cons, suggestions for improvement and future prospects  
  by K. Vimalkumar, Shriram K. Vasudevan 
  Abstract: Integrated vehicle health management is a concept which comprises the integration of sensors, communication technologies, artificial intelligence, data analytics and software health management to facilitate vehicle-wide abilities for diagnosing problems and recommending solution. The IVHM uses sensors to monitor the condition/health of the vehicle by analysing the data readings from the installed sensors in the vehicle. The aircraft needs to be monitored continuously for the flawless and continuous functioning. The data collected from the sensors installed in the aircraft helps to analyse the present and predict the future performance of the aircraft. Also, the data can also be used to make operational decisions, which are very critical for real-time performance. This paper provides the state-of-the-art report of the IVHM concept.
  Keywords: integrated vehicle health management; IVHM; IVHM for aircraft; prognostics; prediction; aircraft safety; aircraft health monitoring.
  DOI: 10.1504/IJAIP.2025.10074311
   
  
• Some properties of bipolar complex neutrosophic graph  
  by Hossein Rashmanlou, Muhammad Shoaib, M.A. Malik, Yahya Talebi, Ali Asghar Talebi 
  Abstract: A bipolar complex neutrosophic model is useful in field of mathematics which gives more precision, comparability and flexibility to the system as compared to the complex intuitionistic fuzzy model and complex neutrosophic model. In these years, a mathematical approach is a generalised approach of blending different aspects. According to the above mathematical approach, we introduce strong techniques which are properties of bipolar complex neutrosophic graph. We prove that a bipolar complex neutrosophic graph is a generalisation of the complex neutrosophic graph. In this paper, our important aim of the study is to apply some properties namely Cartesian products, composition, strong product, semi-strong product and direct product of bipolar complex neutrosophic graph with examples.
  Keywords: Cartesian products; composition; strong product; semi strong product; direct product.
  DOI: 10.1504/IJAIP.2025.10074312