Dr. SWADHIN KUMAR BARISAL

@soa.ac.in

Dr. SWADHIN KUMAR BARISAL


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https://researchid.co/swadhinbarisal
40

Scopus Publications

175

Scholar Citations

7

Scholar h-index

6

Scholar i10-index

Scopus Publications

  • An efficient blending model for classifying security-related software vulnerabilities into software development life cycle phases
    Pushkar Kishore, Swadhin Kumar Barisal, Durga Prasad Mohapatra, Rajib Mall
    Software Quality Journal, 2026
  • Conflict aware retail recommendation decision support system
    Bijayini Mohanty, Santilata Champati, Swadhin Kumar Barisal
    Discover Applied Sciences, 2026
    Retail recommendation systems play a crucial role in enhancing customer engagement and driving sales across modern commerce platforms. Traditional Association Rule Mining (ARM) based approaches often prioritize consistency and completeness in recommendations, overlooking the inherent uncertainty and contradictions in real-world retail data. To address these challenges, this study introduces the Conflict Aware Retail Recommendation Decision Support (CAR2DS) framework, which integrates Paraconsistent Annotated Logic (PAL) with evidential analysis to enable conflict-tolerant and transparent retail recommendations. This framework prioritizes ARM-derived rules based on support-confidence strength, temporal consistency, and reliability indices, ensuring coherent and explainable decision-making under uncertainty. Empirical evaluation reveals that CAR2DS achieves superior performance, attaining an average accuracy of 95.8% and demonstrating mean gains of 23.6% over ARM and 14.3% over FARM . It provides an interpretable decision with validating its transparency and reliability. Additionally, the model demonstrates balanced performance across key metrics, including conflict resolution, stability, and temporal adaptivity. The proposed system establishes a new paradigm for explainable, Conflict Aware retail intelligence.
  • MACHINE LEARNING FOR PREDICTING AUDIENCE PREFERENCES IN DANCE
    Faizan Anwar Khan, Swadhin Kumar Barisal, Chintan Thacker, Prabhjot Kaur, Nirmala Devi, Ashutosh Kulkarni
    Shodhkosh Journal of Visual and Performing Arts, 2025
    Artificial intelligence and performing art intersect to provide new opportunities to study human emotion, creativity and aesthetic experience. In this paper, I have introduced a generalized machine learning model to predict the audience preferences in the field of dance by applying the multimodal information visual, audio, and physiological to one analytical system. The CNNLSTMTransformer fusion model is based on the proposed CNN-LSTM-Transformer fusion, which captures the spaces choreography, time rhythm, and affective resonance as the high predictive accuracy (MSE = 0.061, R 2 = 0.94, r = 0.97). The framework can determine key elements of audience engagement, including the physiological arousal, rhythmic synchronization, and expressive movement patterns, through attention-based feature fusion and interpretability systems, like SHAP and Grad-CAM. As the experimental assessment shows, the model not only performs better than the baseline architectures, but is also respectful of artistic integrity and cultural sensitivity. The study will help advance the field of intelligent systems that will bridge between computational modeling and creative interpretation, which will lead to emotion-aware, culturally adaptive AI-based applications in performing arts.
  • Real-Time Internet Congestion Detection Using Passive Flow Analysis
    Kholoud Maswadi
    Journal of Internet Services and Information Security, 2025
    This paper describes SUSTAIN-CTRL, an information systems (IS) sustainability artificial intelligence (AI) control framework that places an LSTM-based forecasting model at its "heart". The framework processes heterogeneous telemetry data, including computation, environmental, and network sensors, as well as unstructured logs, transforming them into synchronized feature vectors. Fine-grained, multi-stream, energy-demand forecasting, semantic inefficiency scoring, and anomaly detection at a parallel inference stage using LSTM, transformer, and autoencoder models, respectively, generate labels for a consolidated decision dataset. This dataset is used by a heuristic multi-objective optimizer that minimizes a weighted sum of energy consumption, SLA-violation penalties, and carbon emissions. The carbon budget and SLA-validated control vectors are optimized while generating human-readable explanations for auditability. SUSTAIN-CTRL issues dynamic VM scaling and cooling command execution via orchestration APIs, closing the real-time feedback loop for continuous adaptation. Empirical evaluation using a 24-hour testbed showed that SUSTAIN-CTRL reduced energy consumption and emissions by 22.7% and 14.5%, respectively, while improving SLA by 2.7 percentage points. These results confirm that LSTM forecasting, when integrated into a closed-loop AI architecture, sustains performance-aligned operation decoupled from LSTM forecasting.
  • A novel approach to optimise fuzzy association rule by using evolutionary genetics algorithm
    Bijayini Mohanty, Santi Lata Champati, Swadhin Kumar Barisal
    International Journal of Mathematics in Operational Research, 2025
    In data mining, extracting useful information from complicated and ambiguous datasets has remained a significant issue. To address such problems, the fuzzy logic approach is becoming increasingly very popular. The traditional association rule mining and clustering are two highly efficient approaches for finding underlying information in this discipline. This approach integrates the fuzzy logic theory to improve the efficiency of these two approaches. The integrated approach is more adaptive to dealing with real-world data. So, this study proposes a hybrid framework that effectively explores and optimises the number of generated rules. This work provides a unique cluster fuzzy association rules (CFARs) technique by combining fuzzy association rule mining and fuzzy C-means clustering. The proposed model generates three clusters for the fuzzy association rules for the considered 'online retail' dataset. This approach processes the primary set of CFAR to filter-out optimal number of rules. The optimisation process is carried out under the guidance of evolutionary genetics algorithm. The mining method extracts 5,000 fuzzy association rules from the considered data, which the optimisation process then reduces to 2,005 fuzzy association rules.
  • Enhancing Retail Strategies Through Anomaly Detection in Association Rule Mining
    Bijayini Mohanty, Santi Lata Champati, Swadhin Kumar Barisal
    IEEE Access, 2025
    Association rule mining (ARM) is a fundamental technique for uncovering meaningful patterns and relationships within retail datasets, providing valuable insights for decision-making processes in the retail industry. Traditional association rule mining (TARM) methods sometimes fail to handle inconsistencies and contradictions for Boolean logic concepts. To overcome the scenario, paraconsistent Annotated (PAL) logic offers a solution by embracing contradictions and providing a framework for reasoning with inconsistent information. Our approach integrates PAL with Association rule mining to form a para-association rule. Based on Boolean logic, TARM may struggle to handle such situations effectively. We propose a comprehensive framework named Para-Association rule mining (PARM), which provides decision-making by offering two distinct criteria: decided and undecided. To resolve undecided rules, an anomaly detection technique based on Isolation Forest is employed, ensuring that all rules are ultimately categorized into either “accept” or “reject” based on refined decision criteria. After solving the anomaly within the undecided criteria, all decisions are categorized into two criteria, such as accept and reject, to enhance decision-making. Our proposed approach is validated with our experimental results, where we have considered 549904 input retail transactions for generating retail decisions. Initially, we successfully generated 5000 association rules from this input, which are categorized as accept and reject 3387 and 1613 respectively. The comparative accuracy of the proposed approach achieves an accuracy of 90% which is a 15.38% improvement over the traditional approach. Our approach gives an 88% inconsistency management rate, which is a 36.67% improvement over the traditional approach. To validate the robustness of PARM, a noise test was conducted, demonstrating that the model effectively maintains performance under noisy conditions. The Z-test results confirm that the robustness improvement is statistically significant. In conclusion, PARM represents a significant advancement in data mining and decision-making techniques by offering a novel framework for handling inconsistencies and uncertainty in retail datasets.
  • Heart disease prognosis using machine learning classifier with hyperparameter optimization
    Manas Kumar Swain, Lambodar Jena, Narendra Kumar Kamila, Soumen Nayak, Swadhin Kumar Barisal
    Design Optimization Using Artificial Intelligence, 2025
    In modern era, disease diagnosis is a crucial task that requires the highest level of accuracy. Heart disease prognosis is currently regarded as one of the most challenging jobs in the healthcare sector. The Light Gradient Boosting Machine (LightGBM) classifier was used in our study to forecast a patient’s risk of heart failure. Eventually, the hyperparameters were tuned to increase efficiency. Class imbalance in the dataset was also encountered in this chapter and was resolved using Synthetic Minority Oversampling Technique (SMOTE). The results indicate a 95% accuracy cap. The recall rate is 95%, while the precision rate is 94%. The suggested model also has an F-score of 95%.As a result, the suggested model can predict patient heart failure more precisely. Thus, it may be very helpful to doctors in the diagnosis of heart disease.
  • Cybersecurity Analysis of 5G Telesurgery Systems Using Radial Basis Spatial Encoder Mechanisms
    M.S. Gowtham, Amit Kumar, Reena R, Manish Nagpal, Richa Tiwari, Swadhin Ku. Barisal
    2025 2nd International Conference on Multidisciplinary Research and Innovations in Engineering Mrie 2025, 2025
  • Integrating Dynamic Access Control in Hybrid Cloud Environments for Enhanced Security and Load Balancing
    Lakshya Swarup, Saritha Srinivasmurthy Raghotham, B.P. Singh, J Albert Mayan., Aseem Aneja, Swadhin Ku. Barisal
    2025 International Conference on Automation and Computation Autocom 2025, 2025
    Hybrid cloud environments come into play more each month, this service is becoming increasingly important for security and load balancing. Hybrid cloud environments are too fluid to manage with traditional access control methods and load-balancing solutions. This is where dynamic access control integration comes in. Dynamic access control is a process that enables organizations to define and enforce access controls based on the context of the request, not merely user identities. This could be applied in a hybrid cloud environment to implement access controls based on the whereabouts of users, the kind of data, and the threat level. This is intended to add an extra layer of security as we can limit the users who have permission to access specific resources. You can add dynamic access control to your load-balancing solutions for resource efficiency and performance improvements. It also reduces the possibility of service outage, as more accurate granular access control based on lower visibility to higher certain users making fewer requests at a time than another set can simplify algorithms that tombstone or down-replicated out-of-date servers and thus carve their load across racks of machines.
  • Advanced Framework For Enhanced Diabetes Prediction Using Mathematical Models
    Ganesh D, Swadhin Ku. Barisal, Jegan G, Manvinder Brar, Manish Nagpal, Akash Kumar Bhagat
    2025 International Conference on Metaverse and Current Trends in Computing Icmctc 2025, 2025
    Diabetes is a prevalent and potentially fatal disease worldwide. Apart from negative effects on the blood, it leads to complications such as kidney failure, cardiovascular diseases, and vision impairment, which significantly increase mortality rates, such as kidney, heart and vision issues highlighting the need for a system to accurately predict diabetes from medical records. This research proposes a method for diabetes prediction using a mathematical model. The research proposes the improvement of a graph theory-based adaptive deep neural network (GT+ADNN) to anticipate diabetes in its early stages. In diabetes hazard prediction, graph embedding captures the complex relationships among features in a lower-dimensional space, while community detection identifies clusters of related attributes. Centrality criteria prioritize key features based on their importance and influence throughout the graph. In the research, diabetes data from hospitals is gathered. The data isinitially processed using Z-Score normalization of the acquired data. The proposed method is implemented using Python software. For comparative research, the proposed GT+SDNN mathematical model is evaluated against two existing algorithms to validate its effectiveness. The proposed model obtained superior performance in terms of F1-score (88.9%), accuracy (94.5%), precision (90.71%), and recall (82.38%). The result shows that the proposed GT+ADNN achieves the best results when compared to other existing algorithms.
  • DLDSS: Dual-Layered Decision Support System for Retail Recommendation
    Swadhin Kumar Barisal, Bijayini Mohanty, Santilata Champati, Gayatri Nayak, Bharat Jyoti Ranjan Sahu, et al.
    Icoicc 2025 3rd International Conference on Intelligent and Cloud Computing, 2025
  • Enhancing 5G network management through association rule mining: Uncovering network traffic patterns
    Sushanta Meher, Bijayini Mohanty, Bharat Jyoti Ranjan Sahu, Santilata Champati, Swadhin Kumar Barisal, Shatarupa Dash
    Intelligent Computing Techniques and Applications, 2025
  • SMUP: A technique to improve MC/DC using specified patterns
    Swadhin Kumar Barisal, Arpita Dutta, Sangharatna Godboley, Bibhudatta Sahoo, Durga Prasad Mohapatra
    Computers and Electrical Engineering, 2024
  • Software Bug Classification Using Machine Learning Approach
    Sandeep Soumya Sekhar Mishra, Swadhin Kumar Barisal
    Prospects of Science Technology and Applications, 2024
  • An Intelligent Diagnostic System for Type 2 Diabetes Mellitus
    Abhilash Pati, Manoranjan Parhi, Binod Kumar Pattanayak, Swadhin Kumar Barisal, Gayatri Nayak
    Lecture Notes in Networks and Systems, 2024
  • A Multi-Objective Based Genetic Approach for Increasing Crop Yield on Sustainable Farming
    Swadhin Kumar Barisal, Gayatri Nayak, Bijayini Mohanty, Pushkar Kishore, Santilata Champati, Alakananda Tripathy
    Sustainable Farming Through Machine Learning Enhancing Productivity and Efficiency, 2024
  • CGWO: An Improved Grey Wolf Optimization Technique for Test Case Prioritization
    Gayatri Nayak, Swadhin Kumar Barisal, Mitrabinda Ray
    Programming and Computer Software, 2023
  • An efficient two-stage pipeline model with filtering algorithm for mislabeled malware detection
    Pushkar Kishore, Swadhin Kumar Barisal, Durga Prasad Mohapatra, Rajib Mall
    Computers and Security, 2023
  • Early Stage Ovarian Cancer Prediction using Machine Learning
    Chinmayee Nayak, Alakananda Tripathy, Manoranjan Parhi, Swadhin Kumar Barisal
    2023 International Conference in Advances in Power Signal and Information Technology Apsit 2023, 2023
  • URBAN TRAFFIC FLOW PREDICTION USING TIMEGAN MODEL
    Pratik Dutta, Anuradha Das, Manoranjan Parhi, Alakananda Tripathy, Debahuti Mishra, Swadhin Kumar Barisal
    2023 2nd International Conference on Ambient Intelligence in Health Care Icaihc 2023, 2023
  • A Comprehensive Voice Data Analysis for Parkinson's Disease Prediction via Machine Learning Techniques
    Gayatri Nayak, Siddharth Dehury, Swadhin Kumar Barisal, Sandeep Soumya Sekhar Mishra, Pratik Dutta, Lambodar Jena
    2023 2nd International Conference on Ambient Intelligence in Health Care Icaihc 2023, 2023
  • Designing Fault-Counter for Object-Oriented Software using Bagging Technique
    Sandeep Soumya Sekhar Mishra, Pratik Dutta, Gayatri Nayak, Alakananda Tripathy, Pushkar Kishore, Swadhin Kumar Barisal
    2023 International Conference in Advances in Power Signal and Information Technology Apsit 2023, 2023
  • BOOMPizer: Minimization and prioritization of CONCOLIC based boosted MC/DC test cases
    Swadhin Kumar Barisal, Shorya Pratap Singh Chauhan, Arpita Dutta, Sangharatna Godboley, Bibhudatta Sahoo, Durga Prasad Mohapatra
    Journal of King Saud University Computer and Information Sciences, 2022
  • A Comparative Analysis on Traffic Flow Prediction
    Anuradha Das, Swadhin Kumar Barisal, Pratik Dutta
    Proceedings 2022 International Conference on Machine Learning Computer Systems and Security Mlcss 2022, 2022
  • Family Classification of Malicious Applications using Hybrid Analysis and Computationally Economical Machine Learning Techniques
    Pushkar Kishore, Swadhin Kumar Barisal, Durga Prasad Mohapatra
    Proceedings 2022 IEEE Wic ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology Wi Iat 2022, 2022
  • Familial Analysis of Malicious Android Apps Controlling IOT Devices
    Subhadhriti Maikap, Pushkar Kishore, Swadhin Kumar Barisal, Durga Prasad Mohapatra
    Lecture Notes in Networks and Systems, 2022
  • GWO Based Test Sequence Generation and Prioritization
    Gayatri Nayak, Mitrabinda Ray, Swadhin Kumar Barisal, Bichitrananda Patra
    Smart Innovation Systems and Technologies, 2022
  • Agility Based Coverage Improvement
    Swadhin Kumar Barisal, Arpita Dutta, Sangharatna Godboley, Bibhudatta Sahoo, Durga Prasad Mohapatra
    Lecture Notes in Business Information Processing, 2022
  • Concolic-Based Software Vulnerability Prediction Using Ensemble Learning
    Swadhin Kumar Barisal, Pushkar Kishore, Gayatri Nayak, Ridhy Pratim Hira, Rohit Kumar, Ritesh Kumar
    Smart Innovation Systems and Technologies, 2022
  • Security Improvement and Privacy Preservation in E-Health
    Pushkar Kishore, Swadhin Kumar Barisal, Kulamala Vinod Kumar, Durga Prasad Mohapatra
    IEEE International Conference on Communications, 2021
  • MC/DC guided Test Sequence Prioritization using Firefly Algorithm
    Swadhin Kumar Barisal, Arpita Dutta, Sangharatna Godboley, Bibhudatta Sahoo, Durga Prasad Mohapatra
    Evolutionary Intelligence, 2021
  • Particle Swarm Optimized Federated Learning for Securing IoT Devices
    Pushkar Kishore, Swadhin Kumar Barisal, Durga Prasad Mohapatra
    2021 IEEE Globecom Workshops GC Wkshps 2021 Proceedings, 2021
  • Measuring MC/DC Coverage and Boolean Fault Severity of Object-Oriented Programs Using Concolic Testing
    Swadhin Kumar Barisal, Pushkar Kishore, Anurag Kumar, Bibhudatta Sahoo, Durga Prasad Mohapatra
    Smart Innovation Systems and Technologies, 2021
  • Familial analysis of interface and program targeting noise contained malware using image processing
    Pushkar Kishore, Swadhin Kumar Barisal, Durga Prasad Mohapatra
    Proceedings 2020 IEEE International Symposium on Sustainable Energy Signal Processing and Cyber Security Isssc 2020, 2020
  • JavaScript malware behaviour analysis and detection using sandbox assisted ensemble model
    Pushkar Kishore, Swadhin Kumar Barisal, Durga Prasad Mohapatra
    IEEE Region 10 Annual International Conference Proceedings TENCON, 2020
  • An incremental malware detection model for meta-feature API and system call sequence
    Pushkar Kishore, Swadhin Kumar Barisal, Durga Prasad Mohapatra
    Proceedings of the 2020 Federated Conference on Computer Science and Information Systems Fedcsis 2020, 2020
  • ELM-MVD: An Extreme Learning Machine Trained Model for Malware Variants Detection
    Pushkar Kishore, Swadhin Kumar Barisal, Alle Giridhar Reddy, Durga Prasad Mohapatra
    Communications in Computer and Information Science, 2020
  • NITRSCT: A Software Security tool for collection and analysis of Kernel Calls
    Pushkar Kishore, Swadhin Kumar Barisal, Shikhar Vaish
    IEEE Region 10 Annual International Conference Proceedings TENCON, 2019
  • Validating object-oriented software at design phase by achieving MC/DC
    Swadhin Kumar Barisal, Suvam Suvabrata Behera, Sangharatna Godboley, Durga Prasad Mohapatra
    International Journal of System Assurance Engineering and Management, 2019
  • Achieving MC/DC using UML Communication Diagram
    Parbati Mahanto, Swadhin Kumar Barisal, Durga Prasad Mohapatra
    Proceedings 2018 International Conference on Information Technology Icit 2018, 2018

RECENT SCHOLAR PUBLICATIONS

  • An efficient blending model for classifying security-related software vulnerabilities into software development life cycle phases
    P Kishore, SK Barisal, DP Mohapatra, R Mall
    Software Quality Journal 34 (2), 10 , 2026
    2026
  • Conflict aware retail recommendation decision support system
    B Mohanty, S Champati, SK Barisal
    Discover Applied Sciences , 2026
    2026
  • Enhancing 5G network management through association rule mining: Uncovering network traffic patterns
    S Meher, B Mohanty, BJR Sahu, S Champati, SK Barisal, S Dash
    Intelligent Computing Techniques and Applications, 19-22 , 2025
    2025
    Citations: 5
  • MLDSS: customer-centric retail recommendation via multi-layered decision support system
    S Chamapti, B Moahanty, SK Barisa
    IEEE Access , 2025
    2025
    Citations: 6
  • Heart disease prognosis using machine learning classifier with hyperparameter optimization
    MK Swain, L Jena, NK Kamila, S Nayak, SK Barisal
    Design Optimization Using Artificial Intelligence, 257-266 , 2025
    2025
  • Enhancing retail strategies through anomaly detection in association rule mining
    B Mohanty, SL Champati, SK Barisal
    IEEE Access , 2025
    2025
    Citations: 7
  • DLDSS: Dual-layered decision support system for retail recommendation
    SK Barisal, B Mohanty, S Champati, G Nayak, BJR Sahu, L Jena
    2025 International Conference on Intelligent and Cloud Computing (ICoICC), 1-7 , 2025
    2025
    Citations: 4
  • Integrating Dynamic Access Control in Hybrid Cloud Environments for Enhanced Security and Load Balancing
    L Swarup, SS Raghotham, BP Singh, JA Mayan, A Aneja, SK Barisal
    2025 International Conference on Automation and Computation (AUTOCOM), 442-446 , 2025
    2025
  • A novel approach to optimise fuzzy association rule by using evolutionary genetics algorithm
    B Mohanty, SL Champati, SK Barisal
    International Journal of Mathematics in Operational Research 31 (3), 283-313 , 2025
    2025
    Citations: 4
  • SMUP: A technique to improve MC/DC using specified patterns
    SK Barisal, A Dutta, S Godboley, B Sahoo, DP Mohapatra
    Computers and Electrical Engineering 120, 109706 , 2024
    2024
    Citations: 3
  • 4 A Multi-Objective Based Genetic Approach
    SK Barisal, G Nayak, B Mohanty, P Kishore, S Champati, A Tripathy
    Sustainable Farming through Machine Learning: Enhancing Productivity and … , 2024
    2024
  • A Multi-Objective Based Genetic Approach for Increasing Crop Yield on Sustainable Farming
    A Kumar Barisal, S., Nayak, G., Mohanty, B., Kishore, P., Champati, S ...
    Sustainable Farming through Machine Learnin. https://doi.org/10.1201 … , 2024
    2024
    Citations: 1
  • Software Bug Classification Using Machine Learning Approach
    SSS Mishra, SK Barisal
    Prospects of Science, Technology and Applications,Edition1st Edition,CRC … , 2024
    2024
  • CGWO: an improved grey wolf optimization technique for test case prioritization
    G Nayak, SK Barisal, M Ray
    Programming and computer software 49 (8), 942-953 , 2023
    2023
    Citations: 9
  • An efficient two-stage pipeline model with filtering algorithm for mislabeled malware detection
    P Kishore, SK Barisal, DP Mohapatra, R Mall
    Computers & Security 135, 103499 , 2023
    2023
    Citations: 7
  • A Comprehensive Voice Data Analysis for Parkinson's Disease Prediction via Machine Learning Techniques
    G Nayak, S Dehury, SK Barisal, SSS Mishra, P Dutta, L Jena
    2023 2nd International Conference on Ambient Intelligence in Health Care … , 2023
    2023
    Citations: 1
  • URBAN TRAFFIC FLOW PREDICTION USING TIMEGAN MODEL
    P Dutta, A Das, M Parhi, A Tripathy, D Mishra, SK Barisal
    2023 2nd International Conference on Ambient Intelligence in Health Care … , 2023
    2023
  • Designing Fault-Counter for Object-Oriented Software using Bagging Technique
    SSS Mishra, P Dutta, G Nayak, A Tripathy, P Kishore, SK Barisal
    2023 International Conference in Advances in Power, Signal, and Information … , 2023
    2023
  • Early stage ovarian cancer prediction using machine learning
    C Nayak, A Tripathy, M Parhi, SK Barisal
    2023 International Conference in Advances in Power, Signal, and Information … , 2023
    2023
    Citations: 6
  • An Intelligent Diagnostic System for Type 2 Diabetes Mellitus
    A Pati, M Parhi, BK Pattanayak, SK Barisal, G Nayak
    International Conference on Advanced Computing and Intelligent Engineering … , 2022
    2022

MOST CITED SCHOLAR PUBLICATIONS

  • BOOMPizer: Minimization and prioritization of CONCOLIC based boosted MC/DC test cases
    SK Barisal, SPS Chauhan, A Dutta, S Godboley, B Sahoo, DP Mohapatra
    Journal of King Saud University-Computer and Information Sciences 34 (10 … , 2022
    2022
    Citations: 22
  • MC/DC guided test sequence prioritization using firefly algorithm
    SK Barisal, A Dutta, S Godboley, B Sahoo, DP Mohapatra
    Evolutionary Intelligence 14 (1), 105-118 , 2021
    2021
    Citations: 16
  • Validating object-oriented software at design phase by achieving MC/DC
    SK Barisal, SS Behera, S Godboley, DP Mohapatra
    International Journal of System Assurance Engineering and Management 10 (4 … , 2019
    2019
    Citations: 14
  • An incremental malware detection model for meta-feature api and system call sequence
    P Kishore, SK Barisal, DP Mohapatra
    2020 15th Conference on Computer Science and Information Systems (FedCSIS … , 2020
    2020
    Citations: 11
  • JavaScript malware behaviour analysis and detection using sandbox assisted ensemble model
    P Kishore, SK Barisal, DP Mohapatra
    2020 IEEE REGION 10 CONFERENCE (TENCON), 864-869 , 2020
    2020
    Citations: 10
  • Nitrsct: A software security tool for collection and analysis of kernel calls
    P Kishore, SK Barisal, S Vaish
    TENCON 2019-2019 IEEE Region 10 Conference (TENCON), 510-515 , 2019
    2019
    Citations: 10
  • CGWO: an improved grey wolf optimization technique for test case prioritization
    G Nayak, SK Barisal, M Ray
    Programming and computer software 49 (8), 942-953 , 2023
    2023
    Citations: 9
  • Enhancing retail strategies through anomaly detection in association rule mining
    B Mohanty, SL Champati, SK Barisal
    IEEE Access , 2025
    2025
    Citations: 7
  • An efficient two-stage pipeline model with filtering algorithm for mislabeled malware detection
    P Kishore, SK Barisal, DP Mohapatra, R Mall
    Computers & Security 135, 103499 , 2023
    2023
    Citations: 7
  • MLDSS: customer-centric retail recommendation via multi-layered decision support system
    S Chamapti, B Moahanty, SK Barisa
    IEEE Access , 2025
    2025
    Citations: 6
  • Early stage ovarian cancer prediction using machine learning
    C Nayak, A Tripathy, M Parhi, SK Barisal
    2023 International Conference in Advances in Power, Signal, and Information … , 2023
    2023
    Citations: 6
  • Security improvement and privacy preservation in e-health
    P Kishore, SK Barisal, KV Kumar, DP Mohapatra
    ICC 2021-IEEE International Conference on Communications, 1-6 , 2021
    2021
    Citations: 6
  • Enhancing 5G network management through association rule mining: Uncovering network traffic patterns
    S Meher, B Mohanty, BJR Sahu, S Champati, SK Barisal, S Dash
    Intelligent Computing Techniques and Applications, 19-22 , 2025
    2025
    Citations: 5
  • Cooperative Swarm based Evolutionary Approach to find optimal cluster centroids in Cluster Analysis
    B Naik, S Mahapatra, S Swetanisha, SK Barisal
    International Journal of Computer Science Issues (IJCSI) 9 (3), 425 , 2012
    2012
    Citations: 5
  • DLDSS: Dual-layered decision support system for retail recommendation
    SK Barisal, B Mohanty, S Champati, G Nayak, BJR Sahu, L Jena
    2025 International Conference on Intelligent and Cloud Computing (ICoICC), 1-7 , 2025
    2025
    Citations: 4
  • A novel approach to optimise fuzzy association rule by using evolutionary genetics algorithm
    B Mohanty, SL Champati, SK Barisal
    International Journal of Mathematics in Operational Research 31 (3), 283-313 , 2025
    2025
    Citations: 4
  • Particle swarm optimized federated learning for securing iot devices
    P Kishore, SK Barisal, DP Mohapatra
    2021 IEEE Globecom Workshops (GC Wkshps), 1-6 , 2021
    2021
    Citations: 4
  • SMUP: A technique to improve MC/DC using specified patterns
    SK Barisal, A Dutta, S Godboley, B Sahoo, DP Mohapatra
    Computers and Electrical Engineering 120, 109706 , 2024
    2024
    Citations: 3
  • Family Classification of Malicious Applications using Hybrid Analysis and Computationally Economical Machine Learning Techniques
    P Kishore, SK Barisal, DP Mohapatra
    2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and … , 2022
    2022
    Citations: 3
  • Applying unsupervised system-call based software security techniques for anomaly detection
    P Kishore, G Nayak, SK Barisal
    Journal of Information and Optimization Sciences 43 (5), 915-922 , 2022
    2022
    Citations: 3