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WSO-KELM: War Strategy Optimization-Based Kernel Extreme Learning Machine for Automatic Software Fault Prediction Model

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Journal of The Institution of Engineers (India): Series B 2025年第1期106卷 145-163页

作者： Elci, J. Brundha Nandagopalan, S. Department of Computer Science and Engineering Vemana Institute of Technology Visvesvaraya Technological University Karnataka Belagavi India Department of Information Science and Engineering Vemana Institute of Technology Visvesvaraya Technological University Karnataka Belagavi India

The software development projects’ testing part is usually expensive and complex, but it is essential to gauge the effectiveness of the developed software. Software Fault Prediction (SFP) primarily serves to detect faults within software components. The scale of software projects is getting larger, making it impossible and time-consuming to analyze the flaws manually. Artificial Intelligence (AI) techniques are mainly incorporated to solve this problem. The massive data retrieved from the software repositories helps in identifying the features that are highly correlated with fault prediction and the features that are not. This paper introduces a Kernel Extreme Learning Machine (KELM) for SFP. To optimize the hyperparameters of the classifier and also enhance the classification accuracy, the War Strategy Optimization (WSO) algorithm is employed here. The experimental evaluations are conducted using different baseline actual-world datasets of software programs such as Java-coded open-source and PROMISE datasets. The experimental outcomes are conducted using different performance metrics that include sensitivity, Accuracy, and F1-score with values of 0.96%, 0.97%, and 0.96%, respectively. The results show that KELM is beneficial, and the optimization provided by the WSO algorithm also helps us obtain higher classification accuracy for the SFP problem and improve the fault detection process. © The Institution of Engineers (India) 2024.

关键词： Software reliability

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Study of Mangrove Forests Using Google Earth Engine 8

Study of Mangrove Forests Using Google Earth Engine

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8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Chakraborty, Rhydam Das, Rwitayan Sarkar, Bappaditya Bhattacharjee, Anindita Das Institute of Engineering & Management Department of Computer Science & Engineering Kolkata India

ISBN: (纸本)9798331520762

Mangrove forests are crucial ecosystems that provide valuable goods and services while serving as carbon sinks. These ecosystems act as natural buffers against coastal erosion, mitigate the impacts of climate change, and support diverse biodiversity. However, they are increasingly threatened by rising sea levels, changing salinity patterns, and anthropogenic activities such as deforestation and urbanization. Monitoring the health of mangrove forests is critical for ensuring their conservation and long-term sustainability. This study investigates vegetation dynamics in a selected region using satellite-derived indices, specifically the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Chlorophyll Index (NDCI). © 2025 IEEE.

关键词： Biotic

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Fruit Quality Monitoring System using CNN 1

Fruit Quality Monitoring System using CNN

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Ravikumar, B.N. Vidya, M.S. Gowda, Naveen Chandra NarendraBabu, C.R. Veena, H.N. Ramani, D. Roja Bms Institute of Technology and Management Department of Information Science and Engineering Bengaluru India Bms Institute of Technology and Management Department of Computer Science and Engineering Bengaluru India School of Computer Science and Engineering Reva University Bengaluru India Sjb Institute of Technology Department of Computer Science and Engineering Bengaluru India New Horizon College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331529246

The grading of fruits relies on inspections, experiences, and observations, with a proposed system integrating machine learning techniques to assess fruit freshness. By analyzing 2D fruit portrayals based on shape and color, limitations arise as different fruits may share similar color, size, and shape values. To enhance accuracy, the system combines size, shape, and color analysis with Convolutional Neural Network (CNN) technology. Upon capturing the fruit image, properties like size, shape, and color are extracted, followed by CNN training and testing processes. The proposed work have implements UNet and En-UNet chosen over other architectures like ResNet or MobileNet for segmentation. This innovative approach utilizes CNN to extract comprehensive features, yielding promising results in fruit freshness detection with accuracy of 99% and loss rate of 0.0123. © 2025 IEEE.

关键词： Convolutional neural networks

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Defense for Advanced Persistent Threat with Inadvertent and Malicious Insider Threats

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无人系统（英文） 2024年第2期12卷 341-358页

作者： Ziqin Chen Guanpu Chen Yiguang Hong Department of Control Science and Engineering and Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghaiP.R.China School of Electrical Engineering and Computer Science KTH Royal Institute of TechnologyStockholmSweden

In this paper,we propose a game theory framework to solve advanced persistent threat problems,especially considering two types of insider threats:malicious and *** this framework,we establish a unified three-player game model and derive Nash equilibria in response to different types of insider *** analyzing these Nash equilibria,we provide quantitative solutions to advanced persistent threat problems pertaining to insider ***,we have conducted a comparative assessment of the optimal defense strategy and corresponding defender's costs between two types of insider ***,our findings advocate a more proactive defense strategy against inadvertent insider threats in contrast to malicious ones,despite the latter imposing a higher burden on the *** theoretical results are substantiated by numerical results,which additionally include a detailed exploration of the conditions under which different insiders adopt risky *** conditions can serve as guiding indicators for the defender when calibrating their monitoring intensities and devising defensive strategies.

关键词： Security game advanced persistent threat insider threats Nash equilibrium

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Deep learning based RAGAE-SVM for Chronic kidney disease diagnosis on internet of health things platform

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Multimedia Tools and Applications 2024年 1-39页

作者： Kandukuri, Prabhakar Abdul, Ashu Kumar, Kuchipudi Prasanth Sreenivas, Velagapudi Ramesh, G. Gundu, Venkateswarlu Department of Artificial Intelligence and Machine Learning Chaitanya Bharathi Institute of Technology Telangana Hyderabad500075 India Department of Computer Science and Engineering SRM University Andhra PradeshNeerukonda Mangalagiri 522503 India Department of Computer Science & amp Engineering Koneru Lakshmaiah Education Foundation Andhra PradeshVaddeswaram 522302 India Department of Computer Science and Engineering SRK Institute of Technology Andhra Pradesh Vijayawada521108 India Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology TelanganaBachupally Hyderabad500090 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra PradeshVaddeswaram 522302 India

Chronic kidney disease (CKD) is a prominent disease that causes loss of functionality in the kidney. Doctors can now more easily gather patient health status data due to the growth of the Internet of Health Things (IoHT). The IoHT data contains a huge number of redundant data, making it challenging to predict CKD disease quickly and accurately. In healthcare applications like feature-based classification, a variety of disease diagnosis systems were used to address this problem. Current disease detection algorithms suffer from imbalanced dataset processing, low-accuracy feature learning, and high computational power requirements. Thus, deep learning-based clinical decision support systems have been developed to solve these complexities. To remove outliers from medical data, data collected with IoHT devices is first pre-processed using an enhanced K-means clustering technique. The Synthetic Minority over Sampling Technique is used to balance data because the IoHT dataset is highly imbalanced. The classifier detects anomalies in less time because of the implemented processing step. The balanced CKD dataset is then presented for use with a novel classifier called Residual Attention-Gated Autoencoder with Support Vector Machine. In order to improve accurate detection, the adopted classifier can learn and extract features. The proposed method results in an increased accuracy of 99.43% within 70.1 s of computation time. The mean intersection of the union and kappa coefficient of the proposed method is 98.15% and 98.1%, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Classification (of information)

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BGRU-MTRA: bilinear GRU networks with multi-path temporal residual attention for suspicious activity recognition

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Neural Computing and Applications 2025年第1期37卷 185-212页

作者： Pandey, Ajeet Kumar, Piyush Computer Science and Engineering National Institute of Technology Patna Ashok Rajpath Bihar Patna800005 India

Suspicious activity recognition (SAR) is an active research field in computer vision and image processing due to the rapid demand for intelligent video surveillance systems. However, current automated systems focus too much on the temporal dynamics of events in videos and ignore the importance of spatial dynamics. The existing methods for SAR are complex and demand substantial resources. Hence, this paper proposes a novel trade-off architecture for SAR integrating the strengths of the multi-path temporal residual attention (MTRA) module with the bilinear-gated recurrent unit (BGRU) module. The MTRA module combines spatial feature extraction (SFE) with temporal residual attention network (TRAN) blocks for improving SAR by enhancing resilience to variations in object sizes, viewpoints, and motion patterns. It selects relevant action features, addresses vanishing gradient issues, and reduces spatial dimension. The BGRU module preserves spatial features, improving the model’s ability to recognize fine-grained features and complex temporal patterns for effectively recognizing suspicious activities. The BGRU-MTRA system yields recognition accuracy of 93.28%, 98.97%, 99.86%, and 99.66%, and 48.42% on the benchmark hybrid crime action (HCA), UT interaction (UTI), CAVIER, hockey fight (HF), and the most challenging UCF-crime datasets, respectively. Through comprehensive experiments, it is clear that the proposed method outperforms state-of-the-art methods over benchmark datasets with reduced parameters. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Spatio-temporal data

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Global-local term fusion for optimised community Q&A topic modelling

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International Journal of Innovative Computing and Applications 2024年第1期15卷 50-69页

作者： Prabha, Sneh Sardana, Neetu Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Community question and answer (Q&A) websites have become invaluable information and knowledge-sharing sources. Effective topic modelling on these platforms is crucial for organising and navigating the vast amount of user-generated content. To address these challenges, we propose a novel global-local term fusion with optimised community (GLOCOM) Q&A topic modelling approach that leverages both local and global term importance to enhance topic modelling on community Q&A websites. GLOCOM combines term frequency-inverse document frequency for local importance and entropy for global importance. Further, we employ fuzzy clustering to enhance the representation of multifaceted topics. Furthermore, clustering results are optimised using a genetic algorithm (GA) to refine cluster assignments and centroids. We compared the proposed model with baseline models LDA and FLSA. GLOCOM has performed consistently well for all topic numbers. It has shown an improvement of 8.86% in silhouette score as compared to LDA and excelled for datasets with size > 3 MB. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Inverse problems

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Classification of Pancreatic Cancer using Kolmogorov-Arnold Networks: An Upgrade on Traditional Machine Learning for Medical Classification 6

Classification of Pancreatic Cancer using Kolmogorov-Arnold ...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Vinod, Ratnaparkhi Gaurang Kumar, Manemoni Arun Chowdary, Chinthalapudi Vaahnitha Suvarchala, K. Department of Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad India

ISBN: (纸本)9798331522667

Pancreatic cancer is one of the most fatal cancers, and medical doctors basically face major problems in its early diagnosis. This article throws light on the work involving the application of Kolmogorov-Arnold Networks (KANs), where we deploy a new machine learning paradigm in the diagnosis of pancreatic cancer. The process of classification basically happens in pancreatic cancer using urinary biomarkers. Unlike Logistic Regression and XGBoost, models have struggled with complex and non-linear medical data, KANs can manage large and complex datasets with ease. Using a dataset of urinary biomarkers, trained a KAN model with EBM techniques for feature selection to increase the model's interpretability and feature relevance. The model presented in this paper showed superior performance compared to traditional models in terms of high accuracy and generalization capability. KANs therefore will be very essential in handling efficiently the challenge presented by the vastness of data in medical studies regarding pancreatic cancer detection. Findings from the studies indicate a better potential in reliable use for progression in developing improved detection of pancreatic cancer using the KAN method. The publication contributes to current research that makes innovative uses in machine learning a reality for improvements in diagnosis as well. © 2025 IEEE.

关键词： Diseases

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Evaluation of Routing Protocol Performance for Enhanced Operations of Unmanned Aerial Vehicles (UAVs) 2

Evaluation of Routing Protocol Performance for Enhanced Oper...

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2nd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024

作者： Gupta, Vinti Yadav, Devendra Kumar Agarwal, Megha Srm Institute of Science and Technology Computer Science and Engineering Ghaziabad India Xim University Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350372847

Unmanned Aerial Vehicles (UAVs) are airborne nodes that are controlled remotely from ground stations. They have been used in a variety of applications in recent years, including disaster management, military operations, search and rescue, ecological study, and others. Among the advancements, the notion of Flying Ad-hoc Networks (FANETs) stands out. FANET, functions remotely like Mobile Ad-hoc Networks (MANETs) and Vehicular Ad-hoc Networks (VANETs), are an advancement over standard networking systems. Ad-hoc networks are necessary for both civilian and military operations, demanding a variety of support systems. In Ad-hoc Networks, data is always sent under all situations, which is required for seamless communication across all UAVs. In contrast, the complex dynamics and ever-changing topology of UAV networks make it difficult for researchers to integrate efficient routing procedures into FANETs. The accomplishment of UAV routing procedures is related to the total network's success rate. Producing a routing system capable of cost- effective and reliable data transfer in such a difficult environment is a difficult assuming. In this study, we look at routing protocols using the NS3 simulator. In this research, we investigate routing protocols using the NS3 simulator. The study looks at the effectiveness of several UAV routing protocols, including Ad-Hoc On-Demand Distance Vector (AODV), Destination Sequenced Distance Vector (DSDV), and Optimized Link State Routing (OLSR). These protocols' performance is evaluated using a variety of measures, including Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), Throughput, End-to-End Delay, and Jitter Delay. The findings estimation the effectiveness of routing processes in the presence of varying UAV speeds. This research carefully explores how different routing protocols function under varied UAV intensities and speeds. © 2024 IEEE.

关键词： Routing protocols

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An Exploration of Deep Learning Techniques for the Detection of Grape Diseases

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Recent Advances in computer science and Communications 2024年第2期17卷 1-12页

作者： Pandey, Kavita Chandak, Abhimanyu Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Plant diseases are one of the major contributors to economic loss in the agriculture industry worldwide. Detection of disease at early stages can help in the reduction of this loss. In recent times, a lot of emphasis has been done on disease detection due to the overall increase in production as well as the loss of grape number. With deep learning, having a promising future and having the advantages of automatic learning and feature extraction, the use of these techniques has now been widely spread. This paper reviewed the existing deep-learning techniques available for grape disease detection. Firstly, covering the various steps in a grape disease detection model ranging from the various sources of image acquisition, the different image augmentation techniques and the various models used, and the parameters required to evaluate. Secondly, the study summarizes the im-portant findings of all literature available on the theme. The paper also tries to highlight the various challenges faced by the researchers and the common trend among them, so that future research on the topic can achieve higher performance. © 2024, Bentham science Publishers. All rights reserved.

关键词： Losses

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