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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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Malaria Prediction Using Fusion Learning with Enhanced Trust and Interpretability 5th

Malaria Prediction Using Fusion Learning with Enhanced Trust...

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5th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Gangone, Anjali Swapna, Mandu Bharat Kumar, G.J. Department of Computer Science and Engineering CMR Institute of Technology Hyderabad India Department of Computer Science and Engineering Sreyas Institute of Engineering and Technology Telangana Hyderabad India

ISBN: (纸本)9789819788606

Malaria, a significant global health threat, is traditionally diagnosed through manual examination of blood smears for parasite-infected cells, a method limited by its reliance on the examiner’s expertise. To overcome these limitations, this study employs deep learning for malaria prediction, leveraging models like XCEPTION, AlexNet, DenseNet, and MobileNet, enhanced with transfer learning, to classify blood smears as parasitized or uninfected using Anaconda software. The models were trained on a dataset comprising 80% of the online medical records, with 10% used for validation and the remaining 10% for testing on 224 × 224 pixel images. DenseNet emerged as the top performer with a 97% accuracy rate, followed by MobileNet, AlexNet, and XCEPTION. The study further incorporates the LIME (Local Interpretable Model-agnostic Explanations) technique for interpretability, providing understandable explanations for predictions to bolster trust and transparency in the deep learning model’s diagnostic capabilities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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Pattern Analysis and Regressive Linear Measure for Botnet Detection

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computer Systems science & engineering 2022年第10期43卷 119-139页

作者： B.Padmavathi B.Muthukumar Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennaiIndia Department of Computer Science and Engineering Easwari Engineering CollegeChennaiIndia Department of Computer Science and Engineering United Institute of TechnologyCoimbatoreIndia

Capturing the distributed platform with remotely controlled compromised machines using botnet is extensively analyzed by various ***,certain limitations need to be addressed *** provisioning of detection mechanism with learning approaches provides a better solution more broadly by saluting multi-objective *** bots’patterns or features over the network have to be analyzed in both linear and non-linear *** linear and non-linear features are composed of high-level and low-level *** collected features are maintained over the Bag of Features(BoF)where the most influencing features are collected and provided into the classifier ***,the linearity and non-linearity of the threat are evaluated with Support Vector Machine(SVM).Next,with the collected BoF,the redundant features are eliminated as it triggers overhead towards the predictor ***,a novel Incoming data Redundancy Elimination-based learning model(RedE-L)is built to classify the network features to provide robustness towards BotNets *** simulation is carried out in MATLAB environment,and the evaluation of proposed RedE-L model is performed with various online accessible network traffic dataset(benchmark dataset).The proposed model intends to show better tradeoff compared to the existing approaches like conventional SVM,C4.5,RepTree and so ***,various metrics like Accuracy,detection rate,Mathews Correlation Coefficient(MCC),and some other statistical analysis are performed to show the proposed RedE-L model's *** F1-measure is 99.98%,precision is 99.93%,Accuracy is 99.84%,TPR is 99.92%,TNR is 99.94%,FNR is 0.06 and FPR is 0.06 respectively.

关键词： BotNet threat intrusion features linearity and non-linearity redundancy regressive linear measure classification redundancy eliminationbased learning model

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Comprehensive Overview of Gas and Bio-Sensors Enabling Artificial Intelligence in Healthcare 2

Comprehensive Overview of Gas and Bio-Sensors Enabling Artif...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Raut, Yashasvi Chaudhri, Shiv Nath Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra India Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra India

ISBN: (纸本)9798331528713

Gas and biosensors are crucial in the modern healthcare system, enabling non-invasive monitoring and diagnosis of various medical conditions. These sensors are used in various applications, including smart home health monitoring devices and critical care units. Common non-imaging sensor types include gas or VOC sensors, diagnostic ECG sensors, blood glucose sensors, pulse oximeters, blood pressure sensors, body temperature sensors, EEG sensors, accelerometers, and imaging sensors like X-rays, MRIs, CT scans, and ultrasounds. Healthcare-related sensors offer numerous advantages, including diagnosing and treating medical conditions, monitoring vital signs, enabling remote patient monitoring, personalized healthcare, and early detection and prevention. However, issues like data accessibility, privacy, comfort, security, and accuracy must be considered when installing sensors in homes. Efficient data sharing and analysis require devices, sensors, and platforms to communicate effectively. Real-time data processing is essential for prompt diagnosis and intervention. In this article, we have put it briefly: medical sensors are transforming the way we track, identify, and manage adverse health conditions. © 2024 IEEE.

关键词： Glucose sensors

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An efficient sentiment analysis technique based on fine-tuned EdBERT for virtual learning environments

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Multimedia Tools and Applications 2025年第18期84卷 20161-20175页

作者： Srivastav, Gaurav Kant, Shri Srivastava, Durgesh Sharma, Neha Hu, Yu-Chen Department of Computer Science and Engineering Sharda University Gautam Budh Nagar India Department of Computer Science and Engineering Chitkara University Institute of Engineering & Technology Chitkara University Punjab Rajpura India Department of Computer Science Tunghai University No. 1727 Sec. 4 Boulevard Xitun District Taichung City407224 Taiwan

With artificial intelligence propelling rapid technological advances, many tools and frameworks have surfaced to assist virtual learning settings. They all make unique claims about how best to facilitate distance education, particularly in light of the pandemic. This paper presents a new model called "EdBERT," which is created explicitly for sentiment analysis of evaluations written by virtual learners with unmatched categorization accuracy. With Google BERT, EdBERT improves sentiment analysis specifically for educational feedback datasets. Using a comparison analysis and the "fine-tuned Google BERT," taught at several levels to understand language, context, and emotions, the methodology accurately classifies learners' feedback. The discussion includes thorough testing and analysis of the accuracy of the model, as well as information on its effectiveness and operation. The model and its operation are described in the discussion together with reliable evidence from analyses and accuracy tests. EdBERT is a cutting-edge model in AIEd sentiment analysis, boasting the best evaluation matrix to date with accuracy rates of 87.89%, F1-score of 88%, precision of 89%, and recall of 88%. These evaluation matrix values outperform all other current models covered in the article. In an effort to increase the productivity of students, teachers, and the learning environment, academic academics and scientists are delving into the relatively young field of AIEd. Trustworthy sentiment analysis may be produced using three fundamental sentiment classes: deep learning and natural language processing models. Plutchik's wheel of emotion, which will aid in more accurately recording feelings using artificial intelligence and deep learning, can be used to further this study. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Sentiment analysis

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"Survey and analysis of blockchain technologies with respect to: properties, algorithms, architecture, models, evolution and framework"

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Multimedia Tools and Applications 2025年第14期84卷 13571-13615页

作者： Tiwari, Nikita Ranjan, Prakash Biswal, Pradeep Kumar Barde, Chetan Sinha, Neha Department of Computer Science and Engineering Indian Institute of Information Technology Bhagalpur Bhagalpur India Department of Electronics and Communication Engineering Indian Institute of Information Technology Bhagalpur Bhagalpur India

The growing realm of blockchain technology has captivated researchers and practitioners alike with its promise of decentralized, secure, and transparent transactions. This paper presents a comprehensive survey and analysis of various blockchain technologies, focusing on their fundamental properties, architectural designs, operational models, evolutionary trends, and overarching frameworks. Through an extensive review of existing literature and empirical studies, this research synthesizes the multifaceted landscape of blockchain, elucidating key concepts such as consensus mechanisms, smart contracts, scalability, interoperability, and privacy protocols. By examining the evolution of blockchain from its inception to its current state and beyond, this paper delineates the trajectory of technological advancements and identifies emerging paradigms. Furthermore, it critically evaluates the strengths and limitations of diverse blockchain architectures and models, offering insights into their applicability across different domains and use cases. Finally, this study culminates in the proposal of a comprehensive framework that encapsulates the essential elements necessary for understanding, implementing, and harnessing the transformative potential of blockchain technologies. Through this holistic analysis, this research contributes to a deeper understanding of blockchain ecosystems and provides valuable guidance for future research directions and practical implementations. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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Deep ensemble model for skin cancer classification with improved feature set

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Multimedia Tools and Applications 2025年第10期84卷 7599-7626页

作者： Burada, Sreedhar Manjunathswamy, B.E. Kumar, M. Sunil Department of Computer Science and Engineering Don Bosco Institute of Technology Karnataka Bengaluru560074 India Department of Computer Science and Engineering School of Computing Mohan Babu University Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati517102 India

Early identification of skin cancer is mandatory to minimize the worldwide death rate as this disease is covering more than 30% of mortality rates in young and adults. Researchers are in the move of proposing advanced models for the accurate classification of skin disease. This paper introduces a novel skin cancer detection model under the stages of preprocessing, feature extraction, classification as well as segmentation. The given input is first preprocessed via Gaussian filtering. Subsequently, the Deep learning model termed as ‘U-net’ is used for the segmentation process. Afterward, the extraction of the feature set including features like Improved Center symmetric Local Binary Pattern (ICS-LBP), Local Gradient Pattern (LGP), Median binary pattern (MBP) is performed. Classification takes place using Deep Ensemble Model based on the extracted features, that includes BiGRU, Deep Maxout, and Improved Deep Convolutional Neural Network (CNN), respectively. Also, as the hyperparameter tuning of the classification model enhances the performance, this work introduces a new Hybrid Atom and Arithmetic Operation Algorithm (HAAOA) algorithm for tuning the optimal weights of the Bi-GRU (Bi-directional Gated Recurrent Unit) and Deep Maxout model. At last, the validation of the proposed work is performed through different performance measures by comparing it with different algorithms. Accordingly, accuracy of the adopted method at 90th learning percentage is 93.56%, which is (~) 82.63%, 85.15%, 84.98%, 83.66% and 83.42% better than AOA, ASO, BWO, BMO and DO, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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How graph convolutions amplify popularity bias for recommendation?

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Frontiers of computer science 2024年第5期18卷 121-132页

作者： Jiajia CHEN Jiancan WU Jiawei CHEN Xin XIN Yong LI Xiangnan HE School of Information Science and Technology University of Science and Technology of ChinaHefei 230026China School of Computer Science and Technology Zhejiang UniversityHangzhou 310058China School of Computer Science and Technology Shandong UniversityQingdao 250100China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China

Graphconvolutional networks(GCNs)have become prevalent in recommender system(RS)due to their superiority in modeling collaborative *** improving the overall accuracy,GCNs unfortunately amplify popularity bias-tail items are less likely to be *** effect prevents the GCN-based RS from making precise and fair recommendations,decreasing the effectiveness of recommender systems in the long *** this paper,we investigate how graph convolutions amplify the popularity bias in *** theoretical analyses,we identify two fundamental factors:(1)with graph convolution(i.e.,neighborhood aggregation),popular items exert larger influence than tail items on neighbor users,making the users move towards popular items in the representation space;(2)after multiple times of graph convolution,popular items would affect more high-order neighbors and become more *** two points make popular items get closer to almost users and thus being recommended more *** rectify this,we propose to estimate the amplified effect of popular nodes on each node's representation,and intervene the effect after each graph ***,we adopt clustering to discover highly-influential nodes and estimate the amplification effect of each node,then remove the effect from the node embeddings at each graph convolution *** method is simple and generic-it can be used in the inference stage to correct existing models rather than training a new model from scratch,and can be applied to various GCN *** demonstrate our method on two representative GCN backbones LightGCN and UltraGCN,verifying its ability in improving the recommendations of tail items without sacrificing the performance of popular *** are open-sourced^(1)).

关键词： recommendation graph convolution networks popularity bias

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Association Rules Mining for Season-Specific Time Frame in a Retail Supermarket 3

Association Rules Mining for Season-Specific Time Frame in a...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Nayeem, Md. Julker Zaman, Md. Shahid Uz Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Rajshahi Bangladesh Pundra University of Science & Technology Department of Computer Science & Engineering Bogura Bangladesh

ISBN: (纸本)9798350388282

In the realm of data mining, analysis of the market basket is a common issue. Many associations have been found from the item sets of transactions after the daily transactions have been analyzed for different seasons or the whole of the year with a specific time frame. In a retail market, there are numerous products present in their database that are regularly bought just during a certain period in a day of a specific season, even though they are not often thought of as frequently purchased products. For example, commodities like bread and cake are more in demand during the morning session, whereas beverages and noodles are more in demand during the afternoon and evening sessions. Similarly, Hot coffee has a high demand in the winter season whereas in the summer, there is a huge demand for ice cream. During the monsoon season, cake and cookies are in high demand. Rice and soybean oil have high demand in the whole year. This study focused on identifying different types of frequent items as well as recognizing the connection among several frequent item sets by analyzing supermarket transactions. Besides this, the FP-Growth algorithm is utilized in our research to determine several relevant rules for different time frames of a day (morning, afternoon and evening) within a certain season (summer, monsoon and winter) and the entire year by analyzing the transactions. Our empirical research shows that the proposed method is computationally efficient at different times in a day of a specific season or whole of the year, helping a retail supermarket, a Bangladeshi retailer, make the right choices and increase sales. © 2024 IEEE.

关键词： Association rules

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A no-code swarm simulation framework for agent-based modeling using nature-inspired algorithms

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International Journal of Information technology (Singapore) 2024年第7期16卷 4693-4699页

作者： Hasan, Ishraq Islam, Rubyeat Sharmin, Nusrat Md. Akhtaruzzaman Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh

Swarm robotics describes the coordination among multiple robots assigned to perform a single task collectively and work as a system. The system is usually used in search and-rescue missions in adverse natural environments. Research in this field involves testing theories and algorithms by using physical robots which requires expensive environment setup. This study presents a novel no-code simulation framework for swarm robotics. The software framework provides a set of building block of various components to customize the research and development process. Besides the various platforms that exist nowadays, this proposed framework overcomes the common problem of low flexibility in modeling, resulting significant improvements in the development of any simulation through customization of algorithms and multi-agent modeling. For rapid development of simulation instances, this study proposes a swarm simulation framework that allows multi-agent modeling in a no-code development environment by defining the number of agents and the behaviors they are expected to exhibit. A random environment is generated where the agents and specific target is defined to test swarm robots for search and rescue operations. The framework mainly proposes a no-code environment and provides the opportunity to test the swarm behavior and relevant theories with flexibilities to make necessary adjustments. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Multi-agent modeling No-code environment Simulation framework Swarm robotics

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