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Measuring the impact of predictive models on the software project: A cost, service time, and risk evaluation of a metric-based defect severity prediction model

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Automated Software engineering 2025年第2期32卷 1-32页

作者： B, Umamaheswara Sharma Sadam, Ravichandra Assistant Professor Department of Computer Science and Engineering National Institute of Technology Calicut Kattangal Kerala Kozhikode 673601 India Associate Professor Department of Computer Science and Engineering National Institute of Technology Warangal Hanamkonda Telangana Warangal 506004 India

In a critical software system, the testers have to spend an enormous amount of time and effort maintaining the software due to the continuous occurrence of defects. To reduce the time and effort of a tester, prior works in the literature are limited to using documented defect reports to automatically predict the severity of the defective software modules. In contrast, in this work, we propose a metric-based software defect severity prediction (SDSP) model that is built using a decision-tree incorporated self-training semi-supervised learning approach to classify the severity of the defective software modules. Empirical analysis of the proposed model on the AEEEM datasets suggests using the proposed approach as it successfully assigns suitable severity class labels to the unlabelled modules. On the other hand, numerous research studies have addressed the methodological aspects of SDSP models, but the gap in estimating the performance of a developed prediction using suitable measures remains unattempt. For this, we propose the risk factor, per cent of the saved budget, loss in the saved budget, per cent of remaining edits, per cent of remaining edits, remaining service time, and gratuitous service time, to interpret the predictions in terms of project objectives. Empirical analysis of the proposed approach shows the benefit of using the proposed measures in addition to the traditional measures. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Machine learning Performance evaluation measures Software defect severity prediction Software quality

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Improving security performance of Internet of Medical Things using hybrid metaheuristic model

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Multimedia Tools and Applications 2025年第9期84卷 6403-6428页

作者： Kanneboina, Ashok Sundaram, Gopikrishnan Assistant Professor School of Engineering Malla Reddy University Telangana Hyderabad India School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravathi India

The security of the Internet of Medical Things (IoMT) is very important because it connects many medical devices to improve care operations and patient monitoring in real time. Researchers have developed high-security frameworks with attribute-based access control, hashing, encryption, and privacy preservation. Current security models are either too complex or too unreconfigurable for real-time monitoring applications. Current models’ security performance depends on their internal configuration, which is not adaptable to new attacks. This research proposes an improved hybrid metaheuristic model to improve healthcare IoT security to overcome these restrictions. The model initially uses blockchain but can change its internal hashing and encryption standards. The proposed model continuously optimizes the blockchain-based IoMT deployment’s security and quality of service by combining Elephant Herding Optimisation (EHO) and Grey Wolf Optimisation. The proposed model uses dual fitness functions to improve security and service quality against various attacks. Multiple state-of-the-art models and DDoS, man-in-the-middle, masquerading, and Sybil attacks performed to evaluate the model. According to the comparison, the proposed model improved network consistency by 8.7%, throughput by 6.4%, packet delivery ratio by 8.2%, and attack detection and mitigation by 9.4%. Real-time healthcare use cases could use the suggested model after this performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Quality of service

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Statistical assessment of Indian classical music’s role in diabetes, thyroid, hypertension management

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International Journal of Information Technology (Singapore) 2025年第1期17卷 423-429页

作者： Pingle, Yogesh Prabhakar Ragha, Lakshmappa K. Research Scholar at Department of Computer Engineering Terna Engineering College Assistant Professor at Department of Computer Science and Engineering (Data Science) Vidyavardhinis College of Engineering and Technology University of Mumbai Maharashtra Mumbai India Department of Computer Engineering Terna Engineering College University of Mumbai Maharashtra Mumbai India

This multi-center randomized controlled trial explores the therapeutic benefits of Indian classical music, specifically “Raga Therapy,” for managing diabetes, thyroid disorders, and hypertension—prevalent global health issues. The study examines how specific Ragas can enhance traditional treatments: Bhimpalas and Chhanayat for hypertension, Malkauns and Bhupal Todi for diabetes, and Shyam Kalyan and Yaman for thyroid disorders. Results show significant health improvements in patients integrating Raga Therapy into their care, highlighting its holistic benefits. The study underscores the profound connection between music and healing, positioning Indian classical music as a valuable complementary therapy. By addressing a critical gap in the literature, it advocates for further research to understand the mechanisms involved and refine Raga selection for optimal therapeutic outcomes. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Paired t-test Patient satisfaction survey Raga Therapy Wilcoxon signed-rank test

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Enhancing Object Detection Robustness In Adverse Weather Conditions

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Procedia computer science 2025年 252卷 1014-1024页

作者： Shaik Yacoob D. Phani Kumar G.G.S. Harshitha V.Y. Sai Teja C.H. Satya Veni K. Mahidhar Reddy Senior Assistant Professor Department of Computer Science and Engineering Godavari Global University Rajahmahendravaram-533296 India Assistant Professor Department of Computer Science and Engineering Godavari Global University Rajahmahendravaram-533296 India

Fundamental applications in autonomous cars, robots, and surveillance systems include object detection for safety reasons such as recognizing vehicles, pedestrians, trees, street poles, and buildings. However, one of the major challenges it faces from adverse weather conditions like rain, haze, and fog through deterioration of accuracy and an increase in safety risks such as traffic accidents. This paper proposes an enhanced object detection system to improve its robustness and accuracy in challenging environments. The proposed approach effectively addresses the problem of poor image quality due to adverse weather conditions by using unsupervised domain adaptation and including a Quasi-Translation Network and Feature Calibration Network in the YOLO(You Only Look Once) framework. It attempts to leverage the success made in CNNs(Convolutional Neural Networks) and mass amounts of datasets in order to adapt YOLO-series detectors in the best possible way, achieving object detection under challenging environmental conditions with precision and speed. Experimental results demonstrate that the solution proposed outperforms existing models in terms of adaptability and accuracy, providing a scalable and reliable solution for autonomous systems to enhance overall safety and operational efficiency in real-world environments.

关键词： Convolutional Neural Networks (CNN) Object Detection YOLO Quasi-Translation Network (QTNet) Feature Calibration Network (FCNet)

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A hybrid blended stacking disease prediction system based on symptoms

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Multimedia Tools and Applications 2025年第7期84卷 3439-3457页

作者： Indupalli, Manjula Rani Pradeepini, G. Research Scholar Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh522 302 India Professor Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh522 302 India

Traditional disease diagnosis methods often struggle with symptoms-based datasets containing categorical data, leading researchers to favor boosting algorithms like CatBoost for their computational efficiency;despite potential limitations in accuracy. To overcome these challenges, this study proposes a novel approach integrating ensemble boosting with traditional naive Bayesian techniques. Recognizing the risk of overfitting with pure ensemble methods, the model strategically blends non-linear models, including XGBoost, to assign weights to symptoms based on disease impact. The blending process, guided by XGBoost decision-making, significantly improves model accuracy compared to single-boost strategies. Preprocessing involves handling categorical data and missing values, enhancing the model's robustness. During the process of blending, the decision of XGBOOST plays an important role. The proposed model's accuracy of "97.2%" is significantly higher than the single-boost strategy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Accuracy Blended stacking CATegorical Boost Ensemble Multi classification Naive Bayesian

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Semantic Segmentation of Areal Images using Pixel Wise Segmentation

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Procedia computer science 2025年 259卷 463-472页

作者： Swathi Gowroju V. Swathi Narsimhulu K. Shilpa Choudhary Associate Professor Department of Artificial Intelligence & Machine Learning Sreyas Institute of Engineering and Technology Hyderabad Telangana Assistant Professor Department of Computer Science Sreyas Institute of Engineering and Technology Hyderabad Telangana Assistant Professor Department of Computer Science Matrusri Engineering College Hyderabad Telangana Associate Professor Department of Computer Science NGIT Hyderabad Telangana

Particularly in regions hit by flooding, precise semantic segmentation of aerial photographs is essential for post-disaster evaluation and urban planning. The high-resolution aerial photography collection FloodNet, created for pixel-by-pixel segmentation of flooded and non-flooded landscapes, is presented in this work. Aerial photos taken from various geographic regions make up the collection, and segmentation masks are used to detect flooded roads, houses, cars, plants, and other vital infrastructure. In order to conduct semantic segmentation on aerial photos and capture the fine-grained information necessary for flood damage assessments, the suggested model makes use of a U-Net architecture. Morphological post-processing is also used to improve object delineation and smooth segmentation borders. The model showed consistent performance with little overfitting, achieving training accuracy of 84% and validation accuracy of 81% over 200 epochs. Effective generalization to unknown data was demonstrated by the validation loss stabilizing at 0.55 and the training loss declining from 1.1 to 0.45. The findings show that the suggested method can reliably identify and categorize areas damaged by flooding from aerial photos, enabling automated flood assessment and speeding up disaster relief activities. There is a lot of promise for integrating this model and dataset combination into real-time catastrophe monitoring, urban planning, and geospatial analytic pipelines.

关键词： Satellite images areal images semantic segmentation UNet using morphology Pixel wise segmentation

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Unleashing Facial Expression Recognition for Stress Detection Using Deep CNN Model.

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Procedia computer science 2025年 259卷 306-315页

作者： Geethu S Kumar Jo Cheriyan Aparna N Swathy J Research Scholar Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai- 600 119 Tamilnadu India Assistant Professor Department of Information Science and Engineering Cambridge Institute of Technology Bangalore-560 036 Karnataka India Assistant Professor Department of Computer Science and Engineering Saintgits College of Engineering Kottayam- 686 532 Kerala India Assistant Professor Department of Computer Science and Engineering Cambridge Institute of Technology Bangalore-560 036 Karnataka India

The technology and field of study known as Facial Emotion Recognition (FER) focuses on recognising and deciphering human facial expressions to ascertain emotions. It incorporates aspects of psychology, machine learning, and computer vision. Considering its ability to analyse facial expressions and spot signs of stress or emotional distress, FER plays a vital role in the detection of stress. Microexpressions are fleeting, uncontrollably expressed facial expressions that convey true feelings. These ephemeral expressions, which are frequently linked to stress, can be captured by FER systems. With its capacity to manage intricate patterns and variances in face expressions, FER utilising deep learning has grown in popularity. A novel FER system was designed to overcome issues that plague current FER systems, such as imbalanced datasets and robustness to noisy inputs. Large datasets are handled effectively by FER employing Deep CNN because of its architecture, which is built for high-dimensional input like images. Leveraging FER using Deep CNN achieves notable performance improvements, attaining an accuracy of 95.65 % and F1 score of 94.02%. Deep learning-based FER marks a substantial advancement in our ability to recognize and decipher facial expressions that convey emotions. FER technology has the ability to revolutionize several fields by improving intuition and sensitivity to human emotions through further development and thoughtful assessment of ethical ramifications. FER systems will advance in sophistication as science and technology develop, offering more profound understanding of human emotional states and promoting improved human-machine interactions.

关键词： Stress PPG Conv-XGBoost deep learning mental health

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EDRLNet: privacy preservation in online social networks via deep reinforcement learning and graph convolutional networks

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International Journal of Information Technology (Singapore) 2025年 1-10页

作者： Gowda, Nithish Ranjan Venkatesh Venugopal, K.R. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bengaluru India Associate Professor Department Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bangalore India University Visvesvaraya College of Engineering Bangalore University Bangalore India

Online social networks (OSN) have become extremely popular in the past few decades. Prominent OSN companies, such as Facebook and Twitter, control a huge amount of information on its users as well as interactions that may cause people to have major privacy concerns. The OSN information is combined with other online sources to reveal sensitive information. As a result, there is a great need to anonymize the OSN data before providing it to a third party. Thus, in this study, we propose a Deep Reinforcement Learning (DRL)-based rewiring operation that entails changing the relationships (edges) between nodes (users) without changing their degree to protect users’ privacy. To accomplish this, we introduce a novel technique for Enhanced DRL Networks called “EDRLNet” which combines an Optimized Policy Network (OPN) and Graph Convolutional Network (GCN) that preserves the essential characteristics of OSN data while anonymizing it to prevent privacy breaches. This approach employs a GCN to extract embeddings (features) from the edges and nodes. Furthermore, OPN applies the graph feature representation learned by the GCN model to policy learning. Additionally, the hyperparameters of OPN are optimized by using the Addax optimization algorithm (AOA). The suggested scheme’s performance is assessed using PageRank with mean-squared error (MSE), Root mean-squared error (RMSE), mean absolute error (MAE), Privacy score, Utility Score, Clustering coefficient, privacy and information loss. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Addax optimization algorithm Anonymization Deep reinforcement learning Graph convolutional network Online social network Privacy preservation

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Enhancing Educational Video Discovery Using Advanced Latent Semantic Analysis

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Procedia computer science 2025年 252卷 784-795页

作者： B. Sindhu A. Bhaskar G. Yugesh S. Reshma B. Rohit Assistant Professor Department of Computer Science and Engineering Godavari Global University Rajamahendravaram -533296 Andhra Pradesh India

The rapid growth of online educational videos has created abundant learning resources, but also has challenges in discovering high-quality, relevant content. This project mainly focuses on video-based content analysis and providing high-quality learning sources for scholars, easing the best possible way to discover accurate resources. Existing Systems often rely on round-robin, random selection which fails to grasp semantics and linguistic complexity. The proposed system focuses on content-based analytics using NLP, where the extraction of transcripts from videos, perform relevance assessment which maps content to concept and extract insights using Latent Semantic Analysis which effectively captures underlying structures in the transcript. Complexity Assessment measures readability metrics using the Flesch-Kincaid Grade Level and SMOG Index. Created a web-based interface for easy access to educational videos, with built-in analytics to assess relevance. The insights are shared with scholars to refine content and better meet user requirements.

关键词： Latent Semantic Analysis NLP(Natural Language Processing) SMOG(Simple Measure of Gobbledygook) Index Topic Modelling TF-IDF (Term frequency-inverse document frequency) Content-based filtering

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Artificial intelligence in the detection and classification of dental caries

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Journal of Prosthetic Dentistry 2025年第5期133卷 1326-1332页

作者： Ahmed, Walaa Magdy Azhari, Amr Ahmed Fawaz, Khaled Ahmed Ahmed, Hani M. Alsadah, Zainab M. Majumdar, Aritra Carvalho, Ricardo Marins Assistant Professor Department of Restorative Dentistry Faculty of Dentistry King Abdulaziz University Jeddah Saudi Arabia Associate Professor Department of Orthopedic Surgery Faculty of Medicine Cairo University Cairo Egypt Assistant Professor Department of Civil Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia Consultant in Restorative Dentistry Dental Department East Jeddah General Hospital Ministry of Health Jeddah Saudi Arabia Graduate student Department of Computer Science Computer Science and Applications Virginia Polytechnic Institute and State University Blacksburg VA United States Professor Department of Oral Biological and Medical Sciences Faculty of Dentistry University of British Columbia Vancouver BC Canada

Statement of problem: Automated detection of dental caries could enhance early detection, save clinician time, and enrich treatment decisions. However, a reliable system is lacking. Purpose: The purpose of this study was to train a deep learning model and to assess its ability to detect and classify dental caries. Material and methods: Bitewings radiographs with a 1876×1402-pixel resolution were collected, segmented, and anonymized with a radiographic image analysis software program and were identified and classified according to the modified King Abdulaziz University (KAU) classification for dental caries. The method was based on supervised learning algorithms trained on semantic segmentation tasks. Results: The mean score for the intersection-over-union of the model was 0.55 for proximal carious lesions on a 5-category segmentation assignment and a mean F1 score of 0.535 using 554 training samples. Conclusions: The study validated the high potential for developing an accurate caries detection model that will expedite caries identification, assess clinician decision-making, and improve the quality of patient care. © 2025 Editorial Council for The Journal of Prosthetic Dentistry

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