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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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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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An optimal group-wise non-rigid image registration technique based clinical decision support for dementia prediction

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Multimedia Tools and Applications 2025年 1-26页

作者： Mary, F. Sheeja Asanambigai, V. Fred, A. Lenin Assistant Professor Senior Grade School of Computing Dept. of CSE Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai India Computer Science and Engineering Annamalai University Chidambaram India Dept. of CSE Mar Ephraem College of Engineering and Technology Marthandam India

The fundamental challenge in clinical practice is predicting whether patients with cognitive symptoms or impairment will improve or stay stable. Population ageing is a worldwide phenomena with various impacts. Among dementia, Alzheimer disease (AD) is common in the elderly which is the most common form of dementia and may contribute to 60–70% of cases. An early and precise AD diagnosis enhances patient quality of life. In this study, we propose an optimal group-wise non-rigid image registration technique for dementia (AD) prediction. We introduce an improved cuckoo search optimization (ICSO) algorithm to search optimal group-wise dense correspondence in large image sets in non-rigid image registration which reduce dimensionality of the search space. Then, we illustrate a teacher-student learning based optimization algorithm which cooperates with PLS regression to extract deformation which separates the relationship information suitable for each other from the raw relationship information. After that, we compute the distribution of morphologies of age by fitting smooth percentile curves to these scores using hybrid capsule neural network with least Median Square (hybrid CNN-LMS) curve fitting model. Finally, the simulation results of proposed hybrid CNN-LMS technique are compared to the existing methods to analyze the measures. We observed that the accuracy of our proposed hybrid CNN-LMS technique is 96.23% and 95.82% for RSS and ANDI datasets respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Neurodegenerative diseases

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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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The Impact of SMS Phishing using Machine Learning Classifiers with Innovative Techniques

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Procedia computer science 2025年 260卷 608-615页

作者： Anisha Asirvatham C. Meenakshi Research Scholar Department of Computer Science Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai India Associate Professor Department of Computer Science Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai India

These days, machine learning has an amazing ability to present the results efficiently. Network technology computing systems have long served as a critical framework to supply machine learning with deterministic computing resources. This technique can be advantageous to networking. The application of machine learning in detection of phishing of SMS in a network is the main topic of this article. This not only stimulates new network applications but also aids in the resolution of certain obsolete network issues. The fundamental approach to describe how machine learning technology works in network model is summarized in this article. The SMS spam can be detected using the proposed system, where the feature extraction can be done using stemming and the different machine learning classifiers are used to evaluate the accuracy of the proposed system. The different machine learning classifiers used in this system are Logistic regression, linear regression, K Nearest Neighbors and Naïve Bayes. The new system SpamSMS is the ensemble method using Naïve Bayes and Stacking Algorithm which gives the maximum accuracy towards the provided dataset to check for the spam messages.

关键词： Network Intrusion Detection Neural Network Artificial Intelligence computer Network Machine learning classifiers

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An Extensive Overview of anamoly Detection Analysis in FL-Based IDS

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Procedia computer science 2025年 259卷 326-335页

作者： Himani Tyagi Abhilasha Singh Research Scholar Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi- NCR Campus Ghaziabad Associate Professor Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi- NCR Campus Ghaziabad

In this paper, we present a Federated Learning-based Intrusion Detection System (FL-IDS) aimed at enhancing security in Connected Autonomous Vehicles (CAVs) within IoT environments. The novelty lies in the dual utilization of Support Vector Machine (SVM) and Convolutional Neural Networks (CNN) for anomaly detection, leveraging the Flower framework for privacy-preserving, decentralized model training. Unlike traditional centralized approaches, FL-IDS ensures data confidentiality by sharing only model updates. Using the NSL-KDD dataset, our evaluation highlights superior accuracy and loss metrics, validating the efficiency of both machine learning and deep learning methods for detecting DDoS, Probe, and U2R attacks. This work contributes to the advancement of secure and efficient vehicular communication systems, providing scalable solutions for real-world IoT deployments.

关键词： Federated Learning Deep Learning Machine learning CAV IDS Edge Computing

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A proposed deep learning model for multichannel ECG noise reduction

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Discover Artificial Intelligence 2025年第1期5卷 1-16页

作者： Maurya, Jay Prakash Manoria, Manish Joshi, Sunil Department of Computer Science Engineering Samrat Ashok Technological Institute Vidisha India Director & Professor Department of Computer Science Engineering Rungta College of Engineering & Technology Durg India

Heart disease is a critical concern of healthcare for everyone in today’s era. An effective and noninvasive indication of heart disease is an electrocardiogram (ECG). Understanding regular ECG signal patterns and comparisons with irregular, patterns may help to identify the serious nature of heart diseases such as arrhythmia. Comparison of ECG signal patterns is very difficult manually, and machine-based interpretation is a demand of society. Errors in ECG interpretation might result from noise contamination of ECG signals. ECG pretreatment from noise is therefore needed for precise analysis. This article proposed a novel deep learning-based solution for multichannel ECG noise reduction, through utilizing the capabilities of fully convolutional neural network along with the Jacobin regularization to ensure confining and preserving local information. Cascaded layered approach was framed in encoder and decoder sections of model where denoising and reconstruction process worked and compared on standard performance parameters with recent denoising deep autoencoders. This proposed work FCN-DAE with Jacobin regularization uses the noise stress test database (NSTDB) for the noise signal of ECG data sourced from the PhysioNet repository. The proposed model achieves 4.763 * 10–2 mv2 for signal space diversity (SSD), 0.288 mv for the median absolute deviation and 1.859 for the root mean square error (RMSE) in the conducted experiment. The experimental findings demonstrate that complex noise from the ECG signal may be removed up to 97.02%.

关键词： Deep learning

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Real-Time Sign Language Recognition System for Physically Challenged Community Using Deep Learning Technique 8th

Real-Time Sign Language Recognition System for Physically Ch...

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8th International Conference on Microelectronics Electromagnetics and Telecommunications, ICMEET 2023

作者： Devi, V. Anjana Bhuvaneswari, E. Tummala, Rama Krishna Professor Department of Computer Science and Engineering Rajalakshmi Institute of Technology Tamil Nadu Chennai India Associate Professor Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamil Nadu Chennai India Accenture Solutions Pvt. Ltd Chennai India

ISBN: (纸本)9789819784219

Language disparities have always existed in human civilization. In their community, deaf individuals use sign languages to communicate with one another, but it can be difficult to do so when interacting with the wider population. The project comprises a real-time sign language recognition system that enables hearing-impaired individuals and persons who do not know sign language to effortlessly communicate. The most used sign language in South Asian nations is Indian sign language. Convolution neural networks, specifically Tensor Flow object detection, are utilized to assess and train the model on the images that are used. The transfer training approach is used to train the deep learning model with various hand movements. Loss is anticipated to be decreased. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Comparative Evaluation of Deep Learning Models in Alzheimer’s Disease Diagnosis

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Procedia computer science 2025年 258卷 2352-2361页

作者： Leena Arya Yogesh Kumar Sharma Smitha Nayak Janaki Sivakumar Assistant Professor Computing Department Muscat College Oman Associate Professor Programme leader Computer security and forensics Department of Computer Science and Creative Technologies Global College of Engineering and Technology Muscat Oman

Alzheimer’s Disease (AD) is an ever-evolving neurodegenerative confusion that influences the neurons of the mind in a manner that hinders the patients’ ability to solve problems and remember issues and that requires timely and proper identification to enable appropriate management and treatment. Based on MRI medical images, deep learning models have become one of the most effective approaches for analyzing the signs of the early stages of AD. This research, therefore, carries out a comparative analysis of five deep learning models such as Convolutional Neural Networks (CNNs) like AlexNet, VGGNet, and ResNet, as well as Long Short-Term Memory (LSTM) networks and 3D Convolutional Neural Networks (3D-CNNs) in the finding of Alzheimer’s Sickness from medical imaging data. Each model’s arrangement parameters, for example, layer configurations, learning rate, batch size, optimizer, number of epochs, loss function, applying techniques for regularization, activation functions, and the input dimensions, using pre-trained weights, were carefully initialized to yield a proper comparison between the models. Diagnostic performances were evaluated utilizing precision, responsiveness(recall), specificity, and region under the receiver operating characteristic curve (AUC-ROC). ResNet outperformed other profound learning models in diagnosing Alzheimer’s Disease from MRI scans with a precision of 0.91 and AUC-ROC of 0.95. At the same time, VGGNet, AlexNet, LSTM, and 3D-CNNs demonstrated varying strengths and limitations in balancing accuracy, computational efficiency, and feature extraction.

关键词： Artificial Intelligence (AI) Cognitive Impairment Data Augmentation Diagnosis Accuracy Medical Imaging

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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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