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Software Reliability Assessment Using Hybrid Neuro-Fuzzy Model

computer Systems science & engineering 2022年第6期41卷 891-902页

作者： Parul Gandhi Mohammad Zubair Khan Ravi Kumar Sharma Omar H.Alhazmi Surbhi Bhatia Chinmay Chakraborty Department of Computer Applications Manav Rachna International Institute of Research and StudiesFaridabad121006India Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinahSaudi Arabia Department of Computer Applications Chandigarh Group of CollegesLandran140307India College of Computer Sciences and Information Technology King Faisal UniversitySaudi Arabia Department of Electronics&Communication Engineering Birla Institute of TechnologyMesra JharkhandIndia

Software reliability is the primary concern of software developmentorganizations, and the exponentially increasing demand for reliable softwarerequires modeling techniques to be developed in the present era. Small unnoticeable drifts in the software can culminate into a disaster. Early removal of theseerrors helps the organization improve and enhance the software’s reliability andsave money, time, and effort. Many soft computing techniques are available toget solutions for critical problems but selecting the appropriate technique is abig challenge. This paper proposed an efficient algorithm that can be used forthe prediction of software reliability. The proposed algorithm is implementedusing a hybrid approach named Neuro-Fuzzy Inference System and has also beenapplied to test data. In this work, a comparison among different techniques of softcomputing has been performed. After testing and training the real time data withthe reliability prediction in terms of mean relative error and mean absolute relativeerror as 0.0060 and 0.0121, respectively, the claim has been verified. The resultsclaim that the proposed algorithm predicts attractive outcomes in terms of meanabsolute relative error plus mean relative error compared to the other existingmodels that justify the reliability prediction of the proposed model. Thus, thisnovel technique intends to make this model as simple as possible to improvethe software reliability.

关键词： Software quality reliability neural networks fuzzy logic neuro-fuzzy inference system

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Low Light Image Enhancement: A Special Click 2

Low Light Image Enhancement: A Special Click

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Kapoor, Shalini Arora, Yojna Bansal, Nidhi Virdi, Khushwant Ismail, Firdous Sadaf M. Malik, Suraj School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India Department of Computer Science and Engineering Sharda University Uttar Pradesh Greater Noida India Haryana Faridabad India Department of Computer Science and Applications School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India Nagpur Campus Pune India Department of Computer Science & Engineering IIMT University Uttar Pradesh Meerut India

ISBN: (纸本)9798331530389

Photography and images are an important and integral part of today's world, resulting in the development of multimedia and many other subjects comparable to image processing. The specifications of the camera and camera lens are just not enough hence image processing is always required. Low light photography, the problem gets even bigger because the camera lens fails to capture enough light and hence low light photography depends mostly upon image processing algorithms which help to reduce noise in the image, brighten the image for better overall visual quality and enlighten the hidden dark areas. Many efforts have been put into advancing image quality in little light, but none have given satisfactory results. This research report aims to analyses the techniques used for low-light image improvement and what advancements can be made to advance the image condition further. © 2025 IEEE.

关键词： Camera lenses

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AI-Driven Security Enhancements in Wireless Sensor Networks 2

AI-Driven Security Enhancements in Wireless Sensor Networks

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Singh, Kamred Udham Kumar, Ankit Varshney, Neeraj Raja, Linesh Singh, Teekam Dewangan, Amit Kumar School of Computing Graphic Era Hill University Dehradun India Guru Ghasidas Vishwavidyalaya Department of Information Technology Bilaspur India GLA University Department of Computer Engineering & Applications UP Mathura India Manipal University Jaipur Department of Computer Applications Jaipur India Graphic Era Deemed to be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350383652

Wireless sensor networks have garnered significant adoption as a technology for collecting and analyzing data from the environment. Their versatility and wide-ranging applications, encompassing critical military operations, forest fire detection, healthcare systems, and civilian uses, have made them a focal point of extensive research. The potential of WSNs to provide valuable insights and address various real-world challenges has spurred significant interest and exploration in the scientific community. Nevertheless, Wireless Sensor Networks (WSNs) are susceptible to security threats, especially in unattended environments. To address these threats, the popular Low-Energy Adaptive Clustering Hierarchy protocol is deployed with machine learning techniques. This paper makes an analysis of different machine learning algorithms with multiple class of parameters and evaluates it with criterion as such favorable for a WSN. The Random Forest technique, which allows for the classification of assaults such as blackhole attacks, grayhole attacks, TDMA attacks, and flooding attacks, is the most suitable algorithm for intrusion detection. It comprises six extracted characteristics. The accuracy score is 99.12% with prediction accuracy for attacks: 87%, 89%, 92% and 93% respectively. Unlike detecting the anomaly alone, this mechanism when given to a centralized management system become a smart governance model. This prevents the network from different intrusion methodologies and enhance the characteristics such as energy efficiency, network lifetime, throughput, performance and security issue of the wireless sensor network. © 2024 IEEE.

关键词： Random forests

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Assessing the Impact of Migration from SOA to Microservices Architecture

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SN computer science 2023年第5期4卷 577页

作者： Raj, Vinay Bhukya, Hanumanthu Department of Computer Applications National Institute of Technology Tiruchirappalli Tamil Nadu Tiruchirappalli 620015 India Department of Computer Science and Engineering (Networks) Kakatiya Institute of Technology and Science Telangana Warangal 506015 India

Microservices has become a buzzword in IT and large enterprise firms such as Netflix, Twitter, Spotify and others have started to design their applications by adopting this new architectural style. A few organizations have started migrating their traditional monolithic and SOA-based applications to microservices in order to benefit from the features of new style. Software architects, on the other hand, are in chaos whether to adopt this new style or not as they are unaware of the pros and cons of microservices architecture. Also, the impact of the migration on SOA-based applications in terms of performance and complexity is unknown, leading to dilemma on the migration process. In this paper, a study of the migration impact on the existing SOA based applications to microservices is presented. For this study, change propagation probability and architectural stability metrics are used to examine the effect of migrating a SOA-based application to microservices architecture. The proposed approach is illustrated on a case study application designed using SOA and then migrated to microservices. It is observed from the results that though the impact of migration is high, migrating to microservices has significant benefits and it is best suitable for large enterprise applications. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Change propagation Microservices Migration Service oriented architecture Stability

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Retinal Scan Denoising Using Generative Adversarial Networks: A Deep Convolutional Approach 5

Retinal Scan Denoising Using Generative Adversarial Networks...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Gowthami, G. Mano Shankari, J. Babu, Tina St. Francis de Sales College Department of Computer Applications Bengaluru India Alliance School of Advanced Computing Department of Computer Science and Engineering Alliance University Bengaluru India

ISBN: (纸本)9798331518097

Image denoising is needed if there is a need to remove noise while preserving details or structures in images in computing vision and image processing. Recent advancements in generative adversarial networks (GANs) and convolutional neural networks (CNNs) have only improved the application of denoising techniques in varying ways. These methods give clearer and more accurate images since these approaches are able to separate noise from image substance. One of the main use of application of the image denoising is that they enhance the images in media, movies and also in medical field where high quality of images are required for diagnosing the diseases. This paper examines the significance of image denoising and introduces a novel GAN-based retinal scan denoising technique that outperforms both CNN-based denoising and conventional Gaussian filtering, achieving higher performance (PSNR: 27). It outperforms other approaches with SCD of 10 dB, SSIM of 0.866 and MSE of 18.99. In this paper, we propose a GAN-based solution for denoising images and leveraging deep learning-based techniques for evaluating the quality of retinal scans and we demonstrate the potential of our approach for enhancing the quality of the images. © 2024 IEEE.

关键词： Generative adversarial networks

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An Ensembled Grid based Machine Learning Approach For PD Classification From MRI Images 4

An Ensembled Grid based Machine Learning Approach For PD Cla...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Redhya, M. Jayalakshmi, M. Kalasalingam Academy of Research and Education Department of Computer Applications Tamil Nadu India Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798350343670

Parkinson's disease (PD) is a neuro-degenerative disease caused due to breakdown of brain cells in the central-part of the nervous system. As symptoms of PD appear only after 60% or more of these cells are destroyed, early detection is a tedious task. Several studies have proven that PD can be effectively diagnosed from MR-Images (MRI). This paper provides an ensembled machine learning model, Stacked-EG model, for MRI-image based PD classification. The dataset was obtained from PPMI (Parkinson's Progression Markers Initiative) online repository which included 134 PD subjects (80 males and 54 females) and 126 healthy patients (60 males and 66 females) around 60 years old on average. Feature extraction is performed through the application of filters, including wavelet transformations and intensity-based enhancements. Feature selection is conducted using optimised XGBoost model's feature significance scores. For classification, a Stacking Classifier which combines the strengths of XGBoost, SVM, and a Random Forest classifier was used. The model obtained an accuracy of 98.41% in classifying PD from healthy controls. © 2024 IEEE.

关键词： GridSearch Optimization Magnetic Resonance Imaging (MRI) Random Forest Stacking SVM XGBoost

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Model-based recognition in robot vision for monitoring built environments

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Multimedia Tools and applications 2025年第12期84卷 10137-10154页

作者： Khan, Asif Varish, Naushad Pandey, Dhirendra Rizvi, Syed Qasim Afser Mehrotra, Shashi Parveen, Nikhat Department of Computer Applications Integral University Dasauli Bas-ha Kursi Road Uttar Pradesh Lucknow226026 India Hyderabad Campus Telangana Hyderabad502329 India Department of Information Technology Babasaheb Bhimrao Ambedkar University Lucknow Lucknow India Guangdong Guangzhou510006 China Department of Computer Science and Engineering Teerthanker Mahaveer University Uttar Pradesh Moradabad244001 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Gunur522302 India

In contrast to the machine, people have strong perception to determine the multiple objects easily in various environments under certain conditions. It is evident that the objects may exist in different orientations, scales and/or shapes, however it does not affect our ability to correct object recognition accuracy. The computer vision based object recognition is one of the most tedious tasks. So, objective is to design and development of the robust object recognition system to recognize the multiple objects effectively since different variations in the natural images are existed. In this paper, authors have presented comparative analysis and studies of some state of art object-recognition models for robot vision based problems, where the main goal is to recognize position, orientations and the identity of objects/parts, where parts have been taken from the industry. In the dynamic environment problem, the parts are recognized in a complex scenario. This paper presented based on the object representations using the recognition algorithms. Three steps are discussed and examined in details which are common to each and every category, namely, feature extraction, modeling, and matching. Test results confirmed that proposed model-based recognition in robot vision is fast and provides 93.00% accuracy. The proposed algorithm for object-recognition model has been compared with existing industrial part-recognition models and has, provided insights for progress toward future robot vision systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Object recognition

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Video Anomaly Detection Based on HSOE-FAST Modified Deep Neural Network

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SN computer science 2024年第5期5卷 588页

作者： Gupta, Anil Kumar Sharma, Rupak Ojha, Rudra Pratap Department of Computer Science and Engineering SRM Institute of Science and Technology NCR Campus Modinagar Uttar Pradesh Ghaziabad India Department of Computer Applications SRM Institute of Science and Technology NCR Campus Modinagar Uttar Pradesh Ghaziabad India Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and Management (GLBITM) (Affiliated to AKTU) Uttar Pradesh Greater Noida India

In recent times, video surveillance has become indispensable for public security, leveraging computer vision advancements to analyze and comprehend lengthy video feeds. Anomaly detection and classification stand out as crucial components of this technology. Anomaly detection's primary objective is to swiftly identify irregularities within a given timeframe. It is promising to use Deep Neural Network (DNN) approaches for anomaly detection because they combine the ideas of deep learning and reinforcement learning, enabling artificial agents to learn from and gain insights from real-world data. A modified DNN (Deep Neural Network) technique known as HSOE-FAST (Histo sigmoid of Orientation and Enthalpy with Fast Accelerated Segment Test) was proposed in this study. From the dataset, the input is obtained. Initially, the input is pre-processed using a Gaussian filter followed by the feature extraction using the HSOE-FAST method and finally classification is done using the modified DNN approach. Compared to other approaches, our suggested solution achieves an accuracy of almost 99% while overcoming the shortcomings of the existing methodologies. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： DNN Enthalpy Fast Accelerated Segment Test Histo sigmoid of Orientation Video anomaly detection

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Class Room Student Attentiveness Model based on YOLO 14

Class Room Student Attentiveness Model based on YOLO

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Nair, Rekha R. Babu, Tina Singh, Tripty Duraisamy, Prakash Mehra, Vishal Dayananda Sagar University School of Engineering Department of Computer Applications Bengaluru India Dayananda Sagar University School of Engineering Department of Computer Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India University of South Alabama Department of Computer Science United States

ISBN: (纸本)9798350335095

Online classroom discipline is a key concern for teachers now a days. The issue also attracts significant attention from the public and the media. In addition to this using mobile phone in class is one of the reasons for not paying the attention. For avoiding this issue, developed a model called "Class Room Attention Model". The proposed model collects the data using camera, and with the help of machine learning models automatically detects whether the student in the class is paying attention or not. The suggested method uses Tensor Flow to detect the mobile phone and media pipe for extracting the body features using the YOLO model. The model detects presence of mobile in the classroom in addition to the prediction of the students attentiveness. The proposed efficient and accurate model to identify the attention of a student in a class room with the random forest classifier provided an accuracy of 97.65 % for attention of student and 93.81% for mobile detection. © 2023 IEEE.

关键词： Students

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Colon Cancer Prediction with Transfer Learning and K-Means Clustering 7th

Colon Cancer Prediction with Transfer Learning and K-Means...

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7th International Conference on Emerging applications of Information Technology, EAIT 2022

作者： Babu, Tina Nair, Rekha R. Department of Computer Science and Engineering School of Engineering Dayananda Sagar University Karnataka Bengaluru India Department of Computer Applications School of Engineering Dayananda Sagar University Karnataka Bengaluru India

ISBN: (纸本)9789811951909

The automatic diagnosis of colon cancer is important for patients and their prognosis through the analysis of histopathological images. Traditional feature extraction methods extract low-level image features, and prior knowledge is required to choose meaningful features, which may be modified significantly by humans. Hence, unsupervised and supervised deep convolutions neural networks were utilized to analyze histopathological images of colon cancer. To overcome the impact of the unbalanced histopathology images in sub-classes, the balanced sub-classes are turned right and left, up and down, and rotated counter clockwise by 90∘ and 180∘. The proposed experimental findings for supervised histopathological image classification of colon cancer demonstrate that Inception_V3 and Inception ResNet_V2 outperform current algorithms. These findings suggest that the Inception_ResNet_V2 network is superior deep learning architecture for analyzing histopathological images for diagnosis colon cancers. As a result, in order to perform unsupervised image analysis, Inception_ResNet_V2 is utilized to extract features from colon cancer histopathology images. In addition, a new autoencoder network was created to convert the features collected by Inception_ResNet_V2 to a low-dimensional space for image clustering analysis. The test results demonstrate that the proposed autoencoder network outperforms the Inception_ResNet_V2 network in terms of clustering. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diseases

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