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13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022

作者： Sujitha, R. Paramasivan, B. PSNA College of Engineering and Technology Department of Computer Science and Engineering India Department of Information Technology India

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

With the fast pace in collating big data healthcare framework, a streamlining of machine learning algorithms together with apache spark has been designed for effective classification of images and stages of lung cancer to the greatest extent. We experiment on a combination of binary classification (SVM-non linear SVM with Radial Basis Function RBF) and Multi-class classification (WTA-SVM winner-takes-all with support vector machine) with threshold technique (T-BMSVM) to classify nodules into malignant or benign nodules and also their malignancy levels respectively. We have argued for handling and processing large sizes of data sets as sputum cell images in the field of classification using the map-reduce framework in Pyspark, which works better with Apache spark. The outsourced outcomes reveal that classification works better with Multi-class SVM, therefore, may be considered as a promising tool to diagnose the stages of nodules. Also, Scalability and convergence analysis embed to prove the improving results of multi-class classification than SVM. © 2022 IEEE.

关键词： Classification (of information)

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Anomaly Detection in Image Processing using Artificial Intelligence

Anomaly Detection in Image Processing using Artificial Intel...

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2023 IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2023

作者： Jain, Swati Choudhary, Naveen College of Technology and Engineering Department of Computer Science and Engineering Udaipur India College of Technology and Engineering The Department of Computer Science and Engineering Udaipur India

ISBN: (纸本)9781665474924

Any method that looks for samples that deviate from anticipated patterns is sometimes referred to as anomaly detection. Many outlier finding methods are established on the types of irregularities, various types of data labels that are available, and the applications. The aim of this paper is to give an overview and systematic evaluation of outlier finding research. We believe that the paper contributes to a better understanding of the numerous fields in various types of research done on this subject. The approaches for anomaly detection have been separated into many groups. We discuss the pros and cons of each category and look at how computationally complex the various methods are in terms of real-world application domains. We concluded the paper by providing research gaps and constraints we faced while applying artificial intelligence algorithms in practical applications. This review paper examines several outlier detection and treatment techniques in images, as well as gives better performance of various AI approaches. © 2023 IEEE.

关键词： Anomaly detection

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Deep ensemble optimal classifier-based software defect prediction for early detection and quality assurance

Journal of Umm Al-Qura University for Engineering and Archit...

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Journal of Umm Al-Qura University for engineering and Architecture 2025年 1-12页

作者： Srinivas, Kalyanapu Janapati, Krishna Chaithanya Sai Kiran, Mungalched Sudheer Reddy, Davu Vinay, Thota Vamshi Yashwanth, Choula Department of Computer Science and Engineering Dhanekula Institute of Engineering and Technology Andhra Pradesh Vijayawada India Department of Electronics and Communication Engineering Vardhaman College of Engineering Telangana Hyderabad India Department of Computer Science and Engineering (Data Science) Malla Reddy Engineering College and Management Sciences Medchal Telangana Kistapur India

Software Defect Prediction (SDP) is critical in identifying fault-prone modules during the software development life cycle, enhancing software quality, and reducing maintenance costs. However, existing SDP models face significant challenges, including high false positive rates and severe class imbalance between defect-prone and non-defect modules, which hinder practical model training and accurate defect detection. To overcome these challenges, this study proposes a robust and efficient SDP architecture that integrates several advanced techniques to enhance predictive accuracy and model reliability. The methodology begins with comprehensive data preprocessing, incorporating normalization to standardize feature scales and outlier detection to eliminate noise and improve data quality. To address the inherent class imbalance in the dataset, the minority oversampling by synthetic data (MOSD) technique is employed to generate synthetic samples for the minority class, thus achieving a more balanced distribution. Following this, an Adaptive sequential K-best (ASKB) feature selection strategy is implemented to identify and retain the most relevant features, effectively reducing dimensionality while preserving critical information. Finally, the weighted random forest (WRF) classifier is utilized, which assigns appropriate weights to training instances, enabling improved classification performance, especially for minority class instances. The proposed methodology achieved an accuracy of 99.119%, a precision of 99.431%, a recall of 99.122%, and an F1-score of 99.333%. © The Author(s) 2025.

关键词： Adaptive sequential K-best Class imbalance Data preprocessing Feature selection Minority oversampling Predictive modeling Software defect prediction Synthetic data Weighted random forest

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IoT based deep learning approach for online fault diagnosis against cyber attacks

IoT based deep learning approach for online fault diagnosis ...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Felix, A. Yovan Sharmila, V. Devi, S. Nandhini Deena, S. Yadav, Ajay Singh Jeyalakshmi, K. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering R.M.D. Engineering College Tamil Nadu Kavaraipettai India Department of Computer Science and Engineering VSB Engineering College Tamil Nadu Karur India Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Tamil Nadu Chennai India Department of Mathematics SRM Institute of Science and Technology Delhi-NCR Campus Modinagar Uttar Pradesh Ghaziabad India Department of Physics PSNA College of Engineering and Technology Tamil Nadu Dindigul India

ISBN: (纸本)9781032669663

The Internet of Things (IoT) connects systems, software, cloud computing, activities and could provide new access points for cyber-attacks. Virus attacks and unauthorized downloading are the two main risks to IoT security at the moment. These dangers run the risk of obtaining private information, damaging their reputation and their finances. A frame is employed in this paper’s deep learning to identify threat avoidance in IoT. The cyber security detection systems following database has to be built, after which the benchmark variables are chosen and assessed in order to establish a cyber-security alert system in a large data set. The deep learning strategy to identifying threat avoidance was described for the simulation of this scenario. From the Mailing database, malware samples have been gathered for analysis. The research results demonstrate that the proposed method for assessing risks to cyber security in IoT provides highest accuracy to current approaches. Because of this, Deep Convolution Neural Network (DCNN) is utilized in IoT to detect attack avoidance. © 2024 The Author(s).

关键词： Internet of things

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Smart Swimming Pool Management System (SSPMS) using IoT

Smart Swimming Pool Management System (SSPMS) using IoT

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2023 IEEE International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2023

作者： Sangeetha, A. Krishnan, R. Santhana Kumar, G. Praveen Julie, E. Golden Ganesh, R. Karthik Robinson, Y. Harold PSNA College of Engineering and Technology Department of Information Technology Dindigul India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Tirunelveli India V.S.B Engineering College Department of Information Technology Karur India Anna University Department of Computer Science and Engineering Regional Campus Tirunelveli India SCAD College of Engineering and Technology Department of Computer Science and Engineering Tirunelveli India Francis Xavier Engineering College Department of Computer Science and Engineering Tirunelveli India

ISBN: (纸本)9798350397208

Swimming has become one of the major routines nowadays among people. People mostly use swimming pools for exercise or recreation. According to statistics one hour of swimming burns 30% more calories than one hour of running. So many people prefer swimming to keep their body fit. But few people use the swimming pool without having proper swimming skills. This leads to loss of their lives. Around the world nearly 42 people drown per hour. The pH level of water in the swimming pool should be between 7.4 to 7.6. The high pH water can cause rashes on human skin and scaling on pool equipment. Low pH causes eye irritation, skin irritation etc. Hence, a smart system is required to monitor the pH level of the water and to track the condition of the swimmers inside the pool. So with the support of few sensors and Arduino, an automatic swimming pool monitoring system, which is capable of sensing the pH level of the water available in swimming pool and condition of the swimmers is developed. The proposed system will also update all the observed status in the cloud with the support of the Internet facility. © 2023 IEEE.

关键词： Swimming pools

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Hyperparameter optimization: Classics, acceleration, online, multi-objective, and tools

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Mathematical Biosciences and engineering 2024年第6期21卷 6289-6335页

作者： Tan, Jia Mian Liao, Haoran Liu, Wei Fan, Changjun Huang, Jincai Liu, Zhong Yan, Junchi Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China College of Systems Engineering National University of Defense Technology Changsha China

Hyperparameter optimization (HPO) has been well-developed and evolved into a well-established research topic over the decades. With the success and wide application of deep learning, HPO has garnered increased attention, particularly within the realm of machine learning model training and inference. The primary objective is to mitigate the challenges associated with manual hyperparameter tuning, which can be ad-hoc, reliant on human expertise, and consequently hinders reproducibility while inflating deployment costs. Recognizing the growing significance of HPO, this paper surveyed classical HPO methods, approaches for accelerating the optimization process, HPO in an online setting (dynamic algorithm configuration, DAC), and when there is more than one objective to optimize (multi-objective HPO). Acceleration strategies were categorized into multi-fidelity, bandit-based, and early stopping;DAC algorithms encompassed gradient-based, population-based, and reinforcement learning-based methods;multi-objective HPO can be approached via scalarization, metaheuristics, and model-based algorithms tailored for multi-objective situation. A tabulated overview of popular frameworks and tools for HPO was provided, catering to the interests of practitioners. © 2024 the Author(s), licensee AIMS Press.

关键词： Deep neural networks

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Quadratic and Lagrange interpolation-based butterfly optimization algorithm for numerical optimization and engineering design problem

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Soft Computing 2025年第1期29卷 157-194页

作者： Sharma, Sushmita Saha, Apu Kumar Chakraborty, Sanjoy Deb, Suman Sahoo, Saroj Kumar Department of Computer Science and Engineering Indian Institute of Information Technology Tripura Agartala India Department of Computer Science and Engineering National Institute of Technology Tripura Agartala India Department of Mathematics National Institute of Technology Agartala Tripura Agartala India Department of Information Technology Bir Bikram Memorial College Tripura Agartala India

This work proposes a novel and improved Butterfly Optimization Algorithm (BOA), known as LQBOA, to solve BOA’s inherent limitations. The LQBOA uses Lagrange interpolation and simple quadratic interpolation techniques with adapted parameter settings to enhance the search strategy of BOA and to achieve a better balance of diversification and intensification. LQBOA’s performance was examined on 45 traditional benchmark issues as well as the IEEE CEC 2017 benchmark suites with 10, 30 and 50 dimensions, and the results were compared against popular state-of-the-art and modified algorithms. It was discovered that in more than 85% of cases, the proposed LQBOA outperformed the compared algorithms. The Friedman rank test and Wilcoxon rank test were used to validate the suggested LQBOA’s rank and significance. Additionally, convergence and diversity analyses were performed to investigate its convergence speed and searching behavior, respectively. Furthermore, LQBOA has been successfully deployed to solve twelve real-world issues including several engineering design problems and two multiple gravity assist spacecraft trajectory problems. The results of these problems were compared to those of a wide range of algorithms, demonstrating the superior performance of the proposed LQBOA. In conclusion, LQBOA makes a significant addition to the optimization area, and its application could greatly improve the performance of numerous optimization jobs. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Lagrange multipliers

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An Automated System Pre-Trained to Identify Cardiovascular Disorders from ECG Pictures 2

An Automated System Pre-Trained to Identify Cardiovascular D...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Bhaskar, Nuthanakanti Aelgani, Vivekanand Kumar, Sheo Devi, Bigul Sunitha Kumar, Voruganti Naresh Divya, G. CMR Technical Campus Department of Computer Science and Engineering Hyderabad India CMR College of Engineering & Technology Department of Computer Science and Engineering Hyderabad India CMR Engineering College Department of Computer Science and Engineering Hyderabad India CMR Institute of Technology Department of Computer Science and Engineering Hyderabad India CMR Technical Campus Department of Information Technology Hyderabad India

ISBN: (纸本)9798331519056

Globally, heart disorders, often recognized as cardiovascular diseases, are among the major causes of death. Additional lives might be protected the earlier they are identified and anticipated. Cardiovascular disease can be identified with an electrocardiogram (ECG), a simple, low-cost, non-invasive method of detecting the electrical functioning of the heart. Employing a public dataset of ECG images from heart patients, deep learning algorithms were utilized in this study to calculate the four main cardiac abnormalities: abnormal heartbeat, myocardial infarction history, coronary artery disease, and typical individual categories. Initially, the transfer method for learning was studied using the previously learned deep neural networks, VGG16, RESNET50, and InceptionV3. Classical methods of machine learning such as Support Vector Machine, K-nearest relatives, Random Forest, and Naïve Bayes were able to identify features employing the models that were already trained previously stated. For the study, ECG images from people who had or did not have a diagnosis of CVD were collected. Using these preprocessed pictures, relevant features including time-domain, frequency-domain, including nonlinear features were extracted. The collected features were then used to train various machine learning computations, which helped identify the prevalence of CVD. The efficiency of each algorithm was evaluated using metrics such as precision, specificity, and consciousness. In addition, the research employed Flask technology in Python to create an online user experience for the model, facilitating patients' and healthcare practitioners' easy access. ECG pictures can be uploaded through the interface, and the model analyzes them to determine whether CVD is present. These algorithms can also offer a reliable diagnosis, which lowers the possibility of human error. © 2024 IEEE.

关键词： Deep neural networks

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TIKI-TAKA Optimized Algorithn Based on an Enhanced Security System for Teleradiology in DICOM using Two-Level DWT Watermarking

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International Journal of COMADEM 2023年第3期26卷 47-54页

作者： Karthiyayini, S. Muthumari, A. Department of Information Technology Mohamed Sathak Engineering College Tamilnadu Kilakarai India Department of Computer Science and Engineering Syed Ammal Engineering College Tamilnadu Ramanathapuram India

Tele radiology plays a significant role in medicine that allows secure transmission of medical imaging data over a wide range of communication network. It guarantees data reliability and enables convenient way of communication for diagnostic purposes and clinical interpretation. However it raises the issue of legal, privacy, ethical issue and other copyright authenticity. Hence security and privacy remains to be a predominant problem in the medical field. To address this issue the present study has been motivated by the concept of watermarking schemes. Improved tele radiology architecture was developed in this study with efficient watermarking scheme that incorporates the integrated activity of biometrics and cryptography. In accordance to the entropy model, the fingerprint feature obtained from DICOM image is retrieved for key generation. 2L-DWT and TT-OA algorithm is employed to obtain high efficiencywith respect to PSNR (77 dB), 121s of encryption, SSIM (0.99), and embedding efficiency (97%). © 2023 COMADEM International. All rights reserved.

关键词： Discrete wavelet transforms

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Advancing Android Malware Detection with BioSentinel Neural Network using Hybrid Deep Learning Techniques 18

Advancing Android Malware Detection with BioSentinel Neural ...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Rani, D. Sandhya Gnaneshwar, K. Sampurnima Pattem, K. Sekhar, Soma Krishna, G. Bala Kakarla, Shirisha Cvr College of Engineering Department of Computer Science Engineering Rangareddy Ibrahimpatnam India Sreenidhi Institute of Science and Technology Department of Computer Science Engineering-Cyber Security Hyderabad India Vardhaman College of Engineering Department of Computer Science Engineering Hyderabad India

ISBN: (纸本)9789380544519

The paper introduces the BioSentinel Neural Network (BSNN), a novel hybrid deep learning model designed to enhance malware detection, particularly focusing on zero-day threats. The BSNN model integrates diverse neural network architectures, leveraging Graph Neural Networks (GNNs) for feature extraction and Transformers for sequential data analysis. It demonstrates significant improvements in accuracy (93.16%), precision (90.89%), recall (88.19%), and F1-score (90.45%) over traditional methods. The model exhibits a high detection rate of 88% for zero-day malware from 1000 samples tested and shows computational efficiency with an average inference time of 30 milliseconds. This research addresses critical gaps in current malware detection approaches, highlighting the potential of hybrid deep learning techniques in the cybersecurity domain. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Cybersecurity Deep Learning Hybrid Neural Networks Malware Detection Real-Time Analysis Zero-Day Threats

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