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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Balamanigandan, R. Mahaveerakannan, R. Saraswathi, S. Jenifer, A. Mary Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai602105 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Electronics and Communication Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331505745

The number of SMEs opting for cloud computing rises, efficient utilisation of the cloud has become a crucial issue because of the rental nature of the resources. Current approaches tend to be more traditional, targeting only individual components such as the amount of computing capacity, which may not adequately capture or mitigate the factors that define most actual cloud workloads and their unpredictable behaviour. In light of this, this article presents a new strategy that applies anomaly detection learning algorithms and particle swarm optimization to achieve the right cloud resource configuration at the least cost possible. The suggested idea works independent of a specific context in a loop fashion and distinguishes exceptional cases through an anomaly detection strategy in the loop process and at the same time learns the usage patterns. More importantly, it adapts to changes in system utilisation, as well as the pricing models offered by various cloud providers. Realistic costs as well as system data using the Microsoft Azure proved significant with highlighting 85% costs shaving within 10 months. This research supports the effectiveness of our integrated solution for solving multifaceted issues related to resource management in cloud environments for SMEs. © 2025 IEEE.

关键词： Resource allocation

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Deep Learning for Time Series Classification

Deep Learning for Time Series Classification

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Sharma R, Rajesh Sungheetha, Akey Sharma, Animesh Kumar Reddy, L. Chandra Sekhar Lakshmi, Bandaru Satya Kandavalli, Sunanda Ratna. School of Engineering Alliance University Department of Computer Science and Engineering Bangalore India The Icfai University Department of Mathematics Raipur India Medchal Hyderabad501401 India Aditya College of Engineering Department of Computer Science Engineering Andhra Pradesh Surampalem533437 India Sri Padmavati Mahila Visva Vidyalayam Department of Electronics and Communication Engineering Andhra Pradesh Tirupati517502 India

ISBN: (纸本)9798350375237

Time Series Classification (TSC) has been measured as the most demanding crisis in data mining. With the augment of temporal statistics accessibility, thousands of numerical algorithms have been planned since 2016 because of their ordinary temporal ordering;time series data can be found in all kinds of tasks that require some kind of human thinking process. In the recent past, time series categorization became one of the most important problems to be tackled because of various information-keeping categorization problems. Time series can be found in several applications of the present world from the health sector, to personal activity, and detection of anomalies. As temporal data has been made accessible in recent years many new zones are becoming very interested in time series applications and based on that newer numerical chain algorithms have been suggested. Many machines today can be performed without actually touching them and to serve this purpose this machine store a series of pictures that can analyze the user signals. Recognizing the correct signal from the set of pictures can be a time series classification problem. © 2024 IEEE.

关键词： Time series

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Spectrum Sensing and Management using Orthogonal Frequency Division Multiplexing based on Cognitive Radio Networks with Cooperative Spectrum Sensing 4

Spectrum Sensing and Management using Orthogonal Frequency D...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Hussein, Ramy Riad Vasanthakumar, G.U. Samala, Srinivas Pushpalakshmi, G. Rekha, E. College of Technical Engineering The Islamic University of Al Diwaniyah Department of Computers Techniques Engineering Al Diwaniyah Iraq Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electronics and Communication Engineering Sr University Warangal India Department of Mathematics New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Computer and Communication Engineering Gojan School of Buisness and Technology Chennai India

ISBN: (纸本)9798350352931

Nowadays, the fixed spectrum assignment policy restricts the availability of radio spectrum resources, hindering the growth of wireless applications, leading to spectrum scarcity, inefficiencies, and limitations on innovation. This is utilized by constraining full potential of wireless technologies and their associated benefits. However, the sensing system lacks in High computational complexity and energy consumption in spectrum sensing that leads to delays and inefficiencies in cognitive radio networks, limiting real-time adaptability and hindering seamless communication, which can compromise network performance and reliability. To overcome these drawbacks, a classification method is proposed using Orthogonal Frequency Division Multiplexing Based on The Cognitive Radio Networks with Cooperative Spectrum Sensing (OFDM based on CRN-CSS). Initially data is collected from RadioML2016 dataset, then the preprocessing is performed by the scarred convolution, random demodulator, random filtering and then compressive multiplexer. Then the features are selected using optimization method Ant Colony Optimization (ACO). Finally, the classification is performed using OFDM based on CRN-CSS whereas it enhances the adaptability to dynamic spectrum environments. The results prove that the model show better performance in terms of accuracy, precision and F1 score when compared to previous existing method of Particle Swarm Optimization (PSO). © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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A Review on Transformer Models: Applications, Taxonomies, Open Issues and Challenges 4

A Review on Transformer Models: Applications, Taxonomies, Op...

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Passi, Nidhi Raj, Manish Shelke, Nitin Arvind Bennett University School of Computer Science and Engineering Greater Noida India Keshav Mahavidyalaya University of Delhi Department of Computer Science India

ISBN: (纸本)9798350354218

With each passing generation, the human language is getting rich vocabulary usually effected by regional, domain specific and emotional dialects. Natural Language Processing (NLP) deals with training machines the human context of verbal or textual expressions. Over the years transformers have played a crucial role in this regard with substantial advancement. Sequence to sequence (Seq2Seq) models advancements with the incorporation of attention mechanism helped in grasping relationship between different sequences for better understanding. The shortcomings of the model further surfaced giving way to amendments like pretraining on extensive datasets and using varied attention mechanism bringing into forefront of research Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformers (GPT) models. Architectures based on Transformers have ventured strongly into the realm of generative modelling by classifying and understanding images-based data with models such as BERTvision, DALL-E and CLIP. Our analysis discusses the trajectory of Transformers. Current research endeavours emphasize on innovative designs, increased effectiveness, and expanded generalization capabilities. The discovery of Transformers represents a significant change in machine learning, since it enables models to understand intricate data across many modalities, hence improving the capabilities of artificial intelligence. © 2024 IEEE.

关键词： Distribution transformers

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Big Data Graph Node Importance Using Page Rank 1

Big Data Graph Node Importance Using Page Rank

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Roseline, R. Vasudevan, B. Clementking, A. Rani, S. Sivakumar, T. Srinivasan, C. PG Department of Computer Applications Cuddalore India SRM Institute of Science and Technology Department of Electronics and Communication Engineering Kattankulathur India Karnataka Bengaluru India Department of BCA/IT Tamil Nadu Chennai India Sri Eshwar College of Engineering Department of Computer Science and Engineering Tamil Nadu Coimbatore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376517

Improving the efficiency and accuracy of finding important nodes in large networks is the goal of enhancing Big Data graph node significance analysis using PageRank. To deal with the massive and complicated data sets seen in big data settings, it is necessary to modify and enhance the PageRank algorithm. The objective is to provide a more efficient framework that can speed up computation and improve the accuracy of significance ranking in large-scale networks. To control the algorithm's scalability, it is necessary to use state-of-the-art computational methods, such as parallel processing and effective data structures. The expected result is a scalable and reliable system that can quickly and correctly analyze large networks, yielding useful insights for applications like recommendation systems, online search, and social network analysis. The focus is on making PageRank more feasible for use in large data settings while also increasing its computing efficiency and scalability. The Adjacency Matrix of the Graph in a sample of 5 nodes has a value ranging from 15 to 55 in numbers, according to results from synthetic graph data. In another instance of the same dataset, the Initial PageRank Scores range from 0.2-0.15 in numbers, and in yet another instance, the final PageRank Scores for nodes range from 0.177 to 0.242 in numbers. © 2024 IEEE.

关键词： Data accuracy

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A Patient Response Prediction System using Multi-Scale Feature Fusion Semantic Segmentation Network and Med-YOLO 1

A Patient Response Prediction System using Multi-Scale Featu...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Shankari, S. Uma Ali, Hassan Mohamed Durga, Boya Vijaya Vadivu, M. Senthil Mangaiyarkarasi, V. Srm Institute of Science and Technology Ramapuram Department of Computer Science and Applications Chennai India College of Technical Engineering the Islamic University Department of Computers Techniques Engineering Najaf Iraq Maisammaguda School of Computer Science and Engineering Hyderabad India Sona College of Technology Department of Electronics and Communication Engineering Salem India New Prince Shri Bhavani College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350352931

Nowadays, most of the people are getting affected with various health issues such as typhoid, diabetes and cancer. A human immune system plays a crucial role in maintaining and balancing human health and reduces the risk of getting ill. Immunotherapy is a treatment which utilizes human body immunity power to fight cancer. There are many challenges in the existing models such as imbalance data, heterogenic data, uncertainties, and lack of temporal data. These challenges are resolved by imposing deep learning techniques and generating a valid information with the clinical details of patient health conditions. Therefore, a hybrid model name called Multi-Scale Feature Fusion Semantic Segmentation Network (MSSNet) and You Look Only Once (MedYOLO) for feature extraction and detection. Initially, Synthetic Minority Over-sampling Technique (SMOTE) is employed to overcome the issue of imbalanced data;further MSSNet is imposed for extracting the optimal features and detected using MedYOLO. Also, to get detailed information and generate cohort, the data collected from GSC hospital with overall details that are essential to predict the patient response to immunotherapy in cancer treatment. In the end, MSSNet-MedYOLO obtained higher accuracy of 98.16% for an effective prediction of patient response which is better than existing Convolutional Neural Network (CNN). © 2024 IEEE.

关键词： Semantic Segmentation

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Enhancing Disease Prediction through Random Forests in Healthcare Analytics 7

Enhancing Disease Prediction through Random Forests in Healt...

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7th International Conference on Contemporary Computing and informatics, IC3I 2024

作者： Kukreti, Sanjeev Al-Attabi, Kassem Chandrashekar, Rakesh Rani, K. Pushpa Badhoutiya, Arti Boob, Nandini Shirish Srivastava, Abhinav Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Gla University Department of Electrical Engineering Mathura India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Fs University Shikohabad Firozabad India

ISBN: (纸本)9798350350067

This research investigates the application of Random Forest algorithms to enhance disease prediction within healthcare analytics. Using large healthcare datasets, the research compares Random Forests to other machine learning algorithms, such as Neural Networks and Support Vector Machines, and assesses their ability to forecast different illnesses. According to the findings, Random Forests have the ability to reliably forecast diseases since they provide greater accuracy, precision, recall, and F1-score. In addition, healthcare providers may benefit greatly from the model's feature significance metrics, which help them understand which aspects have the most impact on illness outcomes. This study emphasises the revolutionary power of Random Forests to improve healthcare diagnoses, personalised treatment regimens, and patient care overall. © 2024 IEEE.

关键词： Disease Prediction Healthcare Analytics Machine Learning Predictive Performance Random Forests

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Hybrid Optimization for Enhanced Retinal Image Classification Using Neural Networks

Hybrid Optimization for Enhanced Retinal Image Classificatio...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Anwar Basha, H. Deepak, R. Babu, M. Bhanu, Shaik Thasleem Amutha, B. Madhavan, R. Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamilnadu Chennai India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Karnataka Bengaluru India Rajalakshmi Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Mathematics Tamilnadu Chennai India Jeppiaar Institute of Technology Department of Computer Science and Business Systems Tamilnadu Chennai India

ISBN: (纸本)9798331543617

The present study proposes a hybrid optimization algorithm that involves the integration of Neural Networks (NN), Genetic Algorithms(GA), and Particle Swarm Optimization(PSO) to improve the accuracy and efficiency of retinal image analysis. First, a wide range of features are extracted from retinal images and normalized. Then a NN is constructed with its weights and biases initialized to process these features. The fitness of each feature subset is measured with the help of a network's output, quantified by a Mean Squared Error(MSE)-based fitness function. Receiving fitness values, the GA component picks out the most important subsets of features, which are refined with crossover and mutation operations to retain diversity and improve the search process. In a parallel process, PSO dynamically adjusts the position of particles representing subsets of features through modification of velocity and position vectors under the influences of cognition and society. With the proposed integrated approach, iteratively, an optimal feature subset is generated by minimizing error and maximizing classification performance. Finally, after convergence, this last optimized subset of features is used in the final tuning of the NN to achieve a far-reaching improvement in the accuracy of classification. This method takes advantage of the complementary strengths of NN, GA, and PSO;therefore, it is able to provide an effective solution for the optimization of features within medical image analysis. © 2024 IEEE.

关键词： Ophthalmology

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Enhancing Blood Platelet Counting through Deep Learning Models for Advanced Diagnostics

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SN computer Science 2025年第1期6卷 1-10页

作者： Dev, Utkarsh Singh, Tripty Babu, Tina Mandal, Ashish Kumar Sharma, Mansi Mandal, Adhirath Department of Computer Science and Engineering Amrita School of Computing Amrita Viswa Vidyapeetham Karnataka Bengaluru 560035 India Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Karnataka Bengaluru 562106 India Amrita Hospital Faridabad India Graphic Era Deemed to be University Uttarakkand Dehradun India

Platelet counting is considered an essential factor for the diagnosis of blood clotting disorders as well as other illnesses as COVID-19 and Leukemia. Hemocytometer-based manual techniques are labor-intensive and prone to errors, especially in dynamic settings where platelet clumping develops over time. This work presents a novel method based on a collection of regression and object detection models- MobileNetv2, EfficientDetD0, and SSD MobileNet-that were trained on a large number of microscopic blood smear images. Multiple models have been trained and analysed to find out which models perform well for our dataset and problem. This study demonstrates how deep learning and object detection in blood platelet counting can revolutionize the field by increasing diagnostic precision and efficiency in healthcare systems. Out of all the models trained, we observed that EfficientDet D0 gave us the best performance, with a precision of 0.64, mAP of 0.96 and recall of 0.65. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blood cells EfficientDet MobileNet Platelets SSD SSD MobileNet

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Unsupervised Fuzzy Clustering-Based Vehicle Detection and Segmentation in Infrared Thermography 5th

Unsupervised Fuzzy Clustering-Based Vehicle Detection and Se...

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5th International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Ganesan, P. Leo Joseph, L.M.I. Sivakumar, V.G. Thulasi Prasad, S. Sathish, B.S. Sajiv, G. Department of Electronics and Communication Engineering Vidya Jyothi Institute of Technology Hyderabad India Department of Electronics and Communication Engineering SR University Telangana Warangal India Department of Computer Science Engineering AI Sri Venkateshwara College of Engineering Bengaluru India Saveetha School of Engineering SIMATS Chennai India

ISBN: (纸本)9789819718405

The identification of vehicles is indispensable task in this fast moving world where attentiveness is most important than ever before. The suggested methodology explains the effectiveness of fuzzy clustering algorithms for unsupervised vehicle recognition and segmentation in infrared thermography. The method takes use of the natural temperature variations that exist between moving objects and their environment, allowing for precise border delineation. Fuzzy clustering has shown to be a flexible method that offers a framework to organize pixels with comparable properties. By taking into account the uncertainty present in thermal imaging, fuzzy clustering presents a viable solution in the context of vehicle recognition and segmentation. Infrared thermography is more effective in challenging scenarios such as darkness, smoke, or dust. It is also well-suited for real-time scenarios due to fast thermal response. The proposed methodology finds applications in transportation management, security systems, industrial monitoring, and various thermal inspections. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fuzzy clustering

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