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2024 International Conference on Communication, computer Sciences and engineering, IC3SE 2024

作者： Sriprasadh, K. Palit, Shamik Pravallika, B. Manjunatha Lenka, Reena Singla, Atul Srm Institute Of Science And Technology Faculty Of Science And Humanities Department Of Computer Science And Applications Chennai Vadapalani India University Of Stirling Rak Campus Department Of Computing Science And Software Engineering United Arab Emirates Institute Of Aeronautical Engineering Dundigal Hyderabad India New Horizon College Of Engineering Department Of Mechanical Engineering Bangalore India University Pune India Lovely Professional University Phagwara India

ISBN: (纸本)9798350366846

A machine learning-based e-commerce personalised recommendation system helps address the issue of information overload that inevitably arises when consumers have more and more options in e-commerce, leading to an increasingly complex structure. Customer segmentation is a crucial component of contemporary marketing strategy since it enables companies to effectively adjust their advertising campaigns and customise their communications with customers. Through the analysis of enormous quantities of consumer behaviour data and the discovery of trends that can be utilised for dividing customers into segments, ML algorithms provide a potent tool for automating the procedure of customer segmentation. We report our research and results for identifying a customer's segment. In order to perform machine learning, we integrate the Python Tensorflow library with Pandas for manipulating data frames. To address any discrepancies or gaps in the collected client data, it is refined. Key features are found, and to find hidden patterns and relationships, an exploratory analysis of the data is done. After that, we decide on a suitable machine learning technique to divide the clientele into various groups. We also experiment with several clustering models, including conventional machine learning techniques. © 2024 IEEE.

关键词： Python

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Neural Networks and Deep Learning: Innovations and applications in Soft Computing 7

Neural Networks and Deep Learning: Innovations and Applicati...

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

作者： Abdulkadar, Jahagirdar Imran Mohan, Chinnem Rama Bharati, P. Vijaya Palit, Shamik Rao, Gunda Srinivasa Mudholkar, Pankaj Savitribai Phule Pune University India Narayana Engineering College Department of Computer Science and Engineering Andhra Pradesh Nellore524004 India Vignans Institute of Engineering for Women Department of Cse Andhra Pradesh Visakhapatnam530049 India University of Stirling Department of Computing Science and Software Engineering RAK Campus United Arab Emirates Mallareddy University School of Management and Commerce Kompally500100 India Marwadi University Faculty of Computer Applications Gujarat Rajkot360003 India

ISBN: (纸本)9798350350067

The Paper explores different aspects of deep learning techniques and neural networks in the fields of healthcare, time-series forecasting, agriculture, and other relevant sectors through soft computing. The objective of the report is to examine the significant potential and applications of neural networks. While machine learning techniques called neural networks (NN) are utilized for audio, picture, and the processing of natural languages, it has huge implications in robotics where several machine learning algorithms are significantly required. Improvements in deep machine learning are also investigated in the article specifically in the fields of drug construction, genetics, recognizing faces, farming, biological medicine, biological informatics, medical treatment, natural language, multimedia analysis of data, and mobility prediction. However black box challenges and privacy concerns with data are often ignored in this research. The paper also outlines deep learning-driven data mining techniques for precisely predicting, displaying, evaluating, and classifying data. The report has also emphasized on addressing the potential privacy concerns and challenges associated to information representation. By reviewing recent literature sources about neural network techniques, the study proposes innovation strategies and potential improvement areas for the development of advanced technologies like deep learning and neural networks while addressing the key privacy concerns in data processing. © 2024 IEEE.

关键词： Adversarial machine learning

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Deep Learning-Based Approaches for Accurate Brain Tumor Detection in MRI Images

Deep Learning-Based Approaches for Accurate Brain Tumor Dete...

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2024 International Conference on Decision Aid Sciences and applications, DASA 2024

作者： Noori, Mohammed Ahmed, Mohsin Ibrahim, Amer Saeed, Ali Khishe, Mohammad Mahfuri, Mahmoud Al-ma'Moon University College Baghdad Iraq College of Information Technology Department of Computer Science and Software Engineering United Arab Emirates Northern Technical University Computer Center Ninevah Iraq Yuan Ze University Innovation Center for Artificial Intelligence Applications Taiwan Jadara Research Center Jadara University Jordan International University for Science and Technology Computer and Information Technology Department Syria

ISBN: (纸本)9798350369106

Brain tumor classification is considered one of the major tasks in medical image analysis, where correct and timely diagnosis could be achieved to serve as the key to effective treatment. This research paper proposes a deep learning-based approach for automatic classification of brain tumors from MRI images by fine tuning the ResNet50V2 CNN model. The dataset is made up of four classes, namely: Glioma Tumor, Meningioma Tumor, No Tumor, and Pituitary Tumor, totaling 2,844 images. We have done some strategy data augmentation and class weighting in order to prevent class imbalance, which ensures good performance in all tumor classes. Pre-processing and augmentation of the input images are done via rotation, shifting, zooming, and flipping. It yielded 95.29% on the validation set, with the major highlights of performance metrics for the minority class being class precision equal to 0.97 and class recall equal to 0.92, hence proving the efficiency of performed class balancing. The range of F1-scores, from 0.93 to 0.97, means fantastic predictive capability across all tumor types. Other techniques were also used to make this model optimal, such as early stopping, learning rate reduction, and checkpointing. The high performance of this model shows great promise in helping clinicians toward the right diagnosis of brain tumors in an effective way. Future efforts will involve incorporating larger datasets, exploring advanced augmentation techniques to enhance further model generalizability. Model explainability tools such as Grad-CAM will be used to extract insight into the model's decision-making process. This enhances the clinical interpretability of the results. © 2024 IEEE.

关键词： Convolutional neural networks

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EEG Classification Using Reinforcement Learning and Swarm-Based Deep Sparse Autoencoder 5

EEG Classification Using Reinforcement Learning and Swarm-Ba...

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5th International Conference on Inventive Research in Computing applications, ICIRCA 2023

作者： Rekha, V. Punitha, T Santhi Sethuraman, R. Naveena, N. Walid, Md. Abul Ala Srivastava, Aviral Agurchand Manmull Jain College Department of B.C.A. Chennai India Agurchand Manmull Jain College Department of Software Applications India Sathyabama Institute of Science and Technology Department of Cse Chennai India Kongu Engineering College Computer Technology-UG India Department of Computer Science and Engineering Bangladesh Amity University Rajasthan Department of Cse Jaipur300202 India

ISBN: (纸本)9798350321425

Analysis of electroencephalographic (EEG) data has shown to be a useful tool in the fields of Brain-computer interfaces (BCIs), and even business. Recently, deep learning architectures have been implemented, particularly in the interpretation of EEG readings and the knowledge it may impart about mental processes, thanks to the availability of huge EEG data sets and advancements in deep learning. In order to find useful data for brain classification and neuroimaging, Various Analytical Techniques utilized in EEG investigations have turned to machine learning. This research study combines sparse auto-encoders (SAEs) with a post-processing system, which is comprised of a linear system model dependent on the Particle Swarm Optimization (PSO) method as a unique data categorization framework. To boost classification precision, it is to prune datasets of superfluous characteristics. The Q-Learning method is used to built on reinforcement learning to reduce the overall number of features and have obtained the resulting accuracy. Evidence of this strategy leading to better classification performance is shown by experiments on both simulated and actual data. The PSO method provides a metaheuristic strategy for estimating the linear model's parameters. Using publicly available datasets, the suggested methodology is verified;When contrasted to current best practices, the results are favorable. With some adjustments to the input characteristics, hidden neurons, and output classes, the system may be utilized to address any data categorization problem. © 2023 IEEE.

关键词： Reinforcement learning

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Objective Full-Reference Image Assessment Metrics for Estimating the Quality of Remote Sensing Images 2

Objective Full-Reference Image Assessment Metrics for Estima...

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2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Maruthi, R. Anusha, P. Sankar, P. Thiyagaragan, K. Hindustan Institute of Technology and Science Department of Computer Applications Tamil Nadu Chennai India Periyar Maniammai Institute of Science and Technology Department of Software Engineering Thanjavur India Hindustan Institute of Technology and Science Department of Electronics and Communication Engg Tamil Nadu Chennai India

ISBN: (纸本)9798350397635

Image Quality (IQ) assessment is a very complex task and it is extremely important to evaluate the images with the metrics. The metrics applied can be a full reference, partial reference or no-reference metric and it depends on the application and availability of the ground truth. Most of the IQ metrics are developed by considering the Visual System (VS) of humans. The assessment methods studied in this paper focuses on some of the Full-Reference (FR) measures and it is used to estimate the remote sensing noisy images. The effectiveness of the measures demonstrates a considerable outcome and demonstrates how well the noisy remote sensing images are being quantified. © 2023 IEEE.

关键词： Image quality

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Deep Learning Based Intrusion Detection for IoT Networks

Deep Learning Based Intrusion Detection for IoT Networks

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： P. Ananthi K. Nirmaladevi Mothika G Department of Computer Technology-PG Kongu Engineering College Erode Tamilnadu Department of Computer Applications Madanapalle Institute of Technology &Science MSc Software Systems Kongu Engineering College Erode India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Intrusion detection is crucial for securing IoT networks amid the rapid proliferation of devices, posing significant security challenges. This study offers a unique intrusion detection model for IoT deep learning methods used in networks include Convolutional Neural Networks (CNN), TabNet, and the Transient Search Optimization (TSO) method. The project starts by developing and evaluating distinct intrusion detection components. CNN, specializing in feature extraction, analyzes network traffic for potential intrusions. TabNet, known for interpretability and effectiveness with structured data, captures intricate relationships. The novel TSO algorithm improves the system's ability to detect temporary anomalies. Each component's performance is assessed individually, highlighting unique strengths. TabNet excels in structured IoT data pattern identification, surpassing CNN. The TSO algorithm strengthens temporary anomaly recognition, enhancing IoT network security. An ensemble technique combines CNN and TabNet predictions, reducing false positives and negatives to elevate overall detection accuracy. The ensemble, using both CNN and TabNet, achieves an impressive accuracy rate exceeding 99%, demonstrating significant gains in intrusion detection performance.

关键词： Deep learning Accuracy Intrusion detection Telecommunication traffic Prediction algorithms Ubiquitous computing Convolutional neural networks Internet of Things Transient analysis Optimization

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An Analysis of YOLOvX Deep Learning Models for Colon Cancer Detection

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Procedia computer Science 2025年 260卷 81-90页

作者： Raseena T.P. Aditya Kumar Soni Jitendra Kumar T. Akilan Ashutosh Kumar Singh Department of Computer Applications National Institute of Technology Tiruchirappalli 620015 Tamilnadu India Kotak Mahindra Bank Thane West Mumbai 400604 Maharastra India Department of Mathematics Bioinformatics and Computer Applications Maulana Azad National Institute of Technology Bhopal 462003 M.P. India Department of Software Engineering Lakehead University Thunder Bay Canada Department of Computer Science and Engineering Indian Institute of Information Technology Bhopal India

Around the world, Colorectal cancer is still a prevalent form of cancer, and effective treatment and its impact depends on early diagnosis. Recently, polyp detection methods employ a convolutional neural networks to identify precancerous or malignant polyps in colonoscopy images accurately with high speed and precision. This study evaluates four deep learning models namely YOLOv3n, YOLOv5s, YOLOv7, and YOLOv7x, to determine their effectiveness in detecting colorectal cancer polyp frames efficiently. Among these, YOLOv7x model exhibited outstanding performance on the Hyper Kavasir dataset compared to other models, achieving an F1 score of 88.0%, a recall of 86.4%, a precision of 89.5%, and a mAP of 92.0% at a confidence threshold of 0.353. The achieved results highlight the unique capability of models in detecting CRC polyps for diagnosis. Incorporating such detection models can help physicians significantly in clinical practice to improve their ability to identify malignant polyps, ultimately leading to better treatment for patients. Gastroenterologists can benefit considerably from these models as diagnostic tools because of their speed and accuracy.

关键词： Colorectal cancer Colonoscopy Deep learning YOLO

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Experimental Evaluation of Cross-Platform Recognition of Identical and Unknown Users over Social Networking Environment

Experimental Evaluation of Cross-Platform Recognition of Ide...

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

作者： Nagababu, Kancharla Ananthi, S. Sunheriya, Neeraj Venkata Siva Prasad, Ch. Jayanthi, J. Fawareh, Hamed Aditya University Department of Computer Applications Surampalem India St. Joseph's College of Engineering Department of Cse Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Jeppiaar Engineering College Department of Ece Rajiv Gandhi salai Chennai119 India Sona College of Technology Department of Cse Salem India Zarqa University Faculty of Information Technology Software Engineering Dept Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

The capacity to individually identify and categorize people across sites and apps is vital for trustworthiness, fraud prevention, and personalized experiences in online social networking. In this study, strategies for platform identification among similar and unknown social networking users are evaluated. We study strategies that employ user attributes, behavior history, and inter-network connections to match profiles on multiple platforms and uncover new, previously connected people. This cross-validation on the proposed method uses Social Linkage Identification Strategy (SLIS) and Friend Relationship Evaluator. According to quantitative experiment results, our approach improves search people across multiple social networks more than FRE. The extensive social connection web in many online social networks is used to enhance user identification and verification via the Friend Relationship Evaluator (FRE) system. Our process uses advanced machine learning and data fusion to handle a wide variety of natural ventilation phenomena from different sensors and platform formats. We evaluate attribute-based matching, graph approaches, and behavior analytics for accuracy, scalability, and computing cost. This study advances user identification systems across digital platforms and helps service providers and end-users establish more sophisticated online identity management strategies. © 2024 IEEE.

关键词： Anonymous Identical Users FRE Identity Resolution SLIS Social Media Network User Identification

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SmartEdge: Smart Healthcare End-to-End Integrated Edge and Cloud Computing System for Diabetes Prediction Enabled by Ensemble Machine Learning 15

SmartEdge: Smart Healthcare End-to-End Integrated Edge and C...

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15th IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2024

作者： Hennebelle, Alain Dieng, Qifan Ismail, Leila Buyya, Rajkumar Independent Researcher in Information Technology Systems and Artificial Intelligence Applications Abu Dhabi United Arab Emirates Lab School of Computing and Information Systems University of Melbourne Melbourne Australia Lab Department of Computer Science and Software Engineering Al-Ain Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331507589

The Internet of Things (IoT) revolutionizes smart city domains such as healthcare, transportation, industry, and education. The Internet of Medical Things (IoMT) is gaining prominence, particularly in smart hospitals and Remote Patient Monitoring (RPM). The vast volume of data generated by IoMT devices should be analyzed in real-time for health surveillance, prognosis, and prediction of diseases. Current approaches relying on Cloud computing to provide the necessary computing and storage capabilities do not scale for these latency-sensitive applications. Edge computing emerges as a solution by bringing cloud services closer to IoMT devices. This paper introduces SmartEdge, an AI-powered smart healthcare end-to-end integrated edge and cloud computing system for diabetes prediction. This work addresses latency concerns and demonstrates the efficacy of edge resources in healthcare applications within an end-to-end system. The system leverages various risk factors for diabetes prediction. We propose an Edge and Cloud-enabled framework to deploy the proposed diabetes prediction models on various configurations using edge nodes and main cloud servers. Performance metrics are evaluated using, latency, accuracy, and response time. By using ensemble machine learning voting algorithms we can improve the prediction accuracy by 5% versus a single model prediction. © 2024 IEEE.

关键词： Electronic health record

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A New Hybrid Model for Brain Tumor Recognition in MRI Images Based on Hand-Crafted Features and Deep Learning

A New Hybrid Model for Brain Tumor Recognition in MRI Images...

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Digital Signal Processing and its applications (DSPA), International Conference on

作者： Yasser Nizamli Weaam Fadel Anton Filatov Yulia Shichkina Department of Software Engineering and Computer Applications Saint Petersburg Electrotechnical University Saint Petersburg Russian Federation Department of Computer Science Saint Petersburg Electrotechnical University Saint Petersburg Russian Federation

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

Brain tumors are among the most life-threatening cancers that can affect people of any gender or age. MRI scans manually evaluated by an expert are the initial diagnostic procedure that precedes an appropriate treatment plan. However, the human factor involves the possibility of error, and the process itself can be time-consuming and resourceconsuming, ultimately putting patients’ lives at risk. Therefore, efforts are being made to automate the diagnostic process through computer-aided systems, of which artificial intelligence has become the basis. This paper presents a novel system for detecting brain abnormalities in MRI images that ensures high precision without compromising the simplicity of its operational structure. In the proposed model, numerical features are extracted from grayscale MRI images using the rectangular histogram of oriented gradients method. The resulting features are fed after dimensionality reduction to a specialized sequential deep network composed of 11 learnable layers. The classification accuracy of the model reached 99.51%, outperforming many analogues, making it a promising option for supporting the diagnostic decision-making process.

关键词： Deep learning Histograms Image recognition Magnetic resonance imaging Decision making Brain tumors Feature extraction Brain modeling Numerical models Testing

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