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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Ramprasath, J. Saranya, N. Arun Kumar, M. Chandru Vignesh, C. Ramprakash, P. Krishnaraj, N. Dr. Mahalingam College of Engineering and Technology Department of Information Technology Pollachi India Sri Krishna College of Technology Department of Computer Science and Engineering Coimbatore India Sri Krishna College of Technology Department of Electronics and Communication Engineering Coimbatore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India Sri Krishna College of Technology Department of AI & DS Coimbatore India

ISBN: (纸本)9798331534950

Customer relationship management, called CRM, is essential for businesses looking to sustain profitable growth in today's fiercely competitive business environment. This essay examines cutting-edge methods to improve clients in the digital era and thoroughly explains CRM practices. The research examines best-serving customers through various touchpoints by integrating data analytics, ai, and personalized marketing strategies. In addition, it explores the role of organizational alignment and a customer-centric culture in developing enduring customer relationships. The study uses theoretical frameworks and practical data to highlight the main obstacles and changes associated with implementing efficient CRM procedures. As a result, synthesizing pertained research and case studies provides insights into new trends and industry best practices for building long-lasting relationships with customers. The results highlight the significance of proactive involvement, ongoing innovation, and ethicizing deliberations in fostering trust and allegiance across various consumer categories. This paper adds to the current conversation on CRM by providing practice advice that companies can use to adjust to and prosper in a constantly changing market. © 2024 IEEE.

关键词： Sales

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Utilization of Machine Learning in Breast Cancer Detection 25

Utilization of Machine Learning in Breast Cancer Detection

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Momani, Mutaz Yousef, Nidal Aljawazneh, Qotadeh Issa, A.T. Abuowaida, Suhaila Alazaidah, Raed Khouj, Mohammed Talat Batyha, Radwan Elsoud, Esraa Abu Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan Al-Albayt University Faculty of Information Technology Department of Computer Science Mafraq Jordan Zarqa University Department of Data Science and Ai Faculty of Information Technology Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Private University Faculty of It Applied Science Department of Computer Science Amman Jordan

ISBN: (纸本)9798331540012

Breast cancer is the most prevalent cancer among women and can be deadly, necessitating early detection to enhance patient outcomes and treatment effectiveness. Recently, Machine Learning (ml) techniques have shown potential in medical diagnostics, especially in breast cancer screening. ml algorithms examine extensive medical databases, including patient records, to identify subtle patterns and anomalies indicative of cancer. These algorithms excel at recognizing intricate patterns that may be missed by human analysis. Thereby, increasing the precision of breast cancer diagnoses. By evaluating and analyzing medical data, ml algorithms can detect minute anomalies, facilitating early identification of potential cancer cases. A significant advantage of ml-based breast cancer detection is its capacity for continuous learning and adaptation. In this study, we utilized 34 classifiers and five feature selection methods on three datasets. The first dataset comprises 286 instances with 10 integer-type features. The second dataset includes 699 instances with 10 integer-type features, and the third dataset consists of 286 instances with 13 integer-type features. For Dataset 1, Filtered Classifier and J-48 yielded the best results, while the Bayesnet classifier performed best on Dataset 2. In Dataset 3, SimpleLogistic and LMT achieved the best outcomes. Regarding feature selection, the study showed that the GainRatioAttributeEval, InfoGainAttributeEval, and CorrelationAttributeEval techniques are the best among the feature selection methods tested in the study. © 2024 IEEE.

关键词： Lung cancer

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Classification of Adversarial Attacks Using Ensemble Clustering Approach

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computers, Materials & Continua 2023年第2期74卷 2479-2498页

作者： Pongsakorn Tatongjai Tossapon Boongoen Natthakan Iam-On Nitin Naik Longzhi Yang Center of Excellence in AI&Emerging Technologies School of ITMae Fah Luang UniversityChiang RaiThailand Department of Computer Science Aberystwyth UniversityAberystwythUnited Kingdom School of Informatics and Digital Engineering Aston UniversityBirminghamUnited Kingdom Department of Computer and Information Sciences Northumbria UniversityNewcastleUnited Kingdom

As more business transactions and information services have been implemented via communication networks,both personal and organization assets encounter a higher risk of *** safeguard these,a perimeter defence likeNIDS(network-based intrusion detection system)can be effective for known *** has been a great deal of attention within the joint community of security and data science to improve machine-learning based NIDS such that it becomes more accurate for adversarial attacks,where obfuscation techniques are applied to disguise patterns of intrusive *** current research focuses on non-payload connections at the TCP(transmission control protocol)stack level that is applicable to different network *** contrary to the wrapper method introduced with the benchmark dataset,three new filter models are proposed to transform the feature space without knowledge of class *** ECT(ensemble clustering based transformation)techniques,i.e.,ECT-Subspace,ECT-Noise and ECT-Combined,are developed using the concept of ensemble clustering and three different ensemble generation strategies,i.e.,random feature subspace,feature noise injection and their *** on the empirical study with published dataset and four classification algorithms,new models usually outperform that original wrapper and other filter alternatives found in the *** is similarly summarized from the first experiment with basic classification of legitimate and direct attacks,and the second that focuses on recognizing obfuscated *** addition,analysis of algorithmic parameters,i.e.,ensemble size and level of noise,is provided as a guideline for a practical use.

关键词： Intrusion detection adversarial attack machine learning feature transformation ensemble clustering

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A Comprehensive Study on Disaster Tweet Classification on Social Media Information 6th

A Comprehensive Study on Disaster Tweet Classification on So...

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6th International Conference on Soft Computing and Signal Processing, ICSCSP 2023

作者： Dasari, Siva Krishna Srinivas, Gorla Prasad Reddy, P.V.G.D. AI Research Lab Department of Computer Science and Engineering GMR Institute of Technology Rajam Vizianagaram India GITAM Deemed To Be University Visakhapatnam India Andhra University Visakhapatnam India

ISBN: (纸本)9789819986279

Twitter is a popular social media platform where people share their opinions. The impact of these opinions plays a critical role when a sudden unexpected situation or any natural disaster may happen. In such a large volume of information, identifying or extracting informative tweets has become challenging in the present case. Many researchers focus on this area in the classification of informative tweets. In this work, we are presenting a comprehensive review based on different approaches. This work mainly focuses on other methods, namely machine learning, deep learning, and transfer learning, for identifying informative tweets on natural disasters. We also concentrate on separate datasets and their performance with other state-of-the-art models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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Advancing Marine Species Detection: A Comparative Analysis of YOLOv5 and YOLOv8 in Challenging Underwater Environment

Advancing Marine Species Detection: A Comparative Analysis o...

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Women in Computing (InCoWoCo), International Conference for

作者： R Bharath H U Ujwal K Chandan R C Ravindranath R Chandramma H R Rakshith Department of Computer Science and Engineering Global Academy of Technology Bengaluru Karnataka India Department of CSE(AI&ML) Global Academy of Technology Bengaluru Karnataka India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

This work offers a thorough comparison of the YOLOv5 and YOLOv8 object detection algorithms with a focus on the identification and categorization of ten different marine species. To address the urgent demand for sophisticated computer vision solutions in marine biology, we assess these models' performance, paying particular attention to their accuracy and effectiveness in difficult underwater settings. The chosen marine species are diverse in terms of size, shape, and color, providing a strong baseline against which to evaluate the models' performance. The investigation and results provide light on the advantages and disadvantages of the models by carefully examining detection metrics, false positives, and false negatives. The study offers helpful advice for scholars and practitioners working in marine biology and other related fields by providing concrete suggestions for model selection based on observed performance.

关键词： YOLO Measurement computer vision Shape Biological system modeling Computational modeling Color Fish Object recognition Dolphins

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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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Enhancing Medical Image-Based Diagnostics Through the Application of Convolutional Neural Networks Techniques 3

Enhancing Medical Image-Based Diagnostics Through the Applic...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Byeon, Haewon Nair, Rajit Mahalakshmi, V. Khalaf, Mohammed I. Kaushik, Bharat Shabaz, Mohammad Inje University Department Of Ai And Software Gimhae50834 Korea Republic of School Of Computing Science & Engineering Vit Bhopal University Bhopal India College Of Engineering And Computer Science Jazan University Department Of Computer Science Jazan45142 Saudi Arabia Al Maarif University College Department Of Computer Science Al Anbar 31001 Iraq Masters University Of Northern Iowa IA United States Model Institute Of Engineering And Technology J&K Jammu India

ISBN: (纸本)9798350318609

Perioperative nutrition evaluation is crucial for patient care and resource efficiency in healthcare. This work proposes a deep learning-based perioperative nutrition evaluation tool that leverages sophisticated machine learning to improve predictions and assist clinicians in making better choices. As surgical techniques become more complex and medical technology progresses, a thorough postoperative nutrition study becomes more important than ever. An extensive review demonstrates that the recommended strategy performs better than other machine learning techniques. Our model's accuracy, precision, memory, and F1-score let it predict surgical outcomes more accurately. The higher AVC-ROC and AVC-PR values of the suggested approach show that it performs particularly well. Its sensitivity-specificity balance is demonstrated by this. Our approach generates fewer errors, according to the confusion matrix analysis, which makes it reliable for healthcare decisions. Our work enhances critical healthcare by assessing nutrition with state-of-the-art techniques. © 2024 IEEE.

关键词： Diagnosis

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Fast and High-Quality Auto-Regressive Speech Synthesis via Speculative Decoding

Fast and High-Quality Auto-Regressive Speech Synthesis via S...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Bohan Wang, Hankun Zhang, Situo Guo, Yiwei Yu, Kai MoE Key Lab of Artificial Intelligence AI Institute X-LANCE Lab Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350368741

The auto-regressive (AR) architecture, exemplified by models such as GPT, is extensively utilized in modern Text-to-Speech (TTS) systems. However, it often leads to considerable inference delays, primarily due to the challenges associated with next-token prediction in long speech sequences. In this work, we introduce VADUSA, one of the first approaches to accelerate AR-based TTS through speculative decoding. Our findings demonstrate that VADUSA not only delivers a significant reduction in inference time but also enhances TTS quality by employing draft heads to predict future speech tokens in an auto-regressive manner. Additionally, the incorporation of a tolerance mechanism during the sampling process further boosts performance, yielding approximately a 3× speedup in AR TTS. Moreover, our approach exhibits strong generalization across diverse datasets and various speech token types. © 2025 IEEE.

关键词： inference speedup speculative decoding text-to-speech

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Traffic Congestion Detection from Surveillance Videos using Deep Learning

Traffic Congestion Detection from Surveillance Videos using ...

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computer, Electronics & Electrical engineering & their Applications (IC2E3), International Conference on

作者： G. Bindu Madhavi A. Durga Bhavani Y. Sowmya Reddy Ajmeera Kiran N. Thulasi Chitra Pundru Chandra Shaker Reddy Computer Science and Engineering (AI&ML) Geethanjali College of Engineering and Technology Hyderabad Telanagana India Computer Science and Engineering Anurag University Hyderabad Telangana India Computer Science and Engineering (AI&ML) CVR College of Engineering Hyderabad India Computer Science and Engineering MLR Institute of Technology Dundigal Hyderabad Telangana India Computer Science and Engineering Geethanjali College of Engineering and Technology Hyderabad India

Countless cameras, both public and private, have been installed in recent years for the objectives of surveillance, the monitoring of anomalous human activities, and traffic surveillance. Numerous worrisome and aberrant actions, such as theft, aggression, and accidents, make it difficult to notice and recognise such behaviour in a real-world setting. The topic of this study is car wrecks as depicted in online videos of traffic. Modern traffic monitoring and surveillance rely heavily on video traffic surveillance cameras (VTSS). Consequences of a rapidly expanding human population include a higher frequency of accidental injuries. The VTSS is employed to identify unusual occurrences on various roads and highways, such as traffic congestion and car accidents. When accidents happen on lengthy roadways or in remote areas, victims are often powerless and some don't make it. The purpose of this study is to provide a method for automatically identifying incidents in surveillance footage. Convolutional-neural-networks (CNNs), a specific deep learning approach developed to cope with grid-like data, have been shown to be useful in image and video processing, according to a study of the relevant literature. This study use a rolling prediction method and convolutional neural networks (CNNs) to detect accidents in VTSS footage. A dataset of anomalous photographs, called the Vehicle Accident Image Dataset (VaiD), was created and used in the training of the CNN model. The proposed method was put through its paces by analysing data gathered from running the trained CNN model on a number of different films. This study's findings demonstrate a 93% success rate in identifying traffic accident incidents in films from traffic surveillance systems.

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Integrating Grey Wolf Optimizer with U-Net for Precision Retinal Vessel Segmentation in Fundus Images 1st

Integrating Grey Wolf Optimizer with U-Net for Precision Ret...

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1st International Conference on Data engineering and Machine Intelligence, ICDEMI 2023

作者： Sengan, Sudhakar Lambture, Bhagyashree Lazar, Arokia Jesu Prabhu Benarji, Bejjam S. N. Bakkiyaraj, Jebaranjani Sharma, Dilip Kumar Lakineni, Prasanna Kumar Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627451 India Department of AI&DS AISSMS IOIT Pune411001 India Department of Computer Science and Engineering CMR Institute of Technology Hyderabad501401 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Information Technology V.S.B. College of Engineering Technical Campus Tamil Nadu Coimbatore642109 India Department of Mathematics Jaypee University of Engineering and Technology Madhya Pradesh Guna473226 India Department of Computer Science and Engineering Sri Venkateswara College of Engineering Andhra Pradesh Tirupathi517502 India

ISBN: (纸本)9789819776153

Retinal Vessel Segmentation (RVS) is pivotal in diagnosing and monitoring ocular diseases. While Deep Learning (DL) methodologies, particularly Convolutional Neural Networks (CNN), have revolutionized medical image analysis, achieving consistent and superior segmentation accuracy remains challenging. The conventional U-Net architecture, despite its merits, presents limitations in terms of adaptability and precision. This research article introduces a novel, "Hybrid U-Net Deep Neural Network" tailored for RVS in Fundus Images (FI). This approach integrates the Grey Wolf Optimizer (GWO) to fine-tune U-Net’s hyperparameters and elevate its segmentation accuracy. The GWO’s intrinsic ability to search for optimal solutions optimizes the U-Net’s performance. Comparative analyses on benchmark datasets, HRF and STARE, underscore this model’s superior segmentation results. Through rigorous quantitative and qualitative evaluations, the hybrid model significantly enhances RVS, positioning it as a promising tool for clinical and research applications in ophthalmology. This research demonstrates the potential of integrating optimization algorithms with Deep Learning (DL) and paves the system for future advancements in Medical Image Segmentation (MIS). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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