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Deep Reinforcement Learning for IoT-Based Smart Traffic Management Systems 7

Deep Reinforcement Learning for IoT-Based Smart Traffic Mana...

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

作者： Kansal, Vipashi Shnain, Ammar Hameed Deepak, A. Rana, Aditya Manjunatha Dixit, Krishna Kant Rajkumar, K. Varada 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 Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Tamil Nadu Chennai India Uttar Pradesh India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Gla University Department of Electrical Engineering Mathura India Mlr Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798350350067

The increasing complexity of urban traffic networks demands more intelligent and adaptive solutions for traffic management. This paper presents a novel approach utilizing Deep Reinforcement Learning (DRL) in conjunction with Internet of Things (IoT) technology to develop a smart traffic management system aimed at optimizing traffic flow in real-time. IoT devices, including sensors, cameras, and connected vehicles, provide comprehensive, high-resolution traffic data, which is processed by a DRL-based algorithm to dynamically control traffic signals and manage congestion. The proposed system continuously learns from evolving traffic patterns, adapting its decision-making to optimize key metrics such as vehicle throughput, travel time, and energy consumption. Compared to traditional traffic management systems, the DRL-based approach exhibits superior performance in handling dynamic traffic environments, reducing bottlenecks, and minimizing delays. Extensive simulations demonstrate that the integration of DRL with IoT enhances the system's ability to manage complex, real-world traffic scenarios more efficiently. This research contributes to the growing body of work in smart city development, offering an innovative framework for the deployment of intelligent traffic management systems. The study highlights the potential of DRL and IoT technologies to transform traffic management, promoting sustainable urban mobility and improving overall traffic efficiency.. © 2024 IEEE.

关键词： Travel time

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Incremental Learning with Pseudo Labels for Enhanced Glomeruli Segmentation Using SAM

Incremental Learning with Pseudo Labels for Enhanced Glomeru...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Liu, Xinkai Peng, Boyuan Zhu, Xin Iwasaki, Tsuyoshi Sato, Atsuya Kazama, Junichiro James Huang, Jie Graduate Department of Computer Science and Engineering The University of Aizu Fukushima Aizu-Wakamatsu Japan Department of AI Technology Development M&D Data Science Center Institute of Science Tokyo Tokyo Japan Department of Nephrology and Hypertension Fukushima Medical University Fukushima Japan Graduate Department of Computer Science and Engineering Division of Information Systems The University of Aizu Fukushima Aizu-Wakamatsu Japan

ISBN: (纸本)9798350350548

The advent of the Segment Anything Model (SAM) marks a significant milestone in deep learning semantic segmentation tasks. In this study, an incremental learning model based on pseudo labels is proposed. The model generates bounding box prompts for SAM, enhancing the accuracy of glomeruli segmentation in renal pathology images. The model uses DenseNet-121 along with contrastive learning to enable self-supervised pre-training using mouse glomeruli images. Transfer learning is then conducted using labeled human renal pathology images to develop a binary classification model for glomeruli. Unlabeled images of human renal pathology are subsequently introduced for incremental learning. Class activation maps (CAMs) generated by the incremental learning model are post-processed to extract bounding rectangles for connected regions. These rectangles are then used as prompts for SAM segmentation. The proposed approach resulted in an average Dice similarity coefficient (DSC) of 0.825 in the glomeruli segmentation task. This result demonstrates the effectiveness of the incremental learning model and post-processing algorithms in improving SAM’s segmentation accuracy for renal pathology. © 2024 IEEE.

关键词： Contrastive Learning

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Attention Based Generative Adversarial Networks to Augment Image Data for Rapid Training

Attention Based Generative Adversarial Networks to Augment I...

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International Conference on Parallel Distributed and Grid Computing (PDGC)

作者： Vijayakumar Adaickalam Sadagopan Selvarasu Gowthul Alam M A Suresh Kumar S Annamalai Sangeetha N School of Computer Science and Engineering and Information Presidency University Bangalore Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Chennai School of Computer Science and Engineering JAIN (Deemed-to-be University) Bangalore Department of AI & ML City Engineering College Bangalore

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

In this paper, we develop an Attention based Generative Adversarial Networks (AGAN) to augment image data for the purpose of robust training for efficient processing of the hyper spectral imaging. The AGAN model enables generation of images from the sample images that helps in training the classifier and in this study a fundamental classifier namely a convolutional neural network is used. A robust training is conducted to test the accuracy of detecting the instances effectively using the dataset. The simulation shows that the proposed AGAN-CNN attains improved accuracy after robust training than the existing methods.

关键词： Training Accuracy Training data Imaging Production computer architecture Generative adversarial networks Grid computing Convolutional neural networks Hyperspectral imaging

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Deep Learning-based Prediction of Parkinson's Disease Progression using Voice Recordings 7

Deep Learning-based Prediction of Parkinson's Disease Progre...

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Alagdeve, Vilas D. Thatipudi, Jayendra Gopal Roy, Priyanka Nithya, R. Anil Kumar, N. Ramesh, B. Yeshwantrao Chavan College of Engineering Department of Electronics Engineering Maharashtra Nagpur441110 India University of North Texas Department of Computer Science DentonTX76203 United States Vellore Institute of Technology Department of Electrical and Electronics Engineering Tamilnadu Vellore India Vivekanandha College of Engineering for Women Department of Computer Science and Engineering Tamilnadu Tiruchengode637205 India Mohan Babu University School of Engineering and Technology Department of Electronics and Communications Engineering Tirupati India St. Martin's Engineering College Department of Ai&ml Telangana Secunderabad Dhulapally500100 India

ISBN: (纸本)9798350341485

The main underlying factor behind Parkinson's Disease (PD) is the degeneration of brain cells responsible for producing dopamine, a neurotransmitter crucial for interconnecting brain cells. These dopamine-producing cells play a vital role in controlling adaptability, regulating movement, and facilitating fluency. The concept of active aging has emerged to enhance various aspects of fitness as individuals age, ultimately improving their overall quality of life. However, most efforts have concentrated on promoting normal aging, with limited attention given to the elderly population afflicted with chronic conditions like PD. This research focuses on the application of deep learning techniques for early detection and diagnosis of Parkinson's disease (PD). In the first experiment, this study utilizes the voice samples from healthy individuals and PD patients as a minimum training set for the Enhanced Deep Neural Network Model (EDNN) classifier, resulting in an optimal prediction model capable of identifying early onset PD. Subsequently, this study also investigates the effectiveness of transfer learning using the Convolutional Neural Network (CNN) architecture Alex Net for diagnosing Parkinson's disease from MR images in a second experiment. The results demonstrate the successful implementation of the EDNN classifier, showcasing its potential for early detection of Parkinson's disease, allowing for timely intervention and treatment. Moreover, by employing transfer learning with AlexNet, this study presents a novel approach for accurately diagnosing Parkinson's disease from MR images, representing a significant advancement in medical image analysis. © 2023 IEEE.

关键词： Neural network models

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Enhanced Movie Recommendation and Sentiment Analysis Model Achieved by Similarity Method through Cosine and Jaccard Similarity algorithms

Enhanced Movie Recommendation and Sentiment Analysis Model A...

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International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)

作者： Ulligaddala Srinivasarao R Karthikeyan Prakash Kumar Sarangi Bhawani Sankar Panigrahi Department of Computer Science and Engineering (AI & ML) Vardhaman College of Engineering Hyderabad India

ISBN: (纸本)9781665462013

Nowadays, with technology at the fore front of almost every business, there is an abundance of data and information that is difficult to sort through and understand. Consequently, a recommendation system is beneficial in dealing with such a big amount of information and filtering out the valuable material that is rapid also, pertinent to the client’s choice. In order to suggest films that are similar to the one the user has chosen, this paper describes a method for a movie recommendation system that uses different similarity approaches. In order to improve the user experience, this system performs sentiment analyses the reviews of the movie that was selected using optimization techniques. To increase accuracy and efficiency, a novel recommends-based sentiment classification technique that combines the Support Vector Machine Classifier with the Harris Hawks optimization method is utilised. The proposed approach is capable of handling high-dimensional data, according to empirical findings on the dataset (Movie). In terms of classification accuracy, we found that the suggested method performs better than the other recommendation systems strategies examined in this study. The trial findings revealed that the suggested strategy had a success rate of 97% accuracy.

关键词： Support vector machines Sentiment analysis Analytical models Films Optimization methods Motion pictures User experience

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Implementing Support Vector Machine Method for Predicting Urban Area Water Quality 1

Implementing Support Vector Machine Method for Predicting Ur...

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

作者： Al-Jawahry, Hassan M. Madhura, G.K. Ganti, Swathi Sheher Banu, S. Gajbhiye, Pratima The Islamic University of Al Diwaniyah Department of Computers Techniques Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India Dadi Institute of Engineering and Technology Anakapalli India Mvj College of Engineering Department of Electronics and Communication Engineering Bangalore India Lovely Professional University School of Chemical Engineering and Physical Science Phagwara India

ISBN: (纸本)9798350352931

Water quality failure is a long-standing global issue, causing illness, poisoning, spread of diseases, and claiming human lives in urban areas. Urban growth patterns have a substantial impact on stream water quality because they affect pollutant formation, buildup, and wash-off process. To successfully advise urban growth planning and policies, stream water quality must be evaluated and predicted in relation to various urban development patterns. In this work, Support Vector Machine (SVM) is implemented to predict quality of water in urban areas. Initially, data are collected through collected dataset, and the cleaning and normalization techniques are utilized to remove missing values and outliers and normalized the data. Then, SVM method is employed to effectively predict the quality of water. When compared to existing methods like genetic algorithm_extreme gradient boosting (GA_XGBoost), and artificial neural networks (ANNs), implemented SVM achieved high values of 0.953 of R 2,0.03of Root-mean-square error (RMSE), 0.01 of Mean Absolute Error (MAE), 9 of average invalidity percent (aiP), and 99 of average validity percent (AVP) performance result. © 2024 IEEE.

关键词： River pollution

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Predicting Falls Through Muscle Weakness from a Single Whole Body Image: A Multimodal Contrastive Learning Framework 3rd

Predicting Falls Through Muscle Weakness from a Single Who...

引用

3rd International Workshop on Applications of Medical Artificial Intelligence, AMai 2024 held in conjunction with the 27th International Conference on Medical Image Computing and computer Assisted Intervention, MICCai 2024

作者： Zhang, Xia Saleem, Afsah Ilyas, Zaid Suter, David Nadeem, Uzair Prince, Richard L. Zhu, Kun Lewis, Joshua R. Sim, Marc Gilani, Syed Zulqarnain Centre for AI and ML School of Science Edith Cowan University Joondalup Australia Nutrition and Health Innovation Research Institute Edith Cowan University Joondalup Australia Computer Science and Software Engineering The University of Western Australia Perth Australia Medical School The University of Western Australia Perth Australia Department of Endocrinology and Diabetes Sir Charles Gairdner Hospital PerthWA Australia

ISBN: (纸本)9783031820069

Falls are often attributed to poor muscle function, with weak hand grip strength clinically recognized as a major risk factor. However, grip strength is rarely assessed clinically. Low radiation dual-energy X-ray absorptiometry (DXA) whole-body scans, that can be obtained during routine osteoporosis screening, offer a comprehensive overview of body composition, thereby providing valuable information for musculoskeletal health. Here, we propose a machine learning technique, exploiting image and clinical data to classify weak grip strength ( © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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MolTRES: Improving Chemical Language Representation Learning for Molecular Property Prediction

MolTRES: Improving Chemical Language Representation Learning...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Park, Jun-Hyung Kim, Yeachan Lee, Mingyu Park, Hyuntae Lee, SangKeun Division of Language & AI Hankuk University of Foreign Studies Korea Republic of Department of Artificial Intelligence Korea University Korea Republic of Department of Computer Science and Engineering Korea University Korea Republic of

ISBN: (纸本)9798891761643

Chemical representation learning has gained increasing interest due to the limited availability of supervised data in fields such as drug and materials design. This interest particularly extends to chemical language representation learning, which involves pre-training Transformers on SMILES sequences - textual descriptors of molecules. Despite its success in molecular property prediction, current practices often lead to overfitting and limited scalability due to early convergence. In this paper, we introduce a novel chemical language representation learning framework, called MolTRES, to address these issues. MolTRES incorporates generator-discriminator training, allowing the model to learn from more challenging examples that require structural understanding. In addition, we enrich molecular representations by transferring knowledge from scientific literature by integrating external materials embedding. Experimental results show that our models outperform existing state-of-the-art models on popular molecular property prediction tasks. © 2024 Association for Computational Linguistics.

关键词： Prediction models

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Optimising Business Processes in Project Tracking using CRM 2

Optimising Business Processes in Project Tracking using CRM

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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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