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Enhancing Citywide Flow Forecasting through deep Learning and Spatial Temporal Learning 2

Enhancing Citywide Flow Forecasting through Deep Learning an...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Parvathi, r. Sarkar, Swagata Supraja, M. Swagatha, J.P. Kanagajothi, d. Srimathi, S. SRM Arts and Science College Computer Science Kattankulathur India Sri Sairam Engineering College Department of Artificial Intelligence and Data Science Chennai India Rajarajeshwari College of Engineering Electronics and Communication Engineering Bangalore India Panimalar Engineering College Department of IT Poonamallee India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Mathematics India SIMATS Technology Saveetha School of Engineering Department of Bio Chennai India

ISBN: (纸本)9798350317558

Safe traffic management requires citywide flow projections. Transportation, public safety, and municipal planning are substantially affected. It forecasts city intake and outflow using flow data. Traditional methods are inaccurate and less scalable in complex, dynamic metropolitan settings. This study uses deep learning to predict crow traffic. deep learning can handle complicated nonlinear circumstances, making neural network crowd flow prediction more popular. However, deep learning has solved this problem, but it requires plenty of data. data shortages may develop when a city's service or infrastructure is installed. This research introduces a deep spatial temporal transfer learning system that predicts crowd movement in a data-sparse target city using data from a data-rich source city. Geographic and temporal patterns in crow traffic data may be captured by trained algorithms, enhancing prediction accuracy. We found that our deep learning-based solution is more accurate and robust than prior methods. Crow migration patterns are properly predicted by this research, improving urban planning and management. © 2024 IEEE.

关键词： Learning systems

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revolutionizing Cardiovascular Attack Prediction: A Comprehensive Machine Learning Approach for Accurate and Timely detection 4

Revolutionizing Cardiovascular Attack Prediction: A Comprehe...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： durai, S. Jaganathan, d. Prabhu, V. Harini, r. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Dept. of CSE Chennai India Madanapalle Institute of Technology & Science Department of Artificial Intelligence Andhra Pradesh India R.M.K. Engineering College Department of Computer Science and Engineering Thiruvallur Kavaraipettai India vel Tech Multi Tech Dr.Rangarajan Dr. Sakunthala Engineering College Artificial Intelligence & Data Science Chennai India

ISBN: (纸本)9798350385793

Cardiac attacks are a leading cause of mortality worldwide, underscoring the critical need for timely and accurate healthcare detection. Predicting cardiovascular attacks is a formidable challenge in clinical data analysis. Machine Learning (ML) proves to be a valuable tool for analyzing vast healthcare data. This innovative approach focuses on employing ML techniques to identify significant features, thereby improving the accuracy of cardiovascular attack prediction. A variety of characteristics and well-known classifiers, including Naïve Bayes (NB), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), random Forest (rF), decision Tree (dT), and Logistic regression (Lr), are used to create the final prediction model. Through collaborative efforts, these algorithms pave the way for binary classification, effectively distinguishing between true and false outcomes. By analyzing their accuracies, this approach enables the possibilities for successful predictions. © 2024 IEEE.

关键词： Adversarial machine learning

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Leveraging Machine Learning for Predictive Maintenance in Supply Chain Management Systems

Leveraging Machine Learning for Predictive Maintenance in Su...

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2024 International Conference on Science, technology, Engineering and Management, ICSTEM 2024

作者： Sharan, Bediga Vijai, C. Srivastava, Kingshuk Singla, Atul Bhati, Indira Kumar, T.Ch. Anil School of CS & AI SR University Telangana Warangal India Department of Commerce and Business Administration Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai62 India Department of Data Science UPES Uttarakhand Dehradun India Lovely Professional University Phagwara India India Department of Mechanical Engineering Vignan's Foundation for Science Technology and Research Vadlamudi Andhra Pradesh Guntur522213 India

ISBN: (纸本)9798350376913

The purpose of this exploratory research is to investigate the incorporation of machine literacy (ML) techniques for prediction conservation in supply chain management (SCM) systems. The successful operation of force chains is essential to the success of an organization in the modern business environment, which is always shifting and transitioning. When it comes to preemptively managing outfit failures, performing in expensive time-outs, and functional dislocations, conventional conservation techniques frequently fall short unexpectedly. To analyze literal data, detector inputs, and external variables, the utilization of machine learning algorithms makes it possible to anticipate conservation requirements. Through the utilization of prophetic analytics, supply chain management systems can identify hidden faults in the outfit, which enables visionary conservation actions. This study assesses various machine learning models and algorithms that have been adapted to forecast conservation circumstances in supply chain management (SCM) systems. Specific variables such as monitoring the status of the outfit and recognizing failure patterns are taken into consideration. In the end, the purpose of the investigation is to provide insight into the actual implementation of machine learning-driven prophetic conservation methods. This will enable organizations to improve their overall performance and optimize their force chain operations. © 2024 IEEE.

关键词： Maintenance

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Intelligent data Center Safety Status Prediction based on Algorithm Ensemble

Intelligent Data Center Safety Status Prediction based on Al...

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Mobile Internet, Cloud Computing and Information Security (MICCIS), International Conference on

作者： Wenxin Xu Biying Zhao Yu Zeng Tao Wang Artificial Intelligence R&D Department China Telecom Research Institute Beijing China Big Data and AI Center China Telecom Co. Ltd Beijing China

With the promotion of energy conservation in data centers, ensuring the indoor safety while reducing energy consumption has become a social concern. Excessive temperature in the data center may cause equipment damage, thus a technical method is needed urgently to ensure the environment temperature stays in a safe state. Based on the requirement for temperature warning, this paper proposed an intelligent data center safety prediction method based on model fusion. The experimental data comes from the intelligent data center project in China Telecom. Most recent works only used a single-model-based method such as SVr, Lr or LGBM to predict temperature. This paper first uses fusion-model-based method, integrating three single regressors (SVr, Lr and LGBM) with the best parameters by soft voting. The performance of fusion model has shown lower rMSE with 0.086, compared with the rMSE of the three single regressors. It finds out that the ensembled model reduces the impact caused by the misjudgment of single weak regressors, which is stable and reliable for intelligent data center safety prediction.

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Hybrid Machine Learning Approach of "KNN – LSTM" for detection of Brain Tumour in Mr images 15

Hybrid Machine Learning Approach of "KNN – LSTM" for detect...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Uganya, G. Gunale, Anuja Govindrao Kulkarni, Shruthi V. rajalakshmi, d. Valsaraj, Vani Srividhya, B. Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Engineering UPL University of Sustainable Technology Block no 402 Ankleshwar-Valia Road Gujarat Vataria India Department of AIML HKBK College of Engineering opposite manyata tech park Nagawara Karnataka Bengaluru India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Application Faculty of Humanities and Sciences SRM Institute Of Science and Technology Tamil Nadu Chengalpattu India

ISBN: (纸本)9798350370249

Now a days, more existing techniques are available to found tumours on brain from Mr images. But still the researchers are concentrated on to detect the brain tumour accurately with less time. So, this work proposed the hybrid machine learning algorithm of "K-Nearest Neighbour – Long Short-Term Memory" ("KNN-LSTM") with increased ‘accuracy’, ‘sensitivity’, ‘specificity’, with less ‘time consumption’. In this work, the brain tumour is identified by predicting the Mr images from huge dataset with usage of machine learning approach of "KNN-LSTM". Brain tumour detection is difficult when to compare the abnormal and typical tissues of large capacity. In order to enable a speedy recovery following therapy, brain tumours should be identified as soon as feasible. If the brain tumour is discovered early on, patients can benefit from improved care. due to the irregular spreading of the abrasions, brain tumours are still difficult to identify even with intensive study. Sometimes the tiny tumours look like normal, but it is challenging to find it. So, the techniques are need to increase the classification accuracy, and selecting the useful features. This paper proposes a hybrid machine learning model "KNN-LSTM" for categorizing the tumours with predicting through Mr images. MrI brain imaging collection is used for our experimentation. The "KNN" algorithm usage is to extract the important characteristics from pictures once the data has been pre-processed effectively. The proposed work predicts the brain tumour with 99.15% of ‘accuracy’, 98% of ‘sensitivity’, 97.12% of ‘specificity’ and testing time consumption of 0.5 minutes. © 2024 IEEE.

关键词： Image segmentation

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Towards Seamless Connectivity: Implementing 6G Communication Technologies in Next-Generation Networks 3

Towards Seamless Connectivity: Implementing 6G Communication...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Kalaiselvi, K. Sankar, r. Supriya, S. Kaushik, G. Swamy, Hemanth devika, M. Saveetha College of Liberal Arts and Sciences SIMATS Department of Data Analytics Tamilnadu Chennai India Chennai Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India MLR Institute of Technology Department of Electronics and Communication Engineering Hyderabad India Motorola Solutions United States Dhanalakshmi College of Engineering Department of AI& DS Tamil Nadu Chennai India

ISBN: (纸本)9798350354171

Advanced wireless networks are essential given the increase in demand for smart services enabled by the Internet of Everything (IoE). despite the fact that 5G networks could enable Internet of Everything (IoE)-based services, they can't meet all of the requirements needed to create intelligent applications. In order to overcome the shortcomings of the current infrastructure, there is an increasing demand to investigate the potential of 6G wireless communication technologies. Complex network optimization problems can be resolved by integrating artificial intelligence into sixth generation networks. An increasing user base and support for large-scale data-driven applications are essential given the fast evolving 6G landscape. In addition to highlighting network needs, critical supporting technologies, and a comparison of 5G and 6G networks, this study looks at recent advancements and trends in 6G technology. The report also explores new approaches to 6G connectivity, such as edge computing, backscatter communications, AI-driven IoT networks, and holographic beamforming, all of which are meant to support intelligent communities. In order to achieve the goal of 6Gbased IoT networks, it concludes by outlining potential research avenues. © 2024 IEEE.

关键词： Internet of Everything

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deep Learning Based Intrusion detection System with Integration of Blockchain technology and Internet of Things

Deep Learning Based Intrusion Detection System with Integrat...

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

作者： Uganya, G. rajalakshmi, d. Kumar, K. Ashok ravi, S. Sunheriya, Neeraj Kanan, Mohammad Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India St Joseph's college of Engineering Department of Information Technology OMR Chennai India Department of Vlsi Microeelctronics Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India College of Engineering University of Business and Technology Department of Industrial Engineering Jeddah21448 Saudi Arabia College of Engineering Zarqa University Department of Mechanical Engineering Zarqa Jordan

ISBN: (纸本)9798331543617

The number of people and objects linked to the Internet of Things (IoT) has expanded dramatically due to its fast development, giving consumers more opportunities to interact with and manage their physical surroundings. IoT is susceptible to a number of security risks, because of its openness, connectedness, and constrained device resources. It is essential to guarantee the dependability and security of data transmitted in IoT networks. In order to solve these security problems, this work provides intrusion detection system that combines IoT with and Blockchain technology. As well as, deep learning method is integrated to improve the security. This work uses the SHA-256 algorithm to secure the data, the Adaptive Optimization algorithm for generating the keys, rivest, Shamir, and Adleman (rSA) algorithm for encryption. and deep Neural Network (dNN) for identifying intrusions in Internet of Things. Through identification and mitigation of possible risks, this framework successfully protects IoT networks and ensures the integrity of communications, as demonstrated by a comparison analysis with existing models. © 2024 IEEE.

关键词： Network intrusion

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Certain Investigation of Optimization Methods of Sensor Nodes in Biomedical recording Systems 9

Certain Investigation of Optimization Methods of Sensor Node...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Ashokkumar, N. Pappa, C. Kanmani Siva Kumar, A. Srinivasan, dhamodharan Vijay, M.M. Mohan Babu University Department of ECE Tirupati India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of ECE Tamilnadu Chennai India SRM Institute of Science and Technology Department of Data Science and Business Systems Chennai India Sri Eshwar College of Engineering Department of Computer and Communication Engineering Tamilnadu Coimbatore India SCAD College of Engineering and Technology Department of ECE Tirunelveli India

ISBN: (纸本)9798350397376

Biosensors will monitor a patient's physiological signals (ECG, EEG, etc.) and send an alarm as soon as irregularities are discovered. downsized biomedical sensors are the focus of this article, which explores innovative circuit approaches for miniaturised sensors. In order to demonstrate the proposed biomedical compression engine, ECG compression was applied and assessed. A MIP (mixed-integer programming model) is used to represent and solve the clustering issue based on the power consumption analysis. It follows that an architectural model that may be used to design ECG monitoring systems has been provided, as well as an in-depth study of the ECG monitoring system's value chain and a complete evaluation of relevant literature based on an expert classification. Time synchronisation and signal synthesis are the two stages of the distributed cooperative sensing algorithm's execution of ECG signal gathering. This method uses the mutation probability of each solution to prevent local convergence. © 2023 IEEE.

关键词： Electrocardiograms

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Virtual Palette: An Efficient Object Tracking Tool

Virtual Palette: An Efficient Object Tracking Tool

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： r. Madhura S. Nikkath Bushra devi P. P Sasigresa P Department of Computing Technologies SRM Institute of Science and Technology Kattangulathur Chennai Tamil Nadu India Department of Artificial Intelligence and Machine Learning Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering (Data Science) Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Virtual Palette is a cutting-edge tool designed to enhance audience participation by providing an alternative to conventional jamboards for educators. Leveraging object tracking, a fundamental component of computer vision, it facilitates seamless and intuitive interactions with the audience. The video analysis process comprises three primary steps: object detection, object tracking across frames, and object behavior analysis. This sophisticated approach necessitates meticulous consideration of elements such as accurate object representation, feature selection for tracking, object recognition, and object tracking. One of the primary advantages of Virtual Palette over traditional mouse or touchpad devices is its ability to support clear and legible drawing and effective interaction in virtual environments. This feature is particularly beneficial for individuals with hearing impairments, aiding in the identification of patterns and drawings. The development of Virtual Palette involves overcoming challenges such as precise three-dimensional finger movement tracking and optimizing the technology for virtual backgrounds. Overall, Virtual Palette offers a dynamic and interactive teaching experience, akin to the advantages provided by screen recording for creating tutorials and virtual documentation.

关键词： Fingers Virtual environments Tutorials Transforms Object detection Green computing Feature extraction

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Advancements in demodex mite detection: A comparative analysis of YOLOv5 and YOLOv8 utilizing microscopic examination images

Advancements in Demodex mite detection: A comparative analys...

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2023 International Conference on Computer Application and Information Security, ICCAIS 2023

作者： Wang, Mini Han Fang, Xiaoxiao Lin, Zhiyuan Zeng, Peijin Yang, Yu Liu, Yunxiao Liu, Haoyang Hu, Wenhan Li, Xinyue Jiang, Xudong Chen, Guangshun Hou, Guanghui Chong, Kelvin K.L. Fang, Junbin The Faculty of Medicine Chinese University of Hong Kong 999077 Hong Kong City University of Macao The faculty of Data Sciences 999078 China Radiology and Imaging Department Zhuhai People's Hospital Zhuhai Hospital Affiliated with Jinan University Guangdong Zhuhai519000 China AI and big data R&D department Zhuhai Institute of Advanced Technology Chinese Academy of Sciences Guangdong Zhuhai519000 China AI and Ophthalmology R&D Laboratory Perspective Technology Group Guangdong Zhuhai519000 China The department of Dry Eye Disease Zhuhai Aier Eye Hospital Guangdong Zhuhai519000 China Beijing Institute of Technology Guangdong Zhuhai5190000 China School of Biomedical Engineering Guangdong Medical University Guangdong Dongguan523000 China Faculty of Science and Technology Beijing Normal University-Hong Kong Baptist University United International College Guangdong Zhuhai519000 China School of Optometry and Ophthalmology Tianjin Medical University Tianjin300000 China The School of Computer Zhuhai College of Science and Technology Guangdong Zhuhai519000 China Department of Optoelectronic Engineering Jinan University Guangdong Guangzhou510000 China

ISBN: (纸本)9781510675032

This study conducts a rigorous comparative assessment of YOLOv5 and YOLOv8 for the detection of demodex mites in microscopic examination images, leveraging crucial metrics such as accuracy, precision, recall, and F1-score. The investigation reveals the unequivocal superiority of YOLOv8, not only in quantitative measures but also substantiated by visual evidence, showcasing its applicability for real-time scenarios. YOLOv8 exhibits exceptional accuracy in overall detection and introduces a novel functionality for quantitative assessment of individual mites, providing essential granularity for precise diagnoses and therapeutic planning within dermatological and ophthalmological contexts. Positioned as a substantial advancement in object detection methodologies, YOLOv8 holds promise for significantly improving both accuracy and granularity in demodex mite detection within microscopic examination images. While acknowledging potential limitations associated with dataset-specific considerations, this research underscores the imperative for further validation across diverse clinical scenarios. Computational considerations for real-time processing prompt future investigations to explore optimization strategies, particularly in resource-constrained environments. These findings position YOLOv8 as a valuable tool for clinicians and researchers engaged in dermatological and ophthalmological studies, offering heightened accuracy and nuanced insights. Ongoing research, encompassing clinical validations and comparative assessments with other state-of-the-art models, is anticipated to contribute to a more exhaustive understanding of YOLOv8's potential and limitations in real-world applications based on microscopic examination images. © 2024 SPIE.

关键词： Object detection

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