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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Raviraja, S. Seethalakshmi, K. Kushwaha, Sumit Vishnu Priya, P.M. Kumar, Kallakunta Ravi Dhyani, Bijesh Prabhu, V. Royal Research Foundation® Department of Computer Studies West Bengal Kolkata700050 India Department of Computer Science and Engineering Vel Tech RangarajanDr.Sagunthala R&d Institute of Science and Technology Tamil Nadu Chennai India University Institute of Computing Chandigarh University Department of Computer Applications Punjab 140413 India Kristu Jayanti College Department of Computer Science Karnataka Bengaluru560077 India Koneru Lakshmaiah Education Foundation Department of Electronics and Communication Engineering Andhra Pradesh Vaddeswaram522302 India Graphic Era Deemed to Be University Department of Management Studies Uttarakhand Dehradun248002 India Sri Sai Ram Engineering College Department of Mechanical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9781665456302

This research study discusses about the algebraic reconstruction technique (ART) for performing 3D object reconstruction and two other variants: the use of positivity constraints for reconstructed values and the extra use of transversal projections. This study has used Monte Carlo techniques for 3D objects to mimic illusions and projections using Poisson noise. For a variety of practical signal-to-noise ratios, the modulation parameter was optimized. In contrast to the reuse of transverse projection lines, this study has determined the usage of positivity restriction in the ART technique. If computing time is considered as a significant factor, these enhancements can be compromised in terms of processing speed. © 2023 IEEE.

关键词： Signal to noise ratio

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Wide-Deep Neural Networks with Randomized Sparse Backpropagation Simplify Demand-Side Load Event Detection

Wide-Deep Neural Networks with Randomized Sparse Backpropaga...

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2023 International Conference on Data science and Network Security, ICDSNS 2023

作者： Adusumilli, Manasa Ramesh, G.P. Katkam, Vijayrani Bhargavi Pareek, Piyush Kumar Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India St. Peter's Institute for Higher Education and Research Department of Electronics and Communication Engineering Chennai India Ballari Institute of Technology and Management Department of e and Ce Bellary India Hyderabad Institute of Technology and Management Department of Cse Hyderabad India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350301595

Identifying demand-side load events is a critical step in lowering energy use in a number of situations. Using wide-deep neural networks and randomised sparse backpropagation algorithms, this study presents a unique way to identify demand-side load events. To detect subtleties in the input data, we employ randomised sparse backpropagation and both shallow and deep network topologies. We also use the ReLU and sigmoid activation functions to improve the performance of our model. We use the Adam optimizer to train the proposed model, using a learning rate of 0.001 per batch and a batch size of 64. The results were compared to those of other popular technologies, such as support vector machines. According to the findings of our studies, the technique we utilised is more precise and efficient than any other option we investigated. The proposed approach combines wide-deep neural networks with randomised sparse backpropagation to simplify demand-side load event identification. © 2023 IEEE.

关键词： Energy efficiency

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An innovative analysis of Leveraging Transfer Learning for Accurate Image Recognition 2

An innovative analysis of Leveraging Transfer Learning for A...

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2nd IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2023

作者： Rajan, V.Aravinda Marimuthu, T. Shukla, Rishi Prakash Vats, Satvik Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Krishnankoil Virudhunagar626 126 India Ait Management Chandigarh University Punjab Mohali India Graphic Era Hill University Department of Cs&e Dehradun India

ISBN: (纸本)9798350381979

Transfer learning is a powerful technique used to leverage knowledge learned from one domain and transfer it to another domain, reducing the time needed to train a machine learning model. This paper presents a novel approach to apply transfer learning to improve accuracy in image recognition tasks. The authors first use classical transfer learning algorithms to analyze a publicly available image dataset and extract features relevant to the recognition problem. Then, they combine these extracted features with a convolutional neural network (CNN) to improve the classification accuracy of the CNN. The authors evaluate the performance of their approach using multiple datasets, such as MNIST and SVHN, and demonstrate the superiority in terms of high recognition accuracy, low memory requirements and fewer training iterations. The results show that the proposed approach outperforms the baseline CNNs, with an improvement of up to 10%. This approach could be used by research groups and computer vision systems that are concerned with accurate image recognition tasks. © 2023 IEEE.

关键词： Image recognition

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Design of AED Based Drone to Deliver the Medical Kit on an Emergency Basis 3

Design of AED Based Drone to Deliver the Medical Kit on an E...

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3rd International Conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Nagarani, P. Ahmad, Mohd Wazih Shalini, R. Singh, Pavitar Parkash Cvr College of Engineering Department of Emerging Technologies Hyderabad India Shri Ram Murti Smarak College of Engineering and Technology Department of Computer Science and Engineering Bareilly India Vels Institute of Science Technology and Advanced Studies Department of Bca & It School of Computing Sciences Chennai India Lovely Professional University Department of Management Phagwara India

ISBN: (纸本)9798350330724

Access to reliable modes of transportation is essential. Postal delivery to multi-million-dollar freight transports, practically everything is linked to its connections. Wheeled motor vehicles and manned aeroplanes are now the only options for transporting medical supplies at times of urgent need, which may be expensive, time-consuming, and often impossible if the emergency site is far. Drones are a game-changer in the medical industry because they can transfer vital supplies like blood and medical equipment. Unfortunately, given the state of the art, the drone's battery life is a constraint that may slow down the delivery process. This article details the research into the mechanisms designed for drones to increase their battery life and efficiently deliver first aid supplies. The drone's system safely delivers and drops medical supplies, reducing the drone's takeoffs and landings. The primary goal of this research is to develop a device that can safely transport medical equipment to its destination and then carefully unload it there. Results showed the system could safely transport and deploy first aid supplies during flight and landing. © 2023 IEEE.

关键词： Drones

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An Improved Meta Learning Approach for Optimizing Recipe Parameters for Semiconductor Processes 49

An Improved Meta Learning Approach for Optimizing Recipe Par...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Chen, Zhen-Yin Annie Lin, Chun-Cheng Lu, Ke-Wen Chin, Hui-Hsin Deng, Der-Jiunn National Yang Ming Chiao Tung University Department of Industrial Engineering and Management Hsinchu300 Taiwan Overseas Chinese University Department of Information Technology Taichung407 Taiwan National Changhua University of Education Department of Computer Science and Information Engineering Changhua500 Taiwan

ISBN: (纸本)9798350331820

It has been challenging to find the optimal recipe parameters for semiconductor processes to find a balance between budgets and computing efficiency. Therefore, this study focuses on finding the optimal recipe parameters of a semiconductor process using an improves meta Bayesian optimization (MetaBO) method, which can be trained with extremely few samples and historical data so as to quickly find the optimal process parameter combinations for the product. Experimental results show that the improved MetaBO significantly improves overall quality and efficiency in both model training and new task evaluation. © 2023 IEEE.

关键词： Budget control

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Convolutional Neural Network Based Deep Neural Network Model for Electrocardiogram Records Classification 7

Convolutional Neural Network Based Deep Neural Network Model...

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7th International Conference on Future Networks and Distributed Systems, ICFNDS 2023

作者： Khudhur, Ahmed Mahmood Alwan, Mohammed Hasan Al Omairi, Qayssar Mahmood, Omar Abdulkareem Hammadi, Yousif I Muthanna, Mohammed Saleh Ali Aziz, Ahmed Muthanna, Ammar Information Technology Department College Of Computer Science And Information Technology Kirkuk University Iraq Department Of Communications Engineering College Of Engineering University Of Diyala Baqubah32001 Iraq Department Of Computer Engineering College Of Engineering University Of Diyala Iraq Bilad Alrafidain University College Diyala32001 Iraq Institute Of Computer Technologies And Information Security Southern Federal University Taganrog344006 Russia Department Of Computer Science Faculty Of Computer And Artificial Intelligence Benha University Egypt Department Of International Business Management Tashkent State University Of Economics Tashkent Uzbekistan Department Of Telecommunication Networks And Data Transmission The Bonch Bruevich Saint Petersburg State University Of Telecommunications Russia Department Of Applied Probability And Informatics Peoples Friendship University Of Russia Russia

ISBN: (纸本)9798400709036

Cardiovascular diseases, also known as CVDs, currently rank as the primary incidence of mortality. The present approach for identifying illnesses involves the analysis of the Electrocardiogram (ECG), an electronic diagnostic device utilized to capture the rhythm of the heart. Regrettably, the process of seeking out specialists to conduct analysis on a substantial volume of electrocardiogram (ECG) data results in a significant depletion of medical capabilities. Hence, the utilization of deep learning algorithms for the identification of ECG characteristics has progressively gained prominence. Nevertheless, there exist certain limitations associated with these conventional approaches, which necessitate the need for manual characteristic identification, intricate models, and extensive training duration. This study presents a novel and effective approach for categorizing the five different types of heartbeats in the MIT-BIH Arrhythmia database. The proposed method involves the utilization of a 16-layer deep one-dimensional convolutional neural network, which exhibits both robustness and efficiency in its classification performance. Thus, the designed model is consisting of five-groups, first to fourth groups are the main feature extraction and mapping blocks, while the fifth group is fully connected and classification layers. The findings indicate that the model presented in this study has superior performance in terms of accuracy, precision, and F1-score. The proper classification of medical cases has a significant impact on the conservation of medical resources, hence positively influencing clinical practice. Additionally, the model training phase is characterized by its efficiency, since it does not significantly deplete resources. © 2023 ACM.

关键词： Electrocardiograms

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A Way Towards Next-Gen Networking System for the Development of 6G Communication System 4

A Way Towards Next-Gen Networking System for the Development...

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4th IEEE International Conference on Advance Computing and Innovative technologies in Engineering, ICACITE 2024

作者： Mohan, Chippy Singh, Devesh Pratap Singh, K. Ranjith Divya, N. Ramkumar, R. Abirami, S. Bangalore India Ies Institute of Technology and Management Ies University Electronics & Communication Engineering Department M.P Bhopal India Karpagam Academy of Higher Education Department of Computer Science TN India TN India Bannari Amman Institute of Technology Department of Ece Tamil Nadu Sathyamangalam India Hindustan Institute of Technology and Science Department of Information Technology Tamil Nadu Kelambakkam India

ISBN: (纸本)9798350360165

In this talk, the advancements announced by sixth-generation mobile communication (6G) as compared to the earlier fifth-generation (5G) system are carefully examined. The analysis, based in existing academic works, underscores the goal of improving diverse communication aims across various services. This study finds five crucial 6G core services designed to meet distinct goal requirements. To explain these services thoroughly, the framework presents two central features and delineates eight significant performance indices (KPIs). Furthermore, a thorough study of supporting technologies is performed to meet the stated KPIs. A unified 6G design is suggested, imagined as a combination of these supporting technologies. This design plan is then explained by the lens of five prototype application situations. Subsequently, possible challenges contained in the developing track of the 6G network technology are carefully discussed, followed by suggested solutions. The debate ends in an exhaustive examination of possibilities within the 6G world, seeking to provide a strategy plan for future research efforts. © 2024 IEEE.

关键词： 5G mobile communication systems

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An Efficient Deep Learning Approach for Automatic License Plate Detection with Novel Feature Extraction

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Procedia computer science 2024年 235卷 2822-2832页

作者： Kothai G Povammal E Amutha S Deepa V Department of CSE (AIML) KPR Institute of Engineering and Technology Coimbatore and 641407 India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur and 60203 India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil and 626126 India

In the domain of traffic management, road toll collection, and parking lot systems, vehicle number plate detection and identification play a pivotal role. Unlike conventional methods that treat license plate detection and character recognition as separate tasks, the system simultaneously addresses both challenges within a single neural network. Our Proposed methodology capitalizes on the efficiency and accuracy of the one-stage object detection algorithm known as YOLO (You Only Look Once) to locate license plates under diverse and challenging conditions. To augment the quality of input images with low resolution or poor clarity, we employ super-resolution generative adversarial networks (SRGANs). The image enhancement process substantially improves the visual quality of captured license plate images, facilitating more precise character recognition. Quantitative assessment of propounded system reveals compelling results. The license plate detection component achieves an outstanding average accuracy rate of 98.5%, surpassing previous methods by 15.2%. This comprehensive approach not only reduces the dependency on manual labour but also elevates processing precision. It seamlessly integrates into existing transportation infrastructure, resulting in heightened operational efficiency, reduced traffic congestion, and enhanced security measures. The rapid evolution of neural networks and deep learning techniques has streamlined the deployment of such applications, revolutionizing the field of traffic monitoring and management with unprecedented ease and precision. One-stage object detector, widely referred to as YOLO, is used to find licence plates in difficult circumstances.

关键词： computer vision traffic monitoring road tolls number plate detection single network YOLO

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Waste management System using IoT for Sustainable Cities 2

Waste Management System using IoT for Sustainable Cities

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Anand, J. Elangovan, Vetrimani Muthukumar, V. Chandrasekar, T. Srm Valliammai Engineering College Department of Management Studies Tamil Nadu Chennai India Presidency University Department of Computer Science and Engineering Karnataka Bangalore India Sri Sairam Engineering College Department of Management Studies Tamilnadu Chennai India Kalasalingam Academy of Research and Education Kalasalingam Business School Department of Business Administration Tamilnadu Srivilliputhur India

ISBN: (纸本)9798350325799

The waste management system is an innovative system that leverages Internet of Things (IoT) technologies to monitor and manage waste disposal processes. The system uses various sensors such as ultrasonic, infrared, weight sensors, and camera modules to detect the fill level of waste bins. A cloud server is a central platform that enables remote access to data collected by the sensors data in the waste bins and waste collection truck driver. The data is transmitted to the cloud server using wireless communication technologies such as WiFi. Waste management personnel can access the data remotely through a web-based interface or mobile application, which enables real-time monitoring and analysis of the waste management processes. Integrating sensors with IoT technologies provides a powerful solution for addressing waste management challenges and promoting environmental sustainability. Additionally, the system can analyze the waste composition and generate insights to support effective waste reduction strategies. © 2023 IEEE.

关键词： Internet of things

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AI-based Fuzzy Clustering System for Improving Customer Relationship management 6

AI-based Fuzzy Clustering System for Improving Customer Rela...

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6th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2022

作者： Krishnareddy, Kr Aravinda, T.V. Nair, Kiran Patel, Umesh Kumar Sadvokasova, Gaukhar Shyamala Susan, V. Sjm Institute of Technology Department of Computer Science and Engineering India College Abu Dhabi School of Management. Department of Management United Arab Emirates Chaudhary Charan SIngh University Department of Bca Noida India International Education Corporation Department of Architecture Almaty050042 Kazakhstan A P C Mahalaxmi College for Women Department of Computer Science Tamilnadu Tuticorin India

ISBN: (数字)9781665469418

ISBN: (纸本)9781665469418

Artificial Intelligence (AI) is a technology that is becoming more and more widely recognized with the addition of technologies such as personal helpers and chatbots. To get the most out of AI, your company needs to understand how to implement it better. Today, AI enhances almost every business segment and Customer Relationship management (CRM), which is the area that has the most benefit in enhancing a better customer experience. The existing literature does not provide sufficient empirical evidence of how social media technologies affect a company's distribution chain relationships. Today, Artificial Intelligence empowers almost every business segment and CRM, providing the greatest benefit in enhancing a better customer experience. The main purpose of this study is to show how to successfully implement chatbot in CRM using AI-based Fuzzy Clustering system (AI-FCS), how to integrate chatbot with CRM and how chatbots affect customer relationship management. From forecast lead scoring to service chatbots to customized marketing operations. Chatbots can provide tools to provide a highly productive and better, personalized customer experience to all employees. Chatbots are now easily accessible to everyone in your organization, analyzing data, predicting and planning the next steps, and automating tasks and decisions. Create an optimal control model for Customer Relationship management using the optimal control theory, depending on the characteristics of customer relationship management. With this model, it can gain effective management insights into Customer Relationship management for optimal new customer acquisition and existing customer retention strategies. © 2022 IEEE.

关键词： Fuzzy clustering

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