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Recent Advances in science and engineering Technology (ICRASET), International Conference on

作者： Amulya A Shetty Pushpa G Adithya Balasubramanyam Electronics and Communication PES University Bangalore India Information Science and Engineering RVITM Bangalore India Computer Science and Engineering PES University Bangalore India

In recent years, technological innovations have considerably improved patient care and accelerated the healing process. Effective hydration and electrolyte management are two of the most important parts of hospital care. Intravenous(IV) treatment is critical, especially for patients in intensive care units (ICUs). However, the difficulties of overloaded hospitals frequently impede the seamless administration of this critical care. Currently, manual monitoring of IV drip levels is the responsibility of nurses and hospital employees. Unfortunately, their hectic schedules might lead to mistakes and omissions. Acknowledging the significance of tackling this core problem, the suggestion involves the creation of an independent, self-regulating drip monitoring system utilizing Internet of Things (IoT) technology. This entails the incorporation of ultrasonic and LDR sensors with the ESP8266, all programmed using Arduino UNO language. This novel technology intends to revolutionize drip monitoring by overcoming obstacles such as bubble formation in IV drips and removing the need for sophisticated alarm systems. This suggested system has far-reaching implications. Nurses and hospital personnel will be freed of the burden of manual monitoring by automating the procedure, freeing up their time and attention to focus on other vital patient care activities. The capacity of the device to identify air bubbles will help to avoid potential difficulties and adverse outcomes caused by inappropriate infusion. Furthermore, IoT technology allows for real-time data transfer and remote monitoring, allowing healthcare practitioners to receive precise and up-to-date information about fluid levels and any abnormalities from any place. We can usher in a new era of efficient and dependable IV fluid management by embracing technological innovations in healthcare, improving patient outcomes, and promoting a safer healthcare environment.

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Improving Online Education in Educational Institutions through the Integration of Internet of Things (IoT)

Improving Online Education in Educational Institutions throu...

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International conference of electronics, communication and Aerospace Technology (ICECA)

作者： Kottaimalai Ramaraj Ann Mathews Hp Lynsha Helena Pratheeba U Ramani K.L. Sujitha Thilagaraj M Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India Department of Electrical and Electronics Engineering K Ramakrishnan College of Engineering Trichy India Department of Computer Science and Engineering MVJ College of Engineering Bengaluru India Department of Industrial Internet of Things MVJ College of Engineering Bengaluru India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

A key facilitating infrastructure for building intelligent structures that enable efficient in-person and virtual learning environments is the Internet of Things (IoT). The shift to smart learning, that includes IoT and Artificial Intelligence (AI) into the educational system, is enticing since it directly affects the motivation of learners, enrolment, participation, and in-depth understanding. Teaching and learning, management, examination, and class monitoring are only a few of the numerous issues that afflict conventional education. Many ingenious approaches to many facets of our lives are being made possible by modern advancements in information and communication technology (ICT); nonetheless, these imaginative concepts aren’t adequately incorporated into the framework of education. The COVID-19 pandemic scenario, in specific, has brought out the need for innovative, creative approaches in the field of teaching. The present research examines previous investigations in the field and discusses the following topics: (i) issues with the current educational system and potential fixes; (ii) the shift to smart learning; and (iii) scientific hurdles (such as technological and cultural barriers) in making the move to smart learning. In accordance with these outcomes, creative approaches to the issues with the conventional system-such as smart teaching, smart evaluation, smart school environment, and smart management-are presented. The business and academic community may now include IoT, AI, and ICT into smart learning owing to the aforementioned experimental research’s novel developments.

关键词： Training Technological innovation Electric potential Education Transforms Writing Regulation Information and communication technology Internet of Things Artificial intelligence

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An efficient design and implementation of a reversible logic CCNOT (Toffoli) gate in QCA for nanotechnology

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Materials Today: Proceedings 2023年

作者： Patidar, Mukesh Shukla, Surendra Kumar Tiwari, Vikas Prajapati, Giriraj Kumar Sahu, Mukta Department of Electronics and Communication Engineering Indore Institute of Science and Technology Indore Madhya Pradesh 452001 India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand 248002 India Department of Electical and Electronics Engineering Oriental University Indore Madhya Pradesh 452001 India Department of Electronics and Communication Engineering Sree Chaitanya Institute of Technological Science Karimnagar Telengana 505527 India Department of Electronics and Communication Engineering Shivajirao Kadam Institute of Technology and Management Indore 452001 India

A quantum-dot cellular automaton (QCA) is a novel powerful, transistor-less, emerging quantum-computing nano-technology that has the potential to replace CMOS technology. It's a good fit for the next era of quantum computational Nanodevices technology. This paper proposes a novel reversible logic circuit such as the Toffoli or CCNOT (controlled-controlled-not) gate in QCA for nanotechnology using an XOR logic gate. The proposed solution employs the single-layer coplanar procedure to address the issues of the physical QCA layout-design and clock-synchronization. The reversible logic gate (RLG) layout-designs presented here are achieved by using a bijective algorithm mapping to reduce the number of QCA (quantum) cells, the response time (delay), and the layout area. The proposed controlled-controlled-not design (Toffoli) gate utilized 22 quantum dots, a delay of 0.5 clock-cycles, and an occupied design area of 24702.00 nm2 = 0.02 μm2. The novel QCADesign-E simulation tool optimized and implemented the proposed design using a bi-stable setup engine. © 2023 Elsevier Ltd. All rights reserved.

关键词： Mapping

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Comprehensive Survey on Internet of Medical Things (IoMT) - Applications and Challenges

Comprehensive Survey on Internet of Medical Things (IoMT) - ...

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International Conference on Computing and Networking Technology (ICCNT)

作者： V Sai Anusha R Kiran Kumar G Charan Kumar Palagiri Mabjan Department of Electronics and Communication Engineering Madanapalle Institute of Technology & Science Madanapalle India Department of Computer Applications Annamacharya University Rajampet India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The Internet of Medical Things (IoMT) is changing healthcare through the use of the Internet to connect medical devices, apps, and systems. This helps improve patient care, makes healthcare more efficient, and improves health outcomes. This paper deals with the edge-fog-cloud computing architectures for Internet of Health applications. Remote patient monitoring is done by collecting the data from wearable health devices, processing at different communication levels, applying intelligent algorithms and providing insights to the patient and healthcare professionals. This paper reviews the IoMT architecture and how the entire workflow is managed at device, edge, fog, and cloud levels. The paper also discusses IoMT challenges, such as data security and privacy concerns due to the interconnectedness and interoperability of devices at each layer and hurdles for seamless data integration within healthcare networks.

关键词： Surveys Patient monitoring Data privacy Medical devices Reviews Data security Data integration Internet of Medical Things computer architecture Interoperability

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Decoding the Brain Neuro-Inspired Deep Learning Architectures of Cognitive Computing

Decoding the Brain Neuro-Inspired Deep Learning Architecture...

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Knowledge engineering and communication Systems (ICKECS), International Conference on

作者： J. Latha S. Karthick Murugan S. Jayachandran G. Maheshwari G Naveen S. Navaneethan Department of Computer Science and Engineering R. M. K. Engineering College Tiruvallur Tamil Nadu India Department of Computer Science and Engineering R. M. D. Engineering College Tiruvallur Tamil Nadu India Department of Computer Science and Application SRM Institute of Science and Technology Ramapuram Chennai India Department of Artificial Intelligence and Data Science RMK College of Engineering and Technology Tiruvallur Tamil Nadu India Department of Electronics and Communication Engineering Madha Institute of Engineering and Technology Chennai Tami Nadu India Department of Electronics and Communication Engineering Saveetha Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

In response to the growing need for advanced artificial intelligence (AI) systems, the study thoroughly examines neuro-inspired deep learning architectures within the domain of cognitive computing. The need for such innovation originates from the constraints of traditional systems, characterized by their inefficiency in duplicating complex cognitive activities. Traditional techniques often need help with complex tasks and more biological neural network flexibility. The proposed system uses neuroscience principles to solve these limitations and create advanced models such as spiking neural networks and hierarchical attention processes, increasing machine learning capabilities. The results of extensive testing and analysis show that incorporating neuro-inspired designs considerably improves the system’s performance across several dimensions. The results show increased efficiency, flexibility, and an expanded understanding of cognitive processes, indicating an evolution in AI development. As the results prove, adopting neuro-inspired deep learning architectures is essential for progressing toward artificial general intelligence and realizing the full potential of cognitive computing.

关键词： Adaptation models Technological innovation Neuroscience Computational modeling Artificial general intelligence Deep architecture Spiking neural networks

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An Era of ChatGPT: Systematic Analysis of Utility and Challenges

An Era of ChatGPT: Systematic Analysis of Utility and Challe...

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International Conference on Edge Computing and Applications (ICECAA)

作者： Parveen Kumar Sharma Parveen Singla Vikas Gupta Paras Priyanka Garg Computer Science and Engineering Department Chandigarh Engineering College Mohali Punjab India RISE Department Electronics and Communication Engineering Chandigarh Engineering College Mohali Punjab India

Modern Artificial Intelligence (AI) algorithms are used by ChatGPT (Generative Pre-trained Transformer), a ground-breaking technology, to produce natural language answers to input or prompts. It has been applied in a variety of industries, including content development, customer support, and natural language processing. With the increase in the growth of AI technology, the company like OpenAI created the chatGPT prototype which is used as chatting machine. ChatGPT has several limitations even if it is wonderful and creates interesting stories, poems, songs, essays, and other writing. The bot will respond to queries from users with relevant, persuasive subjects and arguments. Here in this work, a brief introduction about chatGPT like how it came, what are the opportunities, various applications, implementing issues, and future is presented. A variety of chatGPT tools and software used is also analyzed.

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Adaptive Trajectory Planning in Autonomous Vehicles: A Hierarchical Reinforcement Learning Approach with Soft Actor-Critic

Adaptive Trajectory Planning in Autonomous Vehicles: A Hiera...

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International Symposium on Advanced Networks and Telecommunication Systems (ANTS)

作者： Amit Kumar Sharma Amit Choudhary Rajat Chaudhary Aditya Bhardwaj Anjum Mohd Aslam Electronics and Communication Sir Chhotu Ram Institute of Engineering and Technology Meerut Uttar Pradesh India Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden School of Computer Science and Engineering Technology Bennett University Greater Noida India

ISBN: (数字)9798350391725

ISBN: (纸本)9798350391732

This study introduces a methodology enabling automated vehicles to perform lane changes effectively within complex road systems. It emphasizes a hierarchical driver behavior framework that integrates decision-making with trajectory planning to enhance safety. The approach utilizes reinforcement learning (RL) agents for automated vehicles and the MOBIL model for human-operated vehicles, aiming to optimize the lane change process. The paper introduces the Soft Actor-Critic (SAC), an off-policy actor-critic algorithm, to improve training stability and effectiveness in real-world robotics applications. Additionally, it offers a comprehensive review of existing planning and control algorithms for self-driving vehicles, offering a comprehensive survey of techniques and their strengths and limitations to aid in informed design choices.

关键词： Training Surveys Trajectory planning Roads Reinforcement learning Stability analysis Planning Telecommunications Safety Robots

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A Transitional Review on Advancement of Generations of Wireless Technologies

A Transitional Review on Advancement of Generations of Wirel...

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Sustainable Computing and Integrated communication in Changing Landscape of AI (ICSCAI), International Conference on

作者： Sharad Pratap Singh Haripriya Himanshu Sharma Department of Computer Science and Engineering Tula's Institute Dehradun India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Department of Electronics and Communication Engineering Noida Institute Of Engineering and Technology Greater Noida Uttar Pradesh India

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

Wireless technology is one of the fastest growing businesses and is revolutionising mobile and wireless communications in many aspects. Wireless technology has grown tremendously in the last ten years. We now inhabit the 7.5th generation (G) of wireless technology. The most popular wireless technologies and the characteristics they've acquired throughout several generations are examined in this study's examination of the pertinent literature (0G, 1G, 2G, 3G, 4G, 5G, 6G, and 7G). In this article, we go over the importance and advantages of mobile wireless technology as it has developed throughout time.

关键词： Wireless communication 6G mobile communication Reviews 5G mobile communication 3G mobile communication Artificial intelligence Business

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Assessment of deep neural network for prediction of chronic kidney disease

Assessment of deep neural network for prediction of chronic ...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Neetu Gupta Hemant Kumar Gupta S. Vaishali Bobbala Sreedevi Department of Computer Science & Engineering Manipal University Jaipur Rajasthan India Department of Electronics & Communication Engineering Arya College of Engineering & I.T. Jaipur Rajasthan India Department of Electronics & Communication Engineering Vaagdevi College of Engineering Warangal Telangana India

This study uses deep learning techniques to detect chronic kidney disease at earlier stage to aid in its prevention. Disorders that affect the kidney's natural function are referred to as kidney diseases. This research recommends using a perceptron classifier with multiple layers classifier that is based on deep neural networks to identify CKD in patients. In order to diagnose chronic kidney disease, a database of 400 individuals with 25 attributes was employed. For the tests, the means of the respective attributes were used to replace each missing value in the original data set. The deep neural network's optimal parameters were then produced by fine-tuning the parameters and running several trials. In this research, A deep neural network is designed and developed. The performance of proposed model is compared with various cutting-edge machine learning techniques. Experiments show that the proposed model performs with 99.85% testing accuracy in classification tasks.

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Plant Leaf Disease Detection Incorporating IoT and XAI-Enhanced Deep Learning

Plant Leaf Disease Detection Incorporating IoT and XAI-Enhan...

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International Conference on computer and Information Technology (ICCIT)

作者： Pijush Kanti Roy Partho Md. Abid Hasan Rafi Pankaj Bhowmik Dept. of Electronics and Communication Engineering Hajee Mohammad Danesh Science and Technology University Dinajpur Bangladesh Dept. of Computer Science and Engineering Hajee Mohammad Danesh Science and Technology University Dinajpur Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Plant diseases represent a significant global threat, affecting both food supply and economic growth. Despite technological advances, early plant disease recognition remains difficult for small-scale farmers because of the lackings of infrastructure and resources. Thus, early disease detection using machine learning, deep learning, and computer vision techniques can improve the prediction process and make it easier. Previous studies that were implemented for plant-specific purposes, require high-resolution images and lack real-time detection methods. Therefore, to address these issues, we proposed a deep learning and IoT-based system that uses convolutional neural networks (CNN) and other supporting models, such as InceptionNetV3, VGG16, and EfficientNetB5, with a fine-tuning process to achieve higher accuracy in predicting 38 diseases over 14 different plant species in lower resolution, noisy data. Besides, to facilitate the system, we incorporated an ESP32CAM microcontroller and a server host that can be used to detect diseases in a real-time environment, which makes our research study robust and meaningful. We also implemented Explainable Artificial Intelligence (XAI), particularly Grad-CAM, along with our CNN model for visualizing the infected region in a plant leaf. The individual-trained models achieved an average accuracy of around 99%. We believe, our study can contribute to the agricultural sector by providing real-time disease detection, smart decision support systems, and low-cost solutions for farmers, mainly in resource-limited territories.

关键词： Deep learning Plant diseases Accuracy Microcontrollers Explainable AI Computational modeling Biological system modeling Real-time systems Convolutional neural networks Internet of Things

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