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

作者： Upendra, Sheela Verma, Pushpendra Kumar Gupta, Prerana Kulanthaivel, M. Karthigaikumar, P. Maral, Vikas B. Department of Symbiosis College of Nursing Pune India Iimt University School of Computer Science and Applications Meerut India Tmu Department of Psychitary India Karpagam Academy of Higher Education Department of Microbiology Tamil Nadu Coimbatore India Karpagam College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Vishwakarma Institute of Information Technology Computer Engineering Pune India

ISBN: (纸本)9798350370249

The Evaluation Technique, which uses AI, has the potential to completely change the way mental health services are provided. An overview of artificially intelligent technology (AI) and its most recent developments in the medical industry is provided in this article. It also reviews current research on mental health, emphasizing how AI may help with diagnosis and therapy while taking into account its present drawbacks, unexplored territory, and ethical ramifications. New Developments: We have taken a look at psychological wellbeing analyses and evaluations that classify or split psychological problems like discomfort, extreme personality disorder, and various mental illnesses, such suicide ideas and attempts, into different categories. These evaluations make use of medical records, scales for measuring mood, neuroimaging data, cutting-edge monitoring systems, and information from social media sites. The promise of statistical machine learning to treat issues with mental health is shown by these study findings, but it is crucial to identify the methods that provide the greatest outcomes. Together, these investigations add to the expanding corpus of research that shows how AI may be used in psychiatric treatments. As AI methods develop, they hold the potential to help mental health professionals define psychotic symptoms accurately, recognize chronic illnesses at an earlier, asymptomatic stage when measures can be more effective, and customize treatment plans based on unique patient characteristics. However, care must be taken to avoid extrapolating too soon from preliminary results. To close the gap between clinical therapy and behavioral health research, further study is necessary. © 2024 IEEE.

关键词： AI in healthcare and therapy computational intelligence data mining Diagnosis emotional wellness Innovative monitoring systems Mental health Neuroimaging data Personalized therapy

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Oral Cancer Detection at an Earlier Stage 3rd

Oral Cancer Detection at an Earlier Stage

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3rd International Conference on Computational electronics for Wireless Communications, ICCWC 2023

作者： Pal, Ankur Oshiro, Shiho verma, Prem Kumari Yadav, Mithilesh Kumar Singh Raman, Ashish Singh, Prabhat Singh, Nagendra Pratap Department of Computer Science and Engineering Dr. B. R. Ambedkar National Institute of Technology Jalandhar Jalandhar India Ryukyus University Nishihara Japan Department of Electronics and Communication Dr. B. R. Ambedkar National Institute of Technology Jalandhar Jalandhar India Department of Electronics and Communication NIT Hamirpur HimachalPradesh Hamirpur India

ISBN: (纸本)9789819719457

Worldwide, oral cancer is one of the most common cancers. Even with easy access to the oral cavity and significant improvements in treatment, oral cancer death rates remain high, primarily due to late-stage diagnoses and less successful treatments. The best way to treat cancer is to detect it early, and oral cancer is among the most expensive types in the United States. Patients’ survival rates are improved, and medical costs are reduced when early diagnosis occurs. Numerous techniques have been investigated by researchers for detecting oral malignancies in the early stages. An extensive review of the various methods analyzed by researchers to detect oral cancer at an early stage is presented in the paper. An overview of algorithms used for each step of cancer detection algorithms is provided, along with a comparison of different methods for cancer identification and classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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Deep Learning Based Load Balancing in Cloud Computing: A Survey

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Procedia computer Science 2025年 259卷 1963-1972页

作者： Deepali Vashistha Dhairya Mehta Malaram Kumhar Jitendra Bhatia Ahmed Alkhayyat Department of Electronics and Instrumentation Institute of Technology Nirma University Ahmedabad India Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India College of Technical Engineering The Islamic University Najaf Iraq

Deep learning has become a potent tool for solving challenging problems across a variety of professions. It presents intriguing approaches to optimize resource utilization and boost system performance in the context of load balancing in cloud computing. This review paper offers a comprehensive examination of current deep learning-based load balancing methods, highlighting their advantages, disadvantages, and possible uses. We give a comprehensive analysis of the current state of deep learning-based load balancing based on a thorough review of research publications and projects. The results provide insight into how well deep learning models handle load-balancing problems. This assessment lays the foundation for future research by recognizing research needs and understanding the existing environment. The information provided will help researchers and professionals create novel strategies to improve load distribution and increase the effectiveness of cloud computing systems.

关键词： Load balancing Deep learning Distributed computing Resource utilization Performance optimization

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Answer Type Prediction: Unveiling the Power of Deep Learning Models 2

Answer Type Prediction: Unveiling the Power of Deep Learning...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Swathi, B.P. Geetha, M. Shenoy, Manjula Suhas, M.V. Manipal Institute of Technology Manipal Academy of Higher Education Department of Information & Communication Technology Manipal India Manipal Institute of Technology Manipal Academy of Higher Education Department of Computer Science and Engineering Manipal India Manipal Institute of Technology Manipal Academy of Higher Education Department of Electronics and Communication Engineering Manipal India

ISBN: (纸本)9798350308846

Answer type prediction plays a crucial role in natural language question answering systems, enabling the generation of relevant SPARQL queries, a query language for retrieving data from RDF databases. This research paper focuses on answer type prediction for SPARQL query generation. Answer type prediction involves identifying the expected type of the answer to a given question, such as a person's name, a location, or a numerical value. By accurately predicting the answer type, the subsequent SPARQL query generation process can be tailored to retrieve the desired information from a knowledge base. The proposed work evaluates the performance of four deep learning models, which include GRU (Gated Recurrent Unit), Bi-GRU, LSTM (Long Short-Term Memory), and Bi-LSTM. The study's conclusion highlights GRU as the top-performing model for predicting the answer type based on the analysis of input natural language queries. The performance of the answer type prediction model is evaluated on a publicly available dataset, demonstrating its effectiveness in achieving an accuracy of 81% using GRU. The results obtained from the study emphasize the importance of accurate answer type prediction and provide promising outcomes. © 2023 IEEE.

关键词： Answer type prediction Bi-GRU Bi-LSTM GRU LSTM Natural Language Processing Natural Language Query Question Answering Systems SPARQL

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Nano Functionalized Antenna Based IoT Enabled Devices for Health Care Applications 1

Nano Functionalized Antenna Based IoT Enabled Devices for He...

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1st International Conference on electronics, Communication and Signal Processing, ICECSP 2024

作者： Janish Blessy, J. Jaison, B. Daniel Naresh Kumar, M. Velammal Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Surappet India R.M.K. Engineering College Department of Computer Science Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of ECE Tamil Nadu Chennai India

ISBN: (纸本)9798350364590

Continuous glucose monitoring (CGM) is crucial in diabetes management, necessitating the development of advanced sensing technologies. This study proposes a novel approach utilizing nano functionalized antenna-based IoT-enabled devices for CGM applications. The proposed method harnesses the unique properties of graphene, including high electrical conductivity, large surface area, and biocompatibility, to enhance glucose sensing capabilities. Evaluation of graphene-based materials involves assessing their electrical conductivity, surface area, and biocompatibility using techniques such as four-point probe measurements, BET analysis, and biocompatibility assays. A comparison with existing CGM models highlights the advantages of the proposed method, including high sensitivity, biocompatibility, miniaturization, and integration with IoT platforms. Numerical ratings on various parameters provide a quantitative assessment of the proposed method's performance relative to existing models. Overall, the proposed nano functionalized antenna-based IoT-enabled devices offer a promising solution for accurate and reliable CGM, with potential implications for improving diabetes management and patient outcomes. © 2024 IEEE.

关键词： Glucose sensors

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Design and Development of a Non-Invasive Glucose Monitoring Device Using Advanced Microcontroller 4

Design and Development of a Non-Invasive Glucose Monitoring ...

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

作者： Divya, S. Karthikeyan, P. Vaigundamoorthi, M. Ramya, D. Soudagar, Manzoore Elahi Rajendiran, M. Dr. N.G.P. Institute of Technology Department of Biomedical Engineering Tamil Nadu Coimbatore641048 India Velammal College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Madurai6641021 India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore641021 India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Tamil Nadu Coimbatore641202 India Saveetha School of Engineering Saveetha Institute of Medical and Techinical Sciences Department of VLSI Microelectronics Tamil Nadu Chennai602105 India Panimalar Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350385793

Millions of individuals all over the world are living with diabetes, a chronic condition that necessitates regular monitoring of Blood Glucose Levels (BGL). The present technique for minimal invasive BGL monitoring involves taking a small blood sample and transferring it to a glucometer. However, this procedure can be uncomfortable for the patient. This research presents a method for non-invasively monitoring BGL with the help of a powerful controller called the ARM Cortex-M4. For non-invasive applications, we use photoplethysmography (PPG) signals. Using a photodiode and an infrared LED light, PPG samples are received from a finger sensor. Signal processing and feature extraction are carried out using the Gray-Level Co-occurrence Matrix (GLCM) method. To predict the BGL, two ML models are developed: one employing a Linear Regression (LR) model and the other an Artificial Neural Network (ANN) model. The experimental findings show that the ANN model is quite effective, with an R2 score of 0.986. After careful consideration, the ARM Cortex-M4 microcontroller's processing capabilities were found to be optimal for running the ANN model. The model must be fine-tuned before deployment so it can communicate with the microcontroller and provide correct inference in real time. The finger sensor simplifies PPG sample collection, and the OLED display makes it simple to view BGL. © 2024 IEEE.

关键词： Photoplethysmography

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Malware Detection and Classification for Internet of Things Using Self-Attention Based Long Short-Term Memory 3

Malware Detection and Classification for Internet of Things ...

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

作者： Sudhakar, K. Husain, Saif O. Babu, D. Raja Kumar, M. Ashok Dineshkumar, R. Nitte Meenakshi Institute Of Technology Dept. Of Ai & Ds Bengaluru India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Sr University Department Of Electrical And Electronics Engineering Warangal India Skyline University Nigeria Department Of Computer Science Software Engineering And Cyber Security Kano State Nigeria Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Electronics And Communication Engineering Chennai India

ISBN: (纸本)9798350318609

The Internet of Things (IoT) connects numerous intelligent devices providing security features that interact with default settings accessed through applications. Additionally, Deep Learning (DL)-based mechanisms was involved for malware detection to identify and mitigate attacks. The Adaptive Synthetic Sampling (ADASYN) technique addresses class imbalances, especially when splitter class instances are distributed data in complex patterns. The proposed Self-Attention based Long Short-Term Memory (LSTM) classification method facilitates the handling of long-range dependencies in transferred data sequences offering high flexibility and adaptability to varying input lengths. For feature selection Particle Swarm Optimization (PSO) is utilized iteratively optimizes a solution in a high-dimensional space although it may have a low convergence rate due to its iterative process. The obtained results demonstrate the proposed Self-Attention based LSTM model achieves better accuracy reaching 98.05% on the NSL-KDD dataset and 99.01 % on the UNSW-NB15 dataset. These results ensure accurate classification compared to other existing methods, such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). © 2024 IEEE.

关键词： Internet of things

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Development of Cloud Centric Healthcare Monitoring System for Cardiac Patients embedded in IoT Platform 11

Development of Cloud Centric Healthcare Monitoring System fo...

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11th International Conference on Emerging Trends in engineering and technology - Signal and Information Processing, ICETET - SIP 2023

作者： Mishra, Adarsh Ravi Mishra, Ravi Shukla, Ragini Dr. C.V. Raman University Dept. of Computer Application and Information Technology Bilaspur India G H Raisoni Institute of Engineering and Technology Dept. of Electronics and Telecommunication Nagpur India

ISBN: (纸本)9798350348422

In terms of profit and employment, the Indian economy's healthcare sector has emerged as one of the most significant sectors. The Indian healthcare sector is experiencing rapid growth due to improved coverage, expanded functionalities and services and increased public and private spending. Due to which the life expectancy has increased dramatically. As the population of individuals rises in many societies, the number of people affected by chronic diseases, such as diabetes, cardiovascular disease, obesity, and so on, rises as well. As a result, Internet of Things (IoT)-based personalised healthcare is becoming a more significant idea.. This paper's main goal is to illustrate a cardiac patient e-health data acquisition, transfer, and monitoring system. The smart health monitoring system prototype uses a wireless sensor network to measure various health data, which are then sent to the concerned individual through GPRS. These parameters are also saved in Amazon cloud services for future use. © 2023 IEEE.

关键词： Internet of things

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A Machine Learning And Iot Based Contactless Entry System 5

A Machine Learning And Iot Based Contactless Entry System

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5th International Conference on Computational Intelligence and Communication Technologies, CCICT 2022

作者： Jain, Vishal Saini, Nidhi Bera, Some Jana, Keya B. M. Institute of Engineering and Technology Department of Electronics and Communication Engineering Sonepat India B.M. Institute of Engineering and Technology Department of Computer Science & Engg Sonepat India

ISBN: (数字)9781665472241

ISBN: (纸本)9781665472241

The COVID-19 pandemic has intensely impacted humanities globally. This scenario has laid down many protocols and procedures to mitigate the risk and thus ensure the safety of individuals. The pandemic has augmented the demand of contactless mechanism especially in the public *** paper presents novel machine learning and internet of things based solution for contactless entry to premises following mandatory checks at the entry as per COVID-19 protocols. The proposed model senses the body temperature and detects the face mask of an individual prior to entry. The entrance is allowed through contactless opening of the gate only if the body temperature is within prescribed limits and the face mask is properly put *** current work uses a machine learning model for detection of the face mask which uses the real time image during screening;the algorithm is trained using the data sets with and without mask. The temperature screening is carried out with temperature sensor connected to the Arduino processor.A prototype model using Arduino is prepared based on the inputs received from the temperature sensor and the Machine learning Algorithm. The gate shall open for entry if a person has normal body temperature and wearing a proper face mask, else, will be restricted through a custom alert. Also a track of the number of persons entering is monitored and sent to a web portal on real time basis to account for *** model ensures a contactless check at the entry, thus, controlling the outbreak of the Covid situation. © 2022 IEEE.

关键词： COVID-19

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AI-Enhanced Multi-Cloud Security Management: Ensuring Robust Cybersecurity in Hybrid Cloud Environments

AI-Enhanced Multi-Cloud Security Management: Ensuring Robust...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Srimathi, J. Kanagasabapathi, K. Mahajan, Kirti Ahamad, Shahanawaj Soumya, E. Barthwal, Shivangi KPR College of Arts Science and Research Department of Information Technology Covai Arasur641407 India Karpagam College of Engineering Computer Science and Technology Coimbatore India Bharati Vidyapeeth Deemed University Institute of Management & Entrepreneurship Development Department of Information Technology Pune411030 India University of Hail College of Computer Science and Engineering Department of Software Engineering Hail City Saudi Arabia St. Martins Engineering College Department of Computer Science and Engineering Telangana Secunderabad India Graphic Era Deemed to Be University Department of Electronics & Communication Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350319200

The complex landscape of multi-cloud settings is the result of the fast growth of cloud computing and the ever-changing needs of contemporary organizations. Strong cyber defenses are of fundamental importance in this setting. In this study, we investigate the use of AI in hybrid cloud settings for the purpose of multi-cloud security management. To help businesses improve their productivity and resilience, we provide a mathematical model for optimal resource allocation. Our methodology streamlines dynamic threat assessments, making it easier for security teams to efficiently priorities vulnerabilities. The advent of a new age of real-time threat response is heralded by the incorporation of AI-driven security tactics. The technique we use has real-world implications that may help businesses stay ahead of constantly changing threats. In the future, scientists will focus on autonomous security systems, interoperability, ethics, interoperability, and cutting-edge AI models that have been validated in the real world. This study provides a detailed road map for businesses to follow as they navigate the complex cybersecurity landscape of multi-cloud settings, therefore promoting resilience and agility in this era of digital transformation. © 2023 IEEE.

关键词： Adaptive Security Systems Artificial Intelligence Autonomous Security Cybersecurity Hybrid Cloud Multi-Cloud Security Quantum Computing Real-Time Threat Response Threat Assessment

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