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检索条件"机构=Electronics and Computer Engineering Technology"

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Modernizing Home Protection: An IoT-Driven Approach with Smart Lock and Android Application 14

Modernizing Home Protection: An IoT-Driven Approach with Sma...

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14th IEEE International Conference on Control System, Computing and engineering, ICCSCE 2024

作者： Rajput, Summaiya Talpur, Noureen Boudville, Rozan Abro, Ghulam Mustafa E Talpur, Beenish Zahid, Faizan Mehran University of Engineering and Technology Department of Telecommunications Engineering Jamshoro Pakistan Computer Information Science Department Seri Iskandar32610 Malaysia Electrical Engineering Studies College of Electrical Engineering Universiti Teknologi MARA Permatang Pauh Pulau Pinang13500 Malaysia University of Petroleum and Minerals Dhahran Interdisciplinary Research Center for Aviation and Space Exploration King Fahd Dammam31261 Saudi Arabia University of Catania Electronics and Computer Engineering Department of Electrical Italy

ISBN: (纸本)9798350364507

In the field of access control locks, conventional methods often require users to download applications with Bluetooth connectivity, and biometric measures such as fingerprints or face recognition that introduce challenges like access to only authorized parties, limited device accessibility and intricate registration processes. To address these challenges, the research paper presents an innovative IoT-based Smart Door Lock System, aiming to redefine access control. Diverging from traditional approaches, this system eliminates the need for application downloads and simplifies the complexities tied to biometrics. The core innovation lies in the simplicity of a user-friendly 4-digit PIN for primary access, prioritizing ease of use. To overcome real-time access constraints, the system introduces One-time Password (OTP) and One Touch Access (OTA) modes. These modes eliminate the intricacies linked with traditional methods, ensuring immediate and hassle-free access for both authorized and unauthorized users. © 2024 IEEE.

关键词： Face recognition

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Investigating Backscatter Communication in Eco-Friendly Systems for Portable Sensing 4

Investigating Backscatter Communication in Eco-Friendly Syst...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Bisht, Vimal Singh Shnawa, Ammar H. Al-Saidi, Imad Al-Din Hussein Alzubaidi, Laith H. Hameed, Hind Zaidan, Doaa Talib Graphic Era Hill University Department of Electronics & Communication Engineering Uttarakhand Bhimtal India National University of Science and Technology College of Technical Engineering DhiQar64001 Iraq Alkunooze University College Computer Engineer Department Basra Iraq The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Al-Farahidi University Baghdad Iraq Kut University College Department of Laser and Optical Electronics Engineering Al Kut Iraq

ISBN: (纸本)9798350360165

the internet of things (iot) is a crucial technology that enables applications to be smoothly integrated across cyber-physical systems in a variety of scenarios. The energy constraints that battery-powered iot devices confront prevent them from being widely adopted, even if they have the potential to significantly improve things. The growing need for more capability in smaller device dimensions makes this problem more difficult to solve. To solve this issue, researchers have studied energy harvesting, wireless power transmission, and ways to reduce power use. This work sets itself apart by delving into backscatter transmission (backcom), a novel technique that combines sensing and wireless transmission. Backcom's ability to enable battery-free device operation may help create an ecologically friendly internet of things (iot). The article explores the possible applications of backcom in 'healthcare, agriculture, human activity recognition, transportation, and mobile iot' and gives an outline of the design, development, and working principles. In addition, the study explores the security and operational issues that these apps encounter and suggests workable solutions to guarantee strong energy efficiency. The paper concludes by examining potential uses of environmentally friendly backcom-based internet of things. © 2024 IEEE.

关键词： Energy harvesting

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Tumor Infiltration of Microrobot using Magnetic torque and AI Technique 2

Tumor Infiltration of Microrobot using Magnetic torque and A...

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2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Shalini, R. Mishra, Laxmi Athulya, S. Chimankar, Abhijeet Gajanan Kandavalli, Sunanda Ratna. Keshav Kumar, K. Selvan, R. Senthamil School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Department of BCA & IT Chennai India Khwaja Moinuddin Chishti Language University Department of Electronics and Communication Engineering Uttar Pradesh India Christ Deemed to Be University Faculty of Engineering Department of Computer Science and Engineering Bangalore India Chatrapati Shivaji BCA and BCS College Department of B.Sc. Computer Science Maharashtra India Sri Padmavati Mahila Visva Vidyalayam Department of Electronics and Communication Engineering Tirupati India G. Narayanamma Institute of Technology and Science Department of Humanities and Mathematics Hyderabad India Annamacharya Institute of Technology & Sciences Department of Electronics and Communication Engineering Tirupati India

ISBN: (纸本)9798350347982

Because of their surroundings and lifestyle alternatives, human beings, these days be afflicted by a huge style of illnesses. thus, early contamination prediction will become crucial. on the other hand, primarily based just on signs, docs warfare to make correct forecasts. The most challenging issue is accurately forecasting illnesses, which is why machine learning is essential to accomplish this task. To identify concealed patterns within vast amounts of medical data, disease information is processed using data mining techniques. We evolved a extensive contamination prediction primarily based on the affected person's signs. We rent the device getting to know techniques Convolutional Neural network (CNN) and ANFIS to exactly count on sickness (adaptive community-based totally fuzzy inference machine). For an correct forecast, this trendy illness prediction considers the character's way of life picks and fitness history. ANFIS outperforms CNN's set of rules in phrases of popular infection prediction, with an accuracy price of 96.7%. additionally, CNN consumes extra memory and processing energy than ANFIS because it trains and assessments on facts from the UCI repository. The Anaconda notebook is a suitable tool for implementing Python programming as it contains a range of libraries and header files that enhance the accuracy and precision of the process. © 2023 IEEE.

关键词： Forecasting

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Ai Machine Learning: Transformer Models For Enhanced Natural Language Processing - Techniques And Applications 12

Ai Machine Learning: Transformer Models For Enhanced Natural...

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12th International Conference on Internet of Everything, Microwave, Embedded, Communication and Networks, IEMECON 2024

作者： Pragith, S. Karthik, G.M. Sripriya, T. Rajasekaran, E. Periannasamy, S. Manthandi Aslam, Mohamed Zubair M.A. Department of Computer Science Engineering Vellore Institute of Technology Tamil Nadu Vellore India Vellore Institute of Technology Tamil Nadu Vellore India Department of ECE Jeppiaar Institute of Technology Tamil Nadu Chennai India Department of Science and Humanities V.S.B Engineering College Tamil Nadu Karur India Department of Electronics and Communication Engineering Mallareddy Engineering College for Women Maisammaguda Hyderabad India Department of AI &DS Dhanalakshmi College of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350387315

Modern technologies are needed to strengthen network security defenses because of the increase in cyber attacks in recent years. Because deep learning in particular allows for real-time threat identification and thorough performance evaluation, artificial intelligence (AI) has become a powerful tool for improving cybersecurity measures. This study examines how deep learning methods can be used in AI cybersecurity frameworks, with an emphasis on how well they can detect and counteract changing cyberthreats. The use of convolutional neural networks (CNNs) and deep neural networks (DNNs) for anomaly detection and pattern identification in network traffic is specifically discussed. The study also investigates the integration of AI-driven analytics to assess the performance and efficacy of security protocols, offering insights into optimizing defensive strategies against sophisticated cyber attacks. By leveraging AI capabilities, organizations can achieve proactive threat management, enhance incident response times, and fortify their networks against emerging cyber threats. This abstract sets the stage for a comprehensive exploration of AI's role in cybersecurity, highlighting its transformative impact on network security operations. © 2024 IEEE.

关键词： Cyber attacks

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A Novel Dynamic Watermarking for Secure Data Protection from Cyber Theft Based on Artificial Intelligence Supervision 8

A Novel Dynamic Watermarking for Secure Data Protection from...

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8th IEEE International Conference on Science, technology, engineering and Mathematics, ICONSTEM 2023

作者： Madala, Ravikiran Monica, R. Malwade, Sulakshana Sagar Selvakumaran, S. Budhavale, Shilpa Jivan Sathyabalaji, N. University of Cumberlands Information Technology DurhamNC United States Nandha Engineering College Department of Electronics and Communication Engineering Tamilnadu Erode52 India Dr.Vishwanath Karad School of Polytechnic and Skill Development Department of Computer Engineering Maharashtra Pune India Rajalakshmi Institute of Technology Department of Artificial Intelligence and Data Science Tamilnadu Chennai India Dr. Vishwanath Karad School of Polytechnic and Skill Development Department of Computer Engineering Maharashtra Pune India Jkk Munirajah College of Technology Department of Computer Science and Engineering Tamilnadu Erode India

ISBN: (纸本)9798350347791

One of the centre innovations of the Fourth Modern Insurgency (otherwise called Industry 4.0) is artificial intelligence (computer based intelligence), which can be utilized to shield Web associated gadgets from dangers, assaults, harm, and undesirable access. The current range of cyber security issues can be deftly handled by utilising well-known simulated intelligence techniques such as AI and profound learning procedures, the possibility of normal language handling, information portrayal and thinking, as well as the possibility of information or rule-based master frameworks displaying. In view of these man-made intelligence strategies, we give an exhaustive examination of "Simulated intelligence driven cyber security"in this article, which may be helpful for clever cyber security government agencies and the board. The cyber security registration method can be made more computerised and intelligent than the standard security frameworks by the security intelligence exhibiting in light of such simulated intelligence ***, we present various choices for future examination that fall inside the domain of our examination. At last, this paper's primary objective is to give direction and references to industry specialists in the field of cyber security, especially from a canny registering or simulated intelligence based specialized viewpoint. © 2023 IEEE.

关键词： Artificial intelligence

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Role of Empathy to Identify Unmet Needs in Indian Villages through Service Learning Program - A Case of the Unnat Bharat Abhiyan Program

Role of Empathy to Identify Unmet Needs in Indian Villages t...

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2022 IEEE IFEES World engineering Education Forum - Global engineering Deans Council, WEEF-GEDC 2022

作者： Kandakatla, Rohit Penta, Samyuktha Lingam, B. Sinha, Shiwani Chandraprakash, D. Udaya Sri, K. Kg Reddy College of Engineering and Technology Department of Electronics and Communication Hyderabad India Kg Reddy College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Kg Reddy College of Engineering and Technology Department of Civil Engineering Hyderabad India Kg Reddy College of Engineering and Technology Department of Mechanical Engineering Hyderabad India

ISBN: (纸本)9781665475280

Service learning in India is witnessing a large-scale adoption in higher education through the Unnat Bharat Abhiyan (UBA) program, a national-level service-learning program launched by India's Ministry of Education to promote community engagement and foster social responsibility among undergraduate students. The mission of UBA is to enable higher education institutions to partner with communities who live in rural India and engage in service-learning activities. The program advocates to build long-term engagement between academic institutions and nearby communities so that they could collaborate toward sustainable reciprocal partnerships. One of the goals of UBA is to establish linkages between academia and the community so that the knowledge created in the institutions could be targeted to solve complex societal problems. Higher education institutions that are part of this service-learning initiative were encouraged to do field studies through household surveys and informal interaction with the community members with a goal to identify unmet needs in the community. However, it was observed through prior research that the members of the community are only comfortable sharing their problems after faculty and students gain their trust. The paper presents a case study of a rural immersion experience that was introduced as part of the Unnat Bharat Abhiyan program at a private engineering institution in India. The 2-day rural immersion experience was designed to facilitate discussions among faculty and students with the community's various stakeholders, build trustworthy relationships, and empathize with their problems. The study employed a qualitative approach to investigate how students who were part of the rural immersion exercise described their experiences in the villages. Seven focus group discussions were conducted with 28 students who shared their highlights from visiting the villages and the process taken by them to identify unmet needs in the community. Themati

关键词： Rural areas

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A Heterogeneous Network Model Based Analysis and Detection of RNA Enabling Convolutional Neural Network 4

A Heterogeneous Network Model Based Analysis and Detection o...

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4th International Conference on Smart electronics and Communication, ICOSEC 2023

作者： Priyatharsini, G. Soniya Aruna, M. Myneni, Madhu Bala Kumar, P. Kiran Anil Kumar, D.B. Singh, Vijay Dr. M.G.R. Educational and Research Institute Department of Computer Science and Engineering Tamil Nadu Chennai600095 India Nitte Meenakshi Institute of Technology Department of Electrical and Electronics Engineering Bangalore India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Cvr College of Engineering Department of Computer Science and Engineering Telangana Ibrahimpatnam India School of Electrical and Electronic Engineering Reva University Karnataka Bengaluru India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350300888

RNA is an important target in the research of genetic diseases and disorders since it is essential for the process of gene expression and control. Convolutional neural networks (CNNs) and other machine learning-based methods for analyzing RNA sequences have recently attracted more attention. But reliable analysis and detection are significantly hampered by the enormous variability of RNA sequences. In this research, a brand-new heterogeneous network model for CNN-based RNA analysis and detection is provided. This model accounts for the wide range of RNA sequence properties, such as length fluctuation, secondary structure, and chemical changes. A unified model that incorporates these features is created by combining 1D and 2D convolutional layers. Multiple datasets, including synthetic and real-world datasets, were used to assess proposed model's performance. The outcomes show that the model performs better in terms of accuracy, sensitivity, and specificity than current state-of-the-art approaches. Furthermore, the model's performance is greatly enhanced with the addition of 2D convolutional layers and attention processes, according to rigorous studies to assess the contributions of each component of the model. Overall, the heterogeneous network model offers a reliable and precise method for analyzing and identifying RNA sequences, which has significant ramifications for comprehending hereditary illnesses and creating targeted therapies. © 2023 IEEE.

关键词： Machine learning

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Data Fusion and Analysis Techniques for Enhanced Decision-Making in IoT Environments 7

Data Fusion and Analysis Techniques for Enhanced Decision-Ma...

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7th International Conference on electronics, Communication and Aerospace technology, ICECA 2023

作者： Deepa, S. Radhika, M. Diwakar, Meenakshi Rufina, Princy Nithya, A. Mohanapriya, M. Panimalar Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India R.M.D. Engineering College Department of Information Technology Tamilnadu Chennai India S.A Engineering College Department of AI & DS Tamilnadu Chennai India Prince Shri Venkateswara Padmavathy Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India R.M.K.College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Jeppiar Institute of Technology Department of AI & DS Tamilnadu Chennai India

ISBN: (纸本)9798350340600

The proliferation of Internet of Things (IoT) devices has resulted in an inundation of diverse data sets that have the potential to considerably improve decision-making across many domains. This research work investigates the potential for modern data integration and analysis techniques to improve the decision-making abilities of IoT networks. Using regulated data acquisition, data integration, and cutting-edge analytic methods, the proposed work develops a solution to improve the accuracy of judgments. Using examples from smart manufacturing and healthcare monitoring, the proposed work demonstrates how the new methodology increases productivity and yields actionable data. Comparative analyses indicate that new methodologies markedly outperform those of the competition. This study identifies areas for future research on how integrating disparate IoT data with advanced analytics could lead to improved decision-making in a variety of contexts. © 2023 IEEE.

关键词： Internet of things

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Relay Awake Feature-based Efficient Route Formation in Mobile Wireless Sensor Network

Relay Awake Feature-based Efficient Route Formation in Mobil...

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2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and electronics, ICIITCEE 2023

作者： Kavitha, G. Ramanathan, P. Thamizhamuthu, R. Joel, M. Robinson Lenin, J. Department of Computer Science and Engineering Tamil Nadu Namakkal India TJS Engineering College Department of Electronics and Communication Engineering Chennai Tamil Nadu Kavaraipettai India SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India Kings Engineering College Department of Information Technology Tamil Nadu Chennai India Al Musanna College of Technology Department of Information Technology Muladdah Muscat Oman

ISBN: (纸本)9781665462631

Mobile Wireless Sensor Networks (MWSNs) comprise mobile sensor nodes that energetically exchange information between themselves. MWSN is self-configuring because of its dynamic nature, and each node functions with inadequate energy. Thus, energy depletion and link weight are significant challenges since links are unreliable. Uneven link strength initiates through the mobility in MWSN reasons network function. To solve this concern, this introduces Relay Awake Feature-based Efficient Route Formation (RAFE) in MWSN. RAFE approach uses a communication key function that isolates the malicious nodes in the MWSN. This measures the link weight through the sensor node packet obtained rate, loss rate, and delay factors. RAFE chooses the relay sensor node by the highest remaining energy and the greatest link weight. This approach reduces unwanted energy utilization and improves the network lifetime. Simulation results demonstrate that this approach improves the network energy efficiency and minimizes the network delay. In addition, it improves the network throughput in the network. © 2023 IEEE.

关键词： Sensor nodes

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Enhancing Lymph Node Classification in Rectal Cancer MRI Images Through a Multi-Model Approach

Enhancing Lymph Node Classification in Rectal Cancer MRI Ima...

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2024 IEEE International Conferences of AI, Science, engineering, and technology, AIxSET 2024

作者： Gupta, Rishit Iwahori, Yuji Funahashi, Kenji Bhuyan, M.K. Bhatt, Manish Kijsirikul, Boonserm Wang, Aili Ouchi, Akira Shimizu, Yasuhiro Indian Institute of Technology Guwahati Dept. of Electronics & Electrical Eng. Assam Guwahati India Chubu University Dept. of Computer Science Kasugai Japan Nagoya Institute of Technology Dept. of Computer Science Nagoya Japan Chulalongkorn University Dept. of Computer Engineering Bangkok Thailand Harbin University of Science and Technology Higher Educational Key Laboratory Harbin China Aichi Cancer Center Hospital Dept. of Gastroenterological Surgery Nagoya Japan

ISBN: (纸本)9798350390995

Rectal cancer remains to be a major issue worldwide, necessitating accurate and precise methods for proper classification of lymph nodes in MRI scan images to guide treatment decisions. The use of deep learning based techniques can help with the task of classification. This research focuses on enhancing the process of lymph node classification in rectal cancer MRI images by using a multi-model approach. Firstly, we explore the use of diffusion models in data augmentation, to produce synthetic lymph node images to counter the issue of limited dataset size. Secondly, Real-ESRGAN and Swin2SR super-resolution techniques are examined for their ability to improve image quality and hence enhance the accuracy of lymph node classifications. Lastly, ConvNeXt is employed as an advanced technique used to improve overall performance at classifying lymph nodes for rectal cancer. Through a series of detailed experiments on the dataset consisting of T2-Weighted Axial MRI scans from 54 patients, we demonstrate the effectiveness of our approach in classification of lymph nodes. The metrics like accuracy and precision exhibit significant improvements, highlighting the superiority of our multi-model approach. © 2024 IEEE.

关键词： Magnetic resonance imaging

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