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IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS)

作者： B.V. Satish Babu J. Manoranjini Ravindra Changala Mohd Aarif S. Suma Christal Mary I Infant Raj Department of IT Prasad V Potluri Siddhartha Institute of Technology Vijayawada India Department of AI & ML Chaitanya Bharathi Institute of Technology Hyderabad India Department of Information Technology Guru Nanak Institutions Technical Campus Hyderabad India Associate Editor-Publishing Global Research Network Noida India Department of Information Technology Panimalar Engineering College Poonamalle Chennai India Department of Computer Science and Engineering K. Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

The integration of biometric-based access control systems with robust facial recognition technology within Internet of Things (IoT) environments holds paramount significance in addressing contemporary security challenges. The novelty of the research endeavours to contribute to this critical area by investigating and implementing a novel approach, employing Convolutional Neural Networks (CNN) for facial classification within the biometric framework. The significance of this study lies in its potential to enhance security measures in IoT environments, ensuring reliable and efficient access control. Previous attempts at integrating facial recognition into IoT have encountered notable challenges, including suboptimal accuracy, vulnerability to adversarial attacks, and scalability issues. This research builds upon these prior issues by introducing CNN for facial classification, leveraging its capabilities in image processing and pattern recognition to achieve enhanced accuracy and resilience against potential security threats. The novelty of the study is underscored by the application of CNN within the biometric context of IoT, offering a cutting-edge solution tailored to the intricacies of interconnected devices. Experiments conducted on benchmark datasets showcase the promising capabilities of the proposed system, with face recognition accuracies reaching an impressive 98%. These results underscore the potential of this research to significantly advance the state-of-the-art in biometric-based access control, particularly within the context of IoT environments. The utilization of CNN introduces a pioneering element, contributing to the evolution of secure and efficient access control solutions in the era of IoT.

关键词： Access control Face recognition Scalability Image processing Signal processing Convolutional neural networks Internet of Things

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Introducing Service Learning To Under Graduate engineering Students Through EPICS

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Procedia computer science 2020年 172卷 688-695页

作者： Santosh Madeva Naik Surendra Bandi Hema Mahajan Assistant Professor Department of Mechanical Engineering HITAM Hyderabad Telangana 501401 India Associate Professor Department of Computer Science Engineering HITAM Hyderabad Telangana 501401 India Assistant Professor Department of Computer Science Engineering HITAM Hyderabad Telangana 501401 India

This paper describes the introduction of doing engineering through the "engineering Project in Community Service (EPICS)" and the impact of a community-based learning approach on student motivation. Students get practical hands-on experience while working with non-profit organizations. Now a day’s students find it difficult to learn in the traditional way of classroom teaching chalk and board. They want to work on real-time problems with community partners by applying engineering knowledge. EPICS program helps in grooming the students’ skills in design, communication, teamwork, and develop leadership skills, facing the challenges, social awareness, multidisciplinary knowledge, solution-driven approach towards a problem and maintaining social relationships. EPICS is one of the IUCEE (Indo Universal Collaboration for engineering Education) initiative where students are exposed to study the community needs and frame an engineering problem to work on further.

关键词： EPICS Community partner Design process Team work Products

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A Development of Control0020Techniques for Design of OM Based Robots Using Fuzzy Logic

A Development of Control0020Techniques for Design of OM Base...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Alagappan Vikrant Sharma Ramy Riad Al-Fatlawy Sherin Eliyas Tareq Hafdhi Abdtawfeeq Israa Jabbar Falah Hassan Abdullah Ali Saad Alwan Department of computer applications Hindustan institute of technology and science Department of CSE Graphic Era Hill University Adjunct Professor Graphic Era Deemed to be University Dehradun Uttarakhand Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Al-Farahidi university Baghdad Iraq Law Department Al-kunooze University College Basra Iraq Department of Forensic Sciences College of Science National University of Science and Technology DhiQar Iraq Business Administration Department Al kut Kut University College wasit Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

In this study, a fuzzy controller design specifically for omnidirectional robots is presented. We explore mobile robot dynamics and kinematics, providing a basis for our suggested fuzzy control technique. Our method targets the football robots created in our research lab and deals with the mobile robot’s position and rotational angle control. Our created control algorithms demonstrate effective navigation skills for these football robots through extensive simulation testing. The positive outcomes attested to the efficacy of the controllers we suggested. We also do an analysis of the performance characteristics of our proposed control systems, which shows significant improvements in regulation times for modifying orientation angles and attaining new locations. In the future, we want to investigate how Z number-based fuzzy techniques might be applied to improve control system performance even *** is potential to improve our control tactics and achieve even higher efficiency in robotic navigation tasks by utilising the Z number system.

关键词： Navigation Heuristic algorithms Kinematics Control systems Regulation Mobile robots Task analysis

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Profit aware Resource Allocation in Fog Computing: A Stackelberg Game Approach

Profit aware Resource Allocation in Fog Computing: A Stackel...

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International Conference on computer, Information and Telecommunication Systems (CITS)

作者： Akshita Doad Nitin Gupta Mohammad S. Obaidat Kuldeep Singh Jadon Piyush Rawat Kuie–Fang Hsiao Department of Computer Science and Engineering National Institute of Technology Hamirpur HP India Distinguished Professor at the King Abdullah II School of Information Technology The University of Jordan Amman Jordan School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China The Amity University Noida UP India University of Texas-Permain Basin Odessa Tx USA

Recently, Internet of Things (IoT) based smart systems have led to more latency-sensitive and bandwidth hungry IoT applications. Fog computing as an extension of Cloud computing fulfills those requirements more effectively. However, a key challenge for the fog computing is multiple resources allocation between heterogeneous Data Service Operators (DSOs) and End Users (EUs). The DSOs want to increase their revenues, however, due to lack of efficient resource allocation mechanisms, they often end up compromising Quality of Service (QoS) and violating Service Level Agreements (SLAs). It is very important for them to make a balance among user's expectations, expenses of providers and penalties for not providing services within the deadline. Hence, a profit-aware resource allocation policy using Stackelberg game is proposed in the work where DSO acts as leaders and EU as followers. The DSOs being the leaders, set the prices first, send it to the followers, and then the followers can compute their best demand for resources accordingly. Next, Stackelberg equilibrium for each type of resource is obtained to find the best resource demand strategy for an EU. The simulation results demonstrate the superiority of the proposed algorithm.

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Effect of geometric heterogeneity using an auxiliary device on the accuracy of complete arch implant scanning: An in vitro study of different clinical simulations

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Journal of Prosthetic Dentistry 2023年

作者： Abdelrehim, Aly Sulaiman, Eshamsul Sofian, Hazrina Salleh, Nosizana Mohd Graduate student Department of Restorative Dentistry Faculty of Dentistry Universiti Malaya (UM) Kuala Lumpur Malaysia Associate Professor Department of Restorative Dentistry Faculty of Dentistry Universiti Malaya (UM) Kuala Lumpur Malaysia Senior Lecturer Department of Software Engineering Faculty of Computer Science & Information Technology Universiti Malaya (UM) Kuala Lumpur Malaysia

Statement of problem: Intraoral scanning of implants supporting complete arch prostheses is limited because of the lack of geometric heterogeneity and unique reference points, creating inherent errors in the image stitching process by the scanner software program. Purpose: The purpose of this in vitro study was to evaluate the significance of geometric heterogeneity on complete arch implant scanning by using a novel auxiliary geometric device. Three different clinical simulations were tested to assess its significance. The study also assessed whether scans produced using the auxiliary device would meet a clinically acceptable threshold. Material and methods: A total of 60 scans (n=20) were performed using an intraoral scanner in 3 different clinical simulations: 2 parallel implants, 4 parallel implants, and 4 implants with a 30-degree posterior angulation of the distal implants. Scanning alternated between using the auxiliary geometric scanning device (test groups;4IP+, 4IA+, 2IP+) and not using the device (control groups;4IP-, 4IA-, 2IP-). A reference scan for each model was prepared from a high precision laboratory scanner. The scans were analyzed for accuracy in 3-dimensional deviation, interimplant distance deviation, and angular deviation by using an inspection software program. The effect of the auxiliary device was statistically analyzed by comparing scans of the same group using the paired t test for normally distributed data and the Wilcoxon Signed Rank test when data were not normally distributed (α=.05). Results: Significant effects of the auxiliary geometric device were found in 3-dimensional, distance and angular deviations (P<.05). Scans performed using the device were significantly more accurate in most implant positions (P<.05). Linear and angular deviations were clinically acceptable for all test groups. However, the deviations were above the clinically acceptable threshold for the control groups. Conclusions: Using an auxiliary geometric device sig

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Empowering Job Seekers by a Natural Language Solution for Personalized Job Descriptions and Enhanced Employment Prospects through Clustering Techniques

Empowering Job Seekers by a Natural Language Solution for Pe...

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Artificial Intelligence and Emerging Technology (Global AI Summit), International Conference on

作者： Upinder Kaur Salil Bharany Satvik Vats Anand Kumar Keshav Kaushik School of Computer Science and Engineering Lovely Professional University Phagwara Punjab India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura India Department of CSE Graphic Era Hill University Adjunct Professor Graphic Era Deemed to be University Dehradun India Amity School of Engineering and Technology Amity University Mohali Punjab India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

This Paper is based on our project which aims at implementing a Natural Language based solution which would deliver job descriptions based on a few criteria like preparing the transformed dataset, performing NLP processing to rank the best features in the Job Description, training transformer model and implementing an API endpoint which can be consumed over the web. The input data has been refined and data dictionaries have been created to generate Job description based on job title and skills. To anticipate the token that should come after the input, we have trained the GPT2 decoder transformer model to use the most recent token embedding of the input sequence. The study concludes that it is critical for job searchers to customize job descriptions according to an individual's talents, prior experiences, and educational background. While most of the research to date has focused on the client side of the hiring process, the suggested model seeks to empower job searchers by giving them customized job descriptions, which increases the likelihood that they will find acceptable employment. To provide insights into a variety of disciplines and help job searchers better grasp the dynamics of the recruiting process, the initiative made use of an extensive dataset. K-means clustering and DBScan clustering were two of the models and hyperparameters investigated within the project's time restrictions. K-means clustering improved context knowledge, albeit at the expense of adding noise to the data. Subsequent developments may entail investigating topic modeling and clustering strategies in greater detail to strengthen the model. Prospective directions for further research encompass utilizing Part of Speech (POS) tagging to extract distinct portions of job descriptions, implementing machine learning techniques to enhance synonym lists, and investigating substitute text creation models if preliminary efforts provide inconclusive results. With these developments, the suggested mod

关键词： Noise Employment Tagging Speech enhancement Transformers Data models Natural language processing Time factors Recruitment Qualifications

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Effect of Emotional Valence and Emotional Support on the Psychological Well-Being of Caregivers – Analysis Based on Facebook Alzheimer's Community

SSRN

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SSRN 2022年

作者： Sridevi, Mutyala Kumar, Arun B.R. VTU Research Centre and Asst. Professor Department of MCA BMS Institute of Technology and Management Bangalore India VTU Research Centre Department of MCA and Professor Department of Computer Science and Engineering BMS Institute of Technology and Management Bangalore India

The use of social media by patients and caregivers for emotional and psychological support has increased with the advent of interactive online platforms like Facebook that host number of disease support communities. The Emotional Valence exhibited and Emotional Support received by the participants of these communities help the healthcare researchers and professionals understand about an individual’s psychological well-being. Up keeping the caregiver mental condition helps in overcoming the trauma faced by them during caregiving to patients, especially, with Alzheimer’s who encounter diverse but intense symptoms of cognitive deterioration and increased dependence. An ensemble learning model has been proposed to predict the option for trauma counselling by the caregivers of Alzheimer’s patients which demonstrates better performance than the base estimators under study. Neuropsychologists and psychologists would be interested in the results of current research to provide timely and personalised care to the caregivers culminating in healthy and happy caregiving responsibility. © 2022, The Authors. All rights reserved.

关键词： Deterioration

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Improved diabetes disease prediction IWFO model using machine learning algorithms

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AIP Conference Proceedings 2024年第1期3161卷

作者： R. Gunavathi V. Sivakumar B. Senthil Kumar S. Vijayalakshmi Associate professor School of Science Department of Data Science Christ University Pune India Associate Professor & Program Leader School of Computing Asia Pacific University of Technology and Innovation Kuala Lumpur Malaysia Assistant Professor Department of Computer Science Sree Narayana Guru College Coimbatore India

Diabetic disease is the mostly affected and massive disease on a global level. Diagnosing the diabetic earlier will help the medicalist to give the improved and latest clinical treatment. The healthcare specialist unit uses many machine learning techniques, methodologies and tools for decision making in diabetic field. The machine learning techniques are utilized for the prediction of the diabetic diseases in the initial level. To eliminate such issues, optimized detection techniques are proposed. First of all, the training samples are increased using the sliding window protocol. Further, class imbalanced training data classes are balanced and resolved using the adaptive and gradient booster technique. Further, the diabetic feature selection process is improved by the Intensity Weighted Firefly Optimization firefly techniques (IWFO), in which irrelevant features are reduced based on the correlation between the features that deducts the unwanted features involved in the diabetic disease process. Then the feature transformation problem is faced by the PCA technique, which manages the several types of features. Finally, the improved and optimal hybrid random forest is applied into the normal and diabetes classes respectively. The proposed system predicts the diabetic disease efficiently and maximizes its precision of the prediction system. The present paper is compared with different classifiers to determine the efficiency of the work. Overall, the initiated system improved the present studies accuracy level.

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Comparative analysis of reduced feature set based ML models for IDS

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AIP Conference Proceedings 2023年第1期2773卷

作者： P. A. Jishiya K. P. Swaraj Ajay James M-Tech Student Computer Science and Engineering Govt. Engineering College Thrissur India Associate Professor Computer Science and Engineering Govt. Engineering College Thrissur India Assistant Professor Computer Science and Engineering Govt. Engineering College Thrissur India

Cyber-attacks targeting organisations and individuals are getting worse day by day. With the technological improve-ments, the principles used to launch attacks are also varied. Intrusion Detection System (IDS) is a network solution to detect attacks and anomalies in the network. Leveraging Machine Learning for developing an IDS can make it more powerful. This work aims at evaluating the performance of machine learning models for Intrusion Detection System built based on CART Algorithm, ID3Decision tree algorithm, Random forest algorithm with Convolutional Neural Network. The model is built using KDD Cup99 Dataset. In this work we perform Linear Correlation and Mutual Information feature selection algorithms for ML algorithms and for Convolutional Neural Network algorithm being a deep learning technique feature selection happens automatically. Analysing various parameters including F1-score, ID3 has shown better results.

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Survey on various DDoS attacks on firewall and study on emerging DoF attacks

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AIP Conference Proceedings 2023年第1期2773卷

作者： P. A. Jishiya Ajay James K. P. Swaraj M-Tech Student Computer Science and Engineering Govt. Engineering College Thrissur India Assistant Professor Computer Science and Engineering Govt. Engineering College Thrissur India Associate Professor Computer Science and Engineering Govt. Engineering College Thrissur India

A firewall is a network device that act as a security guard between a trusted network and an untrusted network. Firewall is the first line of defence against malicious traffic. Nowadays, attacking an organisation network for evil intentions has become a common thing. Distributed Denial of Service (DDoS) attack floods network traffic with illegitimate traffic and thereby preventing accessibility to legitimate users. However new type of attacks are being emerged targeting the network devices and their vulnerabilities. Attacker uses specially crafted packets to overload firewall with comparatively lesser effort, known as Denial of Firewalling (DoF) attack. This paper discusses about the attacks targeting the firewall and also about the newly emerged DoF attack. Moreover, a detection model for DDoS attack is also analysed.

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