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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Lakshmi, S. Shanmuganathan, C. Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369083

computer Aided Design (CAD) and dental industry are going hand-in-hand due to the increasing requirements for digitalizing the process of dental diagnosis and treatment. The crucial step in dental treatment is the identification of the individual affected teeth and gingiva region. These days 3D intraoral scanners are widely used to take the inputs for analysis and design of the tooth. High accuracy and resolution play a predominant role in providing efficient and hassle-free treatment to the patients. Since the intraoral scanners provide low quality input images in deep intraoral regions, current automated systems are facing the challenge of automated tooth labeling. The problem of segmentation of teeth and gingiva region of 3D IOS mesh scans are complex to process and produce desired output. The design of dental inlay restoration requires a fully automated end-to-end network to be developed. We propose a deep learning method called DPCNet to produce segmentation outputs that can automate the construction of a realistic surface of the defective tooth. Our goal was to start using point clouds as input to the neural network, which are obtained from mesh scans. DPCNet employs graph-based learning modules to acquire local features, which are summarized and fed into a stack of convolutional layers to achieve global features. Our approach is effective in real-time datasets, with minimal need for manual adjustment scenarios encountered. © 2024 IEEE.

关键词： Mesh generation

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Unsolicited Email Filtering

Unsolicited Email Filtering

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Vc, Jintu Nair, Ksheersagar S Karthik, N. Vani, V. National Institute of Technology Puducherry Department of Computer Science and Engineering Karaikal India

ISBN: (纸本)9798331540685

Spam emails, commonly known as junk emails, are unsolicited messages sent in bulk to numerous recipients. These messages typically inundate email inboxes and can range from annoying promotional content to potentially harmful phishing attempts and malware distribution. This work presents a comprehensive approach to email classification and sentiment analysis. It begins by preprocessing email data, encompassing lowercasing, punctuation removal, tokenization, and stemming. The study uses Logistic Regression, Support Vector Machine (SVM), and Naive Bayes models to classify emails as spam or ham with machine learning. The accuracy of these models is evaluated and compared, with SVM demonstrating the highest accuracy. Sentiment analysis is performed using the V alence Aware Dictionary for Sentiment Reasoning (VADER) tool to evaluate email sentiment. This work also generates word clouds to visually represent the most frequent terms in both input mail and the dataset. © 2024 IEEE.

关键词： Logistic regression

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Datasets for Aquaculture: A Deep Learning Perspective 8

Datasets for Aquaculture: A Deep Learning Perspective

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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Kabitha, P. Nandini, D. Usha Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350367904

Aquaculture is being used to a greater extent for conservation goals, such as species recovery, habitat restoration, and mitigating the effects of wild capture on harvested species that are vulnerable. Deep Learning (DL) models are in use for training datasets of aquatic organisms, to provide solutions to various problems in aquaculture farming. However, there is still motivation, awareness, and knowledge that are found necessary to encourage researchers to explore the potential unmet. In this work, the various aquaculture-associated datasets used in DL for image processing procedures have been elucidated. It addresses the challenges of these datasets to pinpoint areas for improvement, ultimately paving the way for future advancements in the application of DL within aquaculture. © 2024 IEEE.

关键词： Aquaculture Convolutional Neural Networks datasets Deep Learning image processing Marine Science smart fish farming

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Predictive Insights into Electric Vehicle Market Dynamics: An Exploration through Machine Learning 4

Predictive Insights into Electric Vehicle Market Dynamics: A...

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4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Aaditya, S. B. Abrish Kurmi, Aditya Karandikar, P.B. Army Institute of Technology Department of Electronics and Telecommunication Engineering Pune India Army Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350354218

The burgeoning electric vehicle (EV) sector epitomizes a crucial transition towards more sustainable modes of transportation, underscored by the global imperative to mitigate carbon emissions. However, despite the technological strides made in this arena, the pace of EV adoption is tempered by a confluence of factors, including but not limited to the initial purchase cost, the driving range per charge, and prevailing consumer perceptions surrounding EV utility and feasibility. This investigation harnesses the analytical prowess of machine learning (ML) methodologies to delve into the nuanced dynamics of the EV market. By examining a comprehensive array of EV attributes, this study endeavors to distill the determinants that significantly sway consumer adoption patterns. Preliminary findings underscore the paramount influence of variables such as top speed, operational range, and rapid charging capability on the EV's market price, delineated by their pronounced positive correlations. These insights not only illuminate the attributes that consumers value most but also furnish automobile manufacturers with the empirical evidence needed to tailor their development and marketing strategies, thereby catalyzing the transition towards electric mobility. © 2024 IEEE.

关键词： Self-supervised learning

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Blockchain enabled Vault for Evidence Management System 13

Blockchain enabled Vault for Evidence Management System

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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Sachan, Akarsh Pandey, Shagun Saxena, Rohit Chaurasia, Brijesh Kumar Pranveer Singh Institute of Technology Department of Computer Science & Engineering Kanpur India

ISBN: (纸本)9798350305463

Blockchain-enabled vault for evidence management systems have played an important role in the judicial system to provide an efficient, convenient, and secure access system over the cloud. Criminal record-keeping is a crucial aspect of law enforcement and judicial systems worldwide. Conventional approaches to criminal record storage are frequently plagued by security flaws, inefficiencies, and a lack of transparency. To achieve data integrity and privacy preservation, we proposed the Ethereum Blockchain to create the Vault. Building a Blockchain application on Ethereum ensures data integrity and security by employing three-factor authentication (password, OTP, and biometrics). The document will be stored on the Blockchain after the update from the judicial system and then stored in the Blockchain and Vault in an encrypted form. This application will ensure data integrity, authentication, and privacy preservation. The results show that the Blockchain-enabled Vault is a more secure way of managing, accessing, and protecting the criminal record that ensures data integrity and privacy. The suggested Vault is costeffective and can be easily integrated into Inter Planetary File System (IPFS) to maintain the necessary security challenges. © 2024 IEEE.

关键词： Blockchain

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A nano-scale arithmetic and logic unit using a reversible logic and quantum-dots

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Journal of Supercomputing 2024年第1期80卷 395-412页

作者： Navimipour, Nima Jafari Ahmadpour, Seyed-Sajad Yalcin, Senay Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Department of Computer Engineering Nisantasi University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou64002 Taiwan

The arithmetic and logic unit (ALU) is a key element of complex circuits and an intrinsic part of the most widely recognized complex circuits in digital signal processing. Also, recent attention has been brought to reversible logic and quantum-dot cellular automata (QCA) because of their intrinsic capacity to decrease energy dissipation, which is a crucial need for low-power digital circuits. QCA will be the preferred technology for developing the subsequent generation of digital systems. These technologies played a substantial role in the design of the ALU for operations such as multiplication, subtraction, and division. In developing reversible logic and QCA technologies, the ALU is frequently studied as a central unit. Implementing an efficient ALU with low quantum cost and a small number of cells based on an efficient reversible block can solve all previous issues. Therefore, this research constructs a Feynman gate, a Fredkin gate, and full adder circuits using reversible logic and QCA technology. Using all of the specified circuits, a 20-operation ALU is constructed. The power consumption of the proposed ALU under various energy ranges demonstrated significant improvements over earlier designs. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Energy utilization

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Attack on Shortest Vector Problem of Lattice Using Machine Learning 5th

Attack on Shortest Vector Problem of Lattice Using Machine L...

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5th International Conference on Trends in Computational and Cognitive engineering, TCCE 2023

作者： Singh, Shaurya Pratap Chaurasia, Brijesh Kumar Tripathi, Tanmay Gupta, Siddharth Pal, Ayush Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India

ISBN: (纸本)9789819719228

In this paper, we studied the attack on shortest vector problem (SVP) of lattice using the machine-learning (ML) algorithm KNN. The prime number factoring assumption or the presumption that the discrete logarithm problem is intractable are the two assumptions that underlie nearly all cryptographic security systems, including RSA and DSA. Lattice-based cryptography has recently gained popularity as a means of improving security as worldwide prepares for the onset of quantum computing. Lattices are used to secure the systems;however, one of the problems is the shortest vector problem of lattice used in cryptography. We addressed in this work the attack on lattice problem (SVP), especially two-dimensional problems, with the help of the machine-learning algorithm K-nearest neighbor (KNN). Results and analysis findings demonstrate that the suggested approach can achieve accuracy of up to 79% on self-prepared datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Nearest neighbor search

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Finger Vein Extraction and Authentication for Security Purpose Based on CNN

Finger Vein Extraction and Authentication for Security Purpo...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Nathan, R. Sakthivel, S. Selvi Rajendran, P. Hindustan Institute Of Technology And Science Department Of Computer Science &engineering Chennai India

ISBN: (纸本)9798350364828

Finger Vein ID is a biometric verification toolthat compares a person's finger vascular pattern to previously gathered data. Numerous applications, such as automated teller machines, computer and network authentication, end point security, and fingerprint recognition-one of the numerous biometrics used to identify people-are now being developed or deployed for this technology. Individuals and validate their identities that uses images of the vein patterns in human fingers that lie beneath the skin's surface employeetime and attendance tracking, credit card authentication, and vehicle security. With the advancement of electronics, there has been a steady rise in the need for easy-to-use, convenient, and highly secure authentication solutions to safeguard personal data held in mobile devices. One of the most effective biometric identification methods for user authentication is the finger vein recognition technique. due to its security and singularity. Since the dawn of technology, created for the detection of finger veins;however, the majority of these techniques relied on manually constructed characteristics, which have limitations with regard to image quality, finger location, etc. Since neural networks initially appeared, numerous identity verification techniques created. This research identifies the variables that may impact the outcome and surveys many finger vein verification methods utilizing CNN. The experimental examination of these methods uses both user- designed datasets with different qualities and publicly available datasets from finger veins. Biometrics, a method of protecting personal information, employs physiological or behavioral traits of humans to uniquely identify them. We send over SMS using AVR technology to deliver the SMS to a verified individual. © 2024 IEEE.

关键词： Biometrics

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IoT-based Smart Pill Reminding System

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Recent Advances in computer Science and Communications 2024年第2期17卷 13-19页

作者： Nagchandi, Akul Kher, Pradyot Gudipalli, Abhishek Tiwari, Amit School of Electrical Engineering Vellore Institute of Technology Tamil Nadu India Department of Computer Applications ABES Engineering College UP Uttar Pradesh Ghaziabad India

Background: The concept of pill reminders has been discussed and developed throughout the decade. It varies from cascaded plastic pill boxes to complicated robust dispensers. This proposed smart pill reminding system based on IoT is being designed by considering ease-to-use and cost-effectiveness. Method: A smart pill reminding system is a system that will alert the patient to take their respective pill at the desired time. It will also track the motion of the patient’s hand while taking the pill and will also display the pill count on an LCD Screen. In case a patient forgets/ignores the reminder provided by the system, the system will automatically display the status on the application that will be installed in the relative/caretaker’s phone and through an email on the patient's relative/caretaker’s email address to take subsequent action. The system will monitor the real-time using an RTC module, and as and when the current time matches the medicines time, it will activate its mechanism, and the patient will have a buffer time to take their medicine. In case a patient does take the medicine in the buffer time provided by the system, then one mechanism of the system will be activated. In another case, if a patient does not take the medicine in the stipulated time, further actions will be initiated by the system to benefit the patient. Results: It was tested and found that out of ten times, the system worked accurately nine times, with calculated accuracy as high as 90%. Initially, the Blynk application will display "Welcome Patient" and "You will be updated". Once the RTC matches the scheduled time to take medicine, the buzzer starts buzzing. If the IR sensor detects the movement of the user’s hand, the LCD will update the pill count, and the pill count is reduced by one. The LCD will also display the message "Medicine Taken". If the IR sensor does not detect the movement of the user’s hand, the LCD will display the same pill count. The LCD will also display the me

关键词： Liquid crystal displays

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A Hybrid Deep Learning Solution for Real-Time DDoS Mitigation in IIoT Environments 5

A Hybrid Deep Learning Solution for Real-Time DDoS Mitigatio...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Kamaleswari, S. Suganthi, N. SRM Institute of Science and Technology Department of Computer Science and Engineering Ramapuram India

ISBN: (纸本)9798331530013

The Industrial Internet of Things (IIoT) has revolutionized industrial operations by enhancing connectivity and automation. However, this interconnectivity also introduces significant vulnerabilities, particularly to Distributed Denial of Service (DDoS) attacks, which can disrupt critical industrial processes. This proposed model designed to fortify IIoT networks against DDoS threats. The proposed approach employs a hybrid model that integrates CNN and Bi-LSTMs networks to effectively capture spatial and temporal patterns in network traffic data. The framework encompasses including data collection, preprocessing, DDoS detection, and automated strategies. The framework using real-world IIoT traffic datasets, preliminary results prove that the work expressively improves DDoS detection capabilities while maintaining low false positive rates. This work ensuring the reliability and availability of industrial operations. Future research will explore advanced deep learning architectures and the integration of this framework with cutting-edge networking technologies for dynamic threat response. © 2024 IEEE.

关键词： Industrial internet of things (IIoT)

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