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检索条件"任意字段=Conference on Photonic Devices and Algorithms for Computing V"

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Revolutionizing Electoral Integrity With A CNN-Powered Smart voting System Using Advanced Facial Recognition Techniques 7

Revolutionizing Electoral Integrity With A CNN-Powered Smart...

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7th IEEE Pune Section International conference, PuneCon 2024

作者： Kathiravan, M. Balasubramanian, Lubin Sreesubha, S. venkataramana, K. Irumporai, A. Sathyadurga, v. Saveetha Institute of Medical and Technical Sciences School of Engineering Department of Cse Saveetha Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology Dept of Computing Technologies India Rajalakshmi Engineering College Department of Aiml Thandalam Chennai India College of Engineering & Technology Department of Cse Qis Andhra Pradesh Ongole India Ict Trainer Datec India Hindustan Institute of Technology and Science Department of Cse Padur Chennai India

ISBN: (纸本)9798331527822

A Smart voting System is a sophisticated web service that offers a straightforward and secure way to conduct elections using blockchain technology. It guarantees the accuracy of votes, even in real-time voting, with fraud nearly eliminated, making the electoral process more straightforward and democratic. This research proposed CNN (convolutional neural network) algorithms, which provide a secure and easy-to-use face recognition-based user interface. The system compares a facial scan with data stored on central servers and confirms your identity if a match is found. The system uses the acronym UDD(Universal Democratic Delivery) to represent a voter's ability to vote on e-Enabled days, DDM(Direct Democracy Machine) and DRE(Direct Recording Electronic) machines, dead-end checking with removable inking pins, discounted absentee ballots, and more. The paper thoroughly discusses the claimed 99.5% accuracy of the CNN algorithm and explores the potential for private information leakage if implemented directly on user devices. This approach enables a more democratic process to emerge, ensuring that elections are both authentic and convenient. The experimental results demonstrated higher accuracy than traditional methods. © 2024 IEEE.

关键词： Convolutional neural networks

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Holistic Knowledge Framework for Improving PC Design using Big Data Analytics 7

Holistic Knowledge Framework for Improving PC Design using B...

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7th International conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Rajendran, T. vignesh, O. Priyadharsini, v. Jagadeesh, K. Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Easwari Engineering College Department of Electronics and Communication Engineering Chennai India Akshaya College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India GRT Institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Tiruttani India

ISBN: (纸本)9798350397253

Cyberspace is massively expanding every day, and the users of these digital devices are looking for more innovative applications to ease their day-to-day work. The main objective of any device is to use available system resources at a maximum optimum level and give high performance to the users. Especially in personal computers (PC), there is still a massive gap in the user experience and utilization of available system resources. There are several situations where error conditions and system responsiveness lag. Over several years the digital world evolved and provided high-performance computing. Furthermore, the PC design enhancements could be carryout through the participation of PC users. That could be accomplished by collecting the system performance indicators, system behavior and properties, software application usage, user working log, and other wide range of real-time data from the PC. These telemetry data needed to be retrieved from an extensive array of PCs then these datasets would be reposited on the centralized servers. By applying the big data analytical methods and other mining algorithms over these extensive datasets to identify the hidden patterns and discover more knowledge about the key indicators that would help improve PC performance and user experiences. Through this knowledge acquisition model, the organizations and research institutions could develop the most resilient and adoptable PC designs and software applications developments. © 2023 IEEE.

关键词： Personal computers

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New and Efficient Lightweight Cryptography Algorithm for Mobile and Web Applications 17

New and Efficient Lightweight Cryptography Algorithm for Mob...

引用

17th International conference on Future Networks and Communications / 19th International conference on Mobile Systems and Pervasive computing / 12th International conference on Sustainable Energy Information Technology, FNC/MobiSPC/SEIT 2022

作者： Tawalbeh, Lo'ai Alicea, Michael Alsmadi, Izzat Department of Computing and Cyber Security Texas AandM University-San Antonio San Antonio78224 United States

Essentially all forms of online communication involve the use of cryptography to ensure that only the intended recipient of a message can read it. Nearly every protocol includes message encryption as a part of its standard. Whether it is https, sh, or S[MIME, the most common types of connections have encryption enabled by default. Most computers can encrypt and decrypt information without any perceivable difference to the user. Processors found in home computers are fairly powerful, but home computers are not the only devices people have. There are an increasing number of smart devices in your average person's home. These smart IoT devices do not have as powerful hardware as the average PC. To maintain the expected instant transmission of data while providing security, lightweight crypto algorithms are needed for these devices. © 2022 The Authors. Published by Elsevier B.v. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the conference Program Chairs.

关键词： Cryptography

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Deep Learning Techniques for Image Recognition in IoT-Enabled Surveillance Systems

Deep Learning Techniques for Image Recognition in IoT-Enable...

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2024 Asian conference on Intelligent Technologies, ACOIT 2024

作者： Dankan Gowda, v. vishnu Tej, Y. Potharaju, vidya Sagar Jakkidi, Premkumar Reddy Sharma, Aanchal Sudhakar Reddy, N. Department of Electronics and Communication Engineering BMS Institute of Technology and Management Karnataka Bangalore India Department of Computer Science and Systems Engineering Andhra University Visakhapatnam India Department of ECE Vignana Bharati Institute of Technology Ghatkesar Hyderabad India 15950 paramount way FriscoTX75189 United States DCSA Panjab University Chandigarh India Department of ECE School of Engineering Mohan Babu University Tirupati India

ISBN: (纸本)9798350374933

This paper proposed an improved method that can be used in the identification of images within IoT based surveillance systems through the use of deep learning techniques especially the CNNs. The growth of concern and complexity therefore entails massive feeding from bursts of data from surveillance cameras, hence the need for reliable algorithms for processing the data in real-time with reasonable accuracy. The described system can be described as inclusive of IoT devices communicating with edge computing and cloud services for the purpose of scalability and performance enhancement. In this study, a very sophisticated experimental setup was employed;the authors used a number of datasets for surveillance purposes to evaluate the model. The results revealed that the segments of the method achieve a high accuracy and are continuously running through data in diverse surveillance scenarios while also being scalable. In the results section, we realized that CNN model was able to handle various environmental conditions such as;extreme lightening and occlusion. On the basis of the above mentioned data, it will be possible to suggest that this approach is rather superior compared to the other conventional approaches and therefore can be suggested as the possible solution of the modern conditions of surveillance. It thus provides platform for the advancement of intelligent surveillance system for use in smart city, security and safety application. © 2024 IEEE.

关键词： Convolutional neural networks

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Homomorphic Encryption Enabled SvM for Preserving Privacy of P2P Communication

Homomorphic Encryption Enabled SVM for Preserving Privacy of...

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2024 IEEE International Students' conference on Electrical, Electronics and Computer Science, SCEECS 2024

作者： Sahu, Sourabh Ganeshan, R. Muneeswaran, v. Vit Bhopal University School of Computing Science and Engineering Bhopal-Indore Highway Sehore Madhya Pradesh Kothrikalan466114 India

ISBN: (纸本)9798350348460

In recent years, the protection of our data privacy has become crucial, particularly within the realm of peer-to-peer (P2P) communication networks. we propose a novel homomorphic encryption framework a deep dive into the exploration of cutting-edge methods to ensure the preservation of privacy, placing special emphasis on the effective utilization of homomorphic encryption. Our approach strives to uphold the confidentiality of encrypted data while maintaining functionality, achieved through the integration of defensive encryption, a machine-learning classifier algorithm, and homomorphic encryption. We conduct a thorough comparative analysis both before and after incorporating homomorphic encryption. This entails the implementation of sophisticated encryption algorithms on the Support vector Machine (SvM) model, covering diverse aspects such as data sources, transmission routes, and individual devices, using the p2p communication threats dataset. The study illuminates the inherent link between strengthening endpoint security and the adoption of homomorphic data encryption, underscoring the significance of network-level privacy controls in the realm of peer-to-peer communication. This research elevates the overall privacy architecture by harnessing Support vector Machines (SvM) for anomaly detection and predictive analytics. In doing so, it clarifies the fundamental relationship between enhancing endpoint security and embracing homomorphic data encryption. Essentially, this work introduces novel perspectives on reinforcing privacy security within decentralized communication networks. © 2024 IEEE.

关键词： Endpoint Security Homomorphic Encryption Network Security P2P Communication Privacy SvM Classifier

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On the Issue on Automation of the Security System of Mobile Electrotechnological Installations 1

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7th International conference on Intelligent computing and Optimization, ICO 2023

作者： Yudaev, I. Daus, Yu Prokofiev, P. Baev, I. Panchenko, v. Kuban State Agrarian University Kalinin St. 13 Krasnodar350044 Russia Volgograd State Agrarian University 26 University Ave Volgograd400002 Russia Russian University of Transport Obraztsova St. 9 Moscow127994 Russia Federal Scientific Agroengineering Center VIM 1St Institutsky Passage 5 Moscow109428 Russia

ISBN: (数字)9783031508875

ISBN: (纸本)9783031508868

Today, mobile electrotechnological installations are actively used in agriculture, which not only perform the functions of moving technological equipment to perform various operations, but are also used to generate electrical energy with its subsequent use for technological purposes. The structure of such an installation usually consists of a wheeled tractor and mounted electrical equipment, the operation of which requires voltage of different levels and different types. In this regard, questions arise about the development of measures to ensure the safe operation of these electrical installations, about the justification of the structure of the system for their safe operation and the use of various automation approaches for speed, high-quality and fast processing of control signals in it. Based on the analysis of specialized documents and the results of experimental studies, a list of electrical safety measures was substantiated and the structure of the security system was designed. Using the example of theelectric impulse weeder, it is shown that such a structure must necessarily be divided into separate components, which contain requirements for circuits and equipment supplied by voltages up to 1000 v, as well as circuits and electrical devices of voltages above 1 kv. To design an automated control system for such security systems, general principles and approaches have been developed, as well as algorithms that implement them. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Security systems

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A Programmable photonic Chip for High-dimensional Quantum computing

A Programmable Photonic Chip for High-dimensional Quantum Co...

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2022 IEEE Region 10 International conference, TENCON 2022

作者： Huang, Jieshan Chi, Yulin Zhang, Zhanchuan Yang, Yan Gong, Qihuang Wang, Jianwei School of Physics Peking University State Key Laboratory for Mesoscopic Physics Beijing China Institute of Microelectronics Chinese Academy of Sciences Beijing China

ISBN: (数字)9781665450959

ISBN: (纸本)9781665450959

Controlling and programming quantum devices to process quantum information by the unit of qudit provides the possibilities for noise-resilient quantum communications, delicate quantum molecular simulations, and efficient quantum computations, showing great potential to enhance the capabilities of qubit-based quantum technologies. Here, we report a programmable high-dimensional quantum processor in a silicon-photonic in-tegrated circuit, which monolithically integrates all the key functionalities and capabilities of initialisation, manipulation, and measurement of the two quantum quart (ququart) states, and demonstrate its enhancement of quantum computational paral-lelism. By reprogramming the processor, we implemented the basic quantum Fourier transform algorithms, all in quaternary, to benchmark the enhancement of quantum parallelism using qudits. The monolithic integration and high programmability have allowed the implementations of more than one million high-fidelity preparations, operations and projections of qudit states in the processor. Our work shows an integrated photonic quantum technology for qudit-based quantum computing with enhanced capacity, accuracy, and efficiency, which could lead to the acceleration of building a large-scale Quantum computer. © 2022 IEEE.

关键词： Silicon photonics

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Cyber-Immunity at the Core: Securing Biomedical devices through Hardware-Level Machine Learning Defense

Cyber-Immunity at the Core: Securing Biomedical Devices thro...

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2023 IEEE Biomedical Circuits and Systems conference, BioCAS 2023

作者： Sayadi, Hossein He, Zhangying Fernandes, Chelsea William Miari, Tahereh California State University Department of Computer Engineering and Computer Science Long BeachCA United States Claremont Graduate University Center for Information Systems and Technology CA United States

ISBN: (纸本)9798350300260

Healthcare cybersecurity is crucial for protecting hospitals' networks and computing systems from malicious cyber-attacks. With the increasing motivation and capability of cyber attackers, it is necessary to secure the software and hardware infrastructure of biomedical computing systems used in healthcare. Due to the increasingly digitized nature of modern biomedical devices used in healthcare systems, effective cybersecurity solutions have become more essential. Traditional security protocols focused on software-level protection, with the underlying hardware assumed to be secure. However, recent studies have revealed vulnerabilities in hardware that attackers can exploit to compromise computing systems. To address this, in this paper a bottom-up approach is proposed, delegating security to the underlying hardware to enhance the security and cost-efficiency with less visibility to the attacker. This paper aims to develop effective intelligent countermeasures for hardware-assisted cyber-security at run-time in biomedical devices. It leverages processors' performance events via Hardware Performance Counters (HPCs) combined with Machine Learning (ML) techniques to fortify biomedical devices at the hardware level. To achieve this objective, we introduce a tailored and cost-effective hardware monitoring framework, complemented by a machine learning-enabled methodology. This approach enhances the accuracy and efficiency of malware detection and identification using real-time data from biomedical processors' hardware events. The experimental results demonstrate that the XGBoost model, using 4 hardware events, excels in malware detection, boasting 95% detection rate (F-measure and Accuracy), efficient resource utilization, and low inference latency. Additionally, the ExtraTree classifier achieves 87% F-measure and exhibits swift performance in classifying malware types in real-time. © 2023 IEEE.

关键词： Biomedical Cybersecurity Hardware Security Machine Learning Malware Detection and Identification

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Comparison of Artificial Intelligence algorithms for IoT Botnet Detection on Apache Spark Platform 4th

Comparison of Artificial Intelligence Algorithms for IoT Bot...

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4th International conference on Innovative Data Communication Technologies and Application, ICIDCA 2022

作者： Anwar, Faaiz Saravanan, S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

The amount of network traffic generated from the Internet of Things (IoT) devices is massive which leads to Big data. In present scenario, IoT devices has taken a leap ahead in terms of growing technology and insecure network traffic flow. The sensitivity of the data in the IoT network is becoming very high, which concern the security in legal and privacy issues. Traditional Intrusion Detection System (IDS) is used as a primary line security to differentiate between the attack and benign network traffic flow. As the size of the network traffic capture increases, it becomes more important to handle such big data. Similarly, when the number of classes and instances increases, the complexity of the data also increases. In this paper, we propose a big data platform based intrusion detection system which can differentiate between the types of network traffic flow generated from the IoT devices. We compared the performance of deep learning algorithms with machine learning algorithms on Apache Spark platform and found that machine learning algorithms outperform deep learning algorithms with greater accuracy and less training time for the model. We evaluated our research based on real world network traffic dataset BoT-IoT [1] for an improved intrusion detection system in IoT network traffic. © 2023 The Authors. Published by Elsevier B.v.

关键词： Internet of things

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Innovative Advances: Crafting an Intelligent Controller for Home Energy Management Systems with Seamless Integration of Renewable Sources 7

Innovative Advances: Crafting an Intelligent Controller for ...

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7th International conference on Contemporary computing and Informatics, IC3I 2024

作者： varasree, B. Reddy, N.v. Uma Yadav, Suman Avdhesh Nijhawan, Ginni Garg, Monica Sirisha, N. Institute of Aeronautical Engineering Department of Information Technology Telangana Hyderabad India New Horizon College of Engineering Department of Artificial Intelligence & Machine Learning Bangalore India Iilm University Department of Cse Uttar Pradesh Greater Noida India Lovely Professional University Phagwara India Lloyd Law College Uttar Pradesh Greater Noida India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350350067

This research proposes an improved energy administration and optimization technique that utilizes fuzzy logic algorithms and incorporates many inputs and outputs (I/O) along with a cost-effective method. This technology is used in the administration of hybrid electric power sources, which include Pv cell, turbines (wind), and grid-supportive devices. The novelty of this research lies in the precedence of solar Pv, wind turbine, and storage system energy sources above the grid network. The grid network is only used in case of bad weather. This system can supply an average household with up to 4,000 Wh of energy per day. In addition, by using an electrolyzing system, surplus unconventional energy produced during good weather conditions is used to produce hydrogen, which is well-suited for household cooking and heating purposes. Developing an intelligent economic strategy by using fuzzy technology to bolster improved energy management and optimization methods. The fuzzy logic software at the heart of this system has been written on the Arduino 2560 mega microcontroller. It is responsible for allocating events and considering all possible circumstances. This system has been developed using a flowchart that facilitates hybrid system management. In order to maintain uninterrupted housing, it is advisable to use strategies such as methodologies, a parametric search, and simulations to precisely characterize the system components that provide assistance to the power grid. The effectiveness of the recommended method is confirmed by the display of output directives generated by the electronic switches. © 2024 IEEE.

关键词： Controllers Energy Management Systems Energy Optimization Renewable Energy Integration Sustainable Living

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