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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Gopalakrushna, A. Raj, Vijilius Helena Vani, V. Divya Singh, Navdeep Jethabhai, Patel Yogeshkumar Chakravarthi, M. Kalyan Vignana Bharathi Institute of technology Telangana Hyderabad India New Horizon College of Engineering Department of Applied Sciences Karnataka Bangalore India Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Sankalchand Patel University Faculty of Computer Science Shri C.J. Patel College of Computer Studies Gujarat Visnagar India University of Technology and Applied Sciences Department of Electronics and Communication Engineering Muscat Oman

ISBN: (纸本)9798350350067

Reliable power system operations, cost reduction, demand and supply balance maintenance, and efficient load scheduling all depend on real-time energy optimisation. Nevertheless, the unpredictability of electric loads and renewable energy sources (RES) makes real-time energy optimization difficult. As a result, the Energy Management Controller (EMC) was developed, which can automatically react to DR signals and resolve energy-related issues. In light of this, EDGE, a hybrid algorithm combining elements of genetic algorithm (GA) and enhanced differential evolution (EDE), is devised in this work. The EDGE algorithm is used to train the EMC to automatically react to DR signals and schedule the 3 classes of household loads - hybrid, non-interruptible, and interruptible - to handle energy management issues. The results show that in terms of power cost reduction, minimizing carbon release, PAR relief, and client discomfort maximization, the suggested EDGE algorithm performs better than benchmark methods. © 2024 IEEE.

关键词： energy optimization renewable energy Smart grid

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A Framework for Blockchain Based E-Voting system for Iraq

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2022年第10期16卷 210-222页

作者： Wahab, Yousif Mohammed Ghazi, Alaan Al-Dawoodi, Aras Alisawi, Muthana Abdullah, Sirwan Saber Hammood, Layth Nawaf, Asmaa Yaseen Computer Science College of Computer Science and Information Technology University of Kirkuk Kirkuk Iraq Gazi University Ankara Turkey Department of Computer Technical Engineering Al-Qalam University College Kirkuk Iraq Faculty of Applied and Human Sciences University Malaysia Perlis Perlis Malaysia Information Technology and Communication Department Rafidain Bank Iraq Altinbas University Istanbul Turkey Computer Science College of Computer Science University of Anbar Kirkuk Iraq

Election is a basic democratic tool that offers an official mechanism for the people to express their opinions in order to form a government by democratic means. Electronic voting has evolved into the most significant application of e-governance and e-democracy. Few countries have recently taken the opportunity to test and use electronic voting systems. Also, many countries expect that internet voting will become a reality during the next decade. In the conventional voting system, the electors authenticate themselves by displaying credentials;this move is open to the public and is validated by poll workers. During this authentication, they are manually checked before being eligible to vote. The standard system of voting is more costly and needs more human capital. Because of these factors, the whole world is heading toward the trend of e-voting. Electronic voting devices are supposed to be the answer to the shortages. A secure e-voting system must meet many criteria, including uniqueness, performance, fairness, stability, safety, authentication, and anonymity. Block-chain is a modern security solution, which is capable of securing e-voting system. Blockchain is distributed ledger. Each block contains only one transaction. Block chain provides security using cryptography and hashing. Blockchain provides a perfect infrastructure to provide secure e-voting ***. Blockchain along with smart contracts need hour to improvise an e-voting system. This paper presents a secure blockchain based framework for an e-electronic voting in Iraq. © 2022

关键词： Smart contract

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A systematic Literature Review of Blockchain Technology

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2022年第10期16卷 97-108页

作者： Ghazi, Alaan Alisawi, Muthana Wahab, Yousif Mohammed Al-Dawoodi, Aras Abdullah, Sirwan Saber Hammood, Layth Nawaf, Asmaa Yaseen Department of Computer Technical Engineering Al-Qalam University College Kirkuk Iraq Faculty of Applied and Human Sciences University Malaysia Perlis Perlis Malaysia Computer Science College of Computer Science and Information Technology University of Kirkuk Kirkuk Iraq Gazi University Ankara Turkey Information Technology and Communication Department Rafidain Bank Iraq Altinbas University Istanbul Turkey Computer Science College of Computer Science University of Anbar Kirkuk Iraq

The Blockchain technology moves data nodes and data streams from one informatics center to another based on the importance of Bitcoins, resulting in a dedicated, public, and secure network for interpretation and development of the ensuing information chain reaction. Blockchains are frequently formed by connecting interrelated patterns of information in order to conduct a secure transaction. Blockchain has the characteristics of decentralization, transparency, immutability, autonomy, open-source, anonymity, and consensus. Block chains are being utilized in a variety of real-world applications, including business and economics, financial technology, and digital currencies. This article does a comprehensive review of the literature on blockchain technology. While searching for the keyword "blockchain" as a subject, we identified a total of 1882 entries in these databases. After removing the less common record kinds, the papers were reduced to 1246 articles, which were then used for further investigation. © 2022

关键词： Smart contract

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NFT-Based Hydroponic Automated Control Using Adaptive Network-Based Fuzzy Inference system 2

NFT-Based Hydroponic Automated Control Using Adaptive Networ...

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2nd International Conference on Robotics, Automation and Artificial Intelligence, RAAI 2022

作者： Surantha, Nico Vincentdo, Vito Bina Nusantara University Binus Graduate Program - Computer Science Jakarta11480 Indonesia Tokyo City University Faculty of Scienc and Engineering Department of Electrical Electronics and Communication Engineering Japan

ISBN: (纸本)9781665459440

The rapid population growth and industrial development in developing countries harm the agricultural sector because many agricultural lands are converted into residential or industrial areas. Applying modern agriculture technologies such as hydroponic could help to overcome the problem. However, hydroponics requires special attention in adjusting the pH and nutrient levels to maximize plant growth, so an automated system is needed to manage the process. In this research, a smart hydroponic system is proposed by applying Adaptive Networkbased Fuzzy Inference system (ANFIS) and Internet-of-Things. The IoT system consists of sensor, actuator, and data processing layer is designed to monitor and control the condition of pH and nutrition of the observed plants. Then, the ANFIS algorithm is designed to control the level of pH and nutrition. The experiment results show that the system can automatically adjust the pH and nutrient levels to the expected range for growing plants, and the fuzzy controller made using ANFIS are more accurate and stable than the fuzzy controller made using Sugeno. This study shows that ANFIS has excellent performance when controlling multiple actuators, as long as the data set has great granularity and well defined. © 2022 IEEE.

关键词： Hydroponics

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Advancing agriculture with machine learning:a new frontier in weed management

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Frontiers of Agricultural science and engineering 2025年第2期12卷 288-307页

作者： Mohammad MEHDIZADEH Duraid K.A.AL-TAEY Anahita OMIDI Aljanabi Hadi Yasir ABBOOD Shavan ASKAR Soxibjon TOPILDIYEV Harikumar PALLATHADKA Renas Rajab ASAAD Department of Agronomy and Plant Breeding Faculty of Agriculture and Natural Resources University of Mohaghegh Ardabili Ilam Science and Technology Park Department of Horticulture College of Agriculture University of Al-Qasim Green Department of GIS and Remote Sensing Faculty of Geography University of Tehran Medical Laboratories Techniques Department Al-Mustaqbal University College Erbil polytechnic university Technical engineering college information system engineering department Department of Fundamental Economics Tashkent State University of Economics Manipur International University Department of Computer Science Nawroz University

Weed management is a crucial aspect of modern agriculture as invasive plants can negatively impact crop yields and profitability. Long-established methods of weed control, such as manual labor and synthetic herbicides, have been widely used but come with their own set of challenges. These methods are often time-consuming, labor-intensive, and pose environmental *** have been the primary method of weed control due to their efficiency and cost-effectiveness. However, over-reliance on herbicides has led to environmental contamination, weed resistance, and potential health hazards. To address these issues, researchers and industry experts are now exploring the integration of machine learning into chemical weed management strategies. As technology advances, there is a growing interest in exploring innovative and sustainable weed management approaches. This review examines the potential of machine learning in chemical weed *** learning offers innovative and sustainable approaches by analyzing large data sets, recognizing patterns, and making accurate *** learning models can classify weed species and optimize herbicide usage. Real-time monitoring enables timely intervention, preventing invasive species spread. Integrating machine learning into chemical weed management holds promise for enhancing agricultural practices, reducing herbicide usage and minimizing environmental impact. Validation and refinement of these algorithms are needed for practical application.

关键词： Weed management herbicides machine learning agricultural practices environmental impact

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A Novel Peak-to-Average Power Ratio Reduction for 5G Advanced Waveforms

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computers, Materials & Continua 2022年第10期73卷 1637-1648页

作者： Rajneesh Pareek Karthikeyan Rajagopal Himanshu Sharma Nidhi Gour Arun Kumar Sami Althahabi Haya Mesfer Alshahrani Mohamed Mousa Manar Ahmed Hamza Department of Computer Science&Engineering JECRC UniversityJaipur303905India Centre for Nonlinear Systems Chennai Institute of TechnologyChennaiIndia Department of Electronics and Communication JECRC UniversityJaipur303905India Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Electrical Engineering Department Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Multi and single carrier waveforms are utilized in cellular systems for high-speed data *** The Fifth Generation(5G)system,several waveform techniques based on multi carrier waveforms are ***,the Peak to Average Power Ratio(PAPR)is seen as one of the significant concerns in advanced waveforms as it degrades the efficiency of the *** proposed article documents the study,progress,and implementation of PAPR reduction algorithms for the 5G radio *** compare the PAPR algorithm performance for advanced and conventional *** simulation results reveal that the advanced Partial Transmission Sequence(PTS)and Selective Mapping(SLM)methods enhanced the throughput and gain of the 5G ***,we have also analyzed the performance of Orthogonal Time Frequency Space Modulation(OTFSM)based on a single carrier system and found that PAPR is significantly low and is best suited to fading *** is seen that the conventional algorithms lower the PAPR but increase the *** proposed PTS and SLM have shown good performance with low computational complexity.

关键词： PAPR 5G OFDM NOMA OTFSM PTS SLM

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Fortifying IoT Against Quantum Threats: Strategies for Securing Connected Devices in the Quantum Era 6

Fortifying IoT Against Quantum Threats: Strategies for Secur...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Rajpoot, Subhadra Singh, Puneet Singh, Pankaj Bahadur, Preeti Singh Dhama, Aditi Amity University Faculty of Applied Science Uttar Pradesh Greater Noida India Military College of Telecommunication Engineering Faculty of Communication Engineering Madhya Pradesh Mhow India Birla Institute of Technology Department of Computer Science and Engineering Rajasthan Pilani India Amity University Departement of Physics Uttar Pradesh Greater Noida India Amity University Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350304480

All data sent to and from IOT devices, (as well as any data held), must be encrypted in order to enhance the security of IOT devices. At the moment, elliptic curve cryptosystems (ECCs) are used as fundamental building blocks to secure communication in the Internet of Things. Yet, forthcoming quantum computers can quickly compromise public key systems like ECC. We should take action right away to get the IOT ready for the quantum future due to recent developments in quantum computing. This paper at first will give a brief overview on Crystal - Kyber, SIKE and BIKE algorithm which are among the NIST's third round candidate. Then the comparison of these three on the basis of key size, computational power and memory requirement. Finally a recommendation on the most suitable algorithm for making IoT secure in quantum world. © 2023 IEEE.

关键词： Comparison Kyber Sike & BIKE

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Low-Resolution Face Recognition for CCTV and Edge-Powered Smart Attendance systems

Low-Resolution Face Recognition for CCTV and Edge-Powered Sm...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Nico Surantha Edward Yose Sani Muhamad Isa Department of Electrical Electronics and Communication Engineering Faculty of Science and Engineering Tokyo City University Tokyo Japan Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

Attendance systems have become more modern, and one of the biometric systems without physical contact is face recognition. However, many face-based attendance systems still carry out attendance individually and cannot detect multiple faces simultaneously. In addition, capturing facial data in real-time is still a challenge because the relatively large distance between the camera and the individual reduces the ability to recognize faces. The general solution is to use super-resolution to generate better-quality faces while maintaining the main facial recognition features. One technique still being researched is super-resolution generative adversarial networks (SRGAN). SRGAN can enlarge the resolution of captured images and maintain image quality sufficient for face recognition. The attendance system can be easily integrated into edge devices such as the Jetson Nano. This paper proposes automatic and effective attendance systems with the super-resolution technique to detect and recognize faces in low-resolution input. The experimental results show that using face data capture with a resolution of 40 × 40 pixels and a four-fold magnification results in a resolution of 160 × 160 pixels. Combining Face SRGAN with FaceNet architecture as the basis of face recognition can achieve an accuracy rate of 78.19% and an F1-Score of 81.13% with an average processing time of 1.61 seconds per frame on a PC and 14.55 seconds per frame on a Jetson Nano at an average of face recognition per frame of as many as up to 8 faces simultaneously.

关键词： Support vector machines Image quality Accuracy Face recognition Image edge detection Superresolution Generative adversarial networks

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Leveraging Data Analytics to Enhance Smart Home Efficiency and Security

Leveraging Data Analytics to Enhance Smart Home Efficiency a...

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Emerging Research in Computational science (ICERCS), International Conference on

作者： Karthi Vivekanandan B. Kaleeswari P L. Natchiammai Arun Anthonisamy R. Vijayabharathi Manjunathan Alagarsamy Department of Electrical and Electronics Engineering Builders Engineering College Tirupur India Department of Electronics and Communication Engineering Easwari Engineering College (Autonomous) Chennai India Department of Electronics and Communication Engineering Mookambigai College of Engineering Pudukkottai India Department of Computer Science and Business System Panimalar Engineering College Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering K. Ramakrishnan College of Technology Trichy India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

Smart home technology has made daily life easier, automated, and managed. IoT devices like smart speakers, cameras, thermostats, and security systems are increasingly integrated, raising security and privacy concerns. This study examines smart home devices’ energy consumption, use trends, device failures, and system efficiency. This research uses smart home devices to identify consumption trends and risks. To better understand smart home ecosystems, the study will evaluate energy consumption, device usage hours, and malfunctions that can affect functionality and security. Using predictive analytics and machine learning, the researchers predict excessive energy usage and device problems. These methods identify trends and irregularities in smart home device operation to improve performance, efficiency, and security. This study investigates how user decisions, usage hours, and device age affect energy consumption, as well as how to identify optimization opportunities. The data analysis study contains statistical summaries and visualisations of device type, usage hours, energy consumption, user preferences, malfunction incidents, and device age. Machine learning algorithms such as linear regression and anomaly detection predict energy usage and locate anomalies. The data illustrate energy and device efficiency trends and recommend improvements to smart home security and effectiveness. This study addresses smart home device vulnerabilities and inefficiencies to improve smart home system optimization. The results offer smart home technology manufacturers and customers ways to improve energy efficiency, device performance, and system security.

关键词： Performance evaluation Energy consumption Data analysis Smart homes Machine learning Market research Energy efficiency Security Object recognition Optimization

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Sleep Monitoring systems based on Edge Computing and Microservices Caching

Sleep Monitoring Systems based on Edge Computing and Microse...

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Artificial Intelligence of Things (AIoT), IEEE Annual Congress on

作者： Nico Surantha David Jayaatmaja Sani Muhamad Isa Faculty of Science and Engineering Department of Electrical Electronics and Communication Engineering Tokyo City University Tokyo Japan Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350392296

ISBN: (纸本)9798350392302

The rapid development of Internet of Things (IoT) technology has enabled the widespread deployment of health monitoring systems. Traditionally, the health monitoring system has been limited by centralized processing and storage in the cloud, leading to latency issues and potential data loss. This paper introduces a smart sleep monitoring system based on edge computing, utilizing microservices architecture and caching techniques. The proposed system employs edge computing to enable data processing closer to the source, reducing latency and improving real-time monitoring capabilities. Caching is employed to reduce database load and optimize random access memory (RAM) usage. This research addresses latency and response time challenges on IoT health monitoring platforms in environments with poor network quality while optimizing database load and resource usage on Jetson Nano as the edge computing device. Using Electrocardiogram (ECG) data as input, the proposed system yields impressive performance metrics. The research results indicate that the proposed system can increase throughput by 26.92 KB/s, reduce response time by 18.8 ms, and decrease latency by 20.86 ms compared to the previous work. Message Queuing Telemetry Transport (MQTT) integration reduces CPU usage by approximately 40% and RAM usage by about 81.24%.

关键词： Cloud computing Databases Random access memory Microservice architectures Real-time systems Nanoscale devices Internet of Things

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