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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Altigani, Abdelrahman Ghalib, Muhammad Rukunuddin Elshoush, Huwaida T. Elsadig, Muawia A. Alrefai, Nashat Naserelden, Shiraz Higher Colleges of Technology Computer Information Science Al Ain United Arab Emirates School of Engineering & Computing De Monfort University Dubai United Arab Emirates University of Khartoum Faculty of Mathematical Sciences and Informatics Computer Science Department Khartoum Sudan College of Computer Science and Information Technology Imam Abdulrahman bin Faisal University Dammam Saudi Arabia College of Science Imam Abdulrahman Bin Faisal University Basic and Applied Scientific Research Center Imam Abdulrahman Bin Faisal University Department of Mathematics Dammam Saudi Arabia Faculty of Science Universiti Teknologi Malaysia Department of Mathematical Sciences Johor Malaysia

ISBN: (纸本)9798350395914

Since the inception of secure communication, safeguarding data confidentiality has been paramount. With the increased reliance on technology, the need for robust security mechanisms is more critical than ever. This paper explores confidentiality in cybersecurity, focusing on symmetric encryption algorithms. While symmetric encryption, particularly the Advanced Encryption Standard (AES), is in wide use, concerns arise regarding its static nature. Therefore, the paper introduces a dynamic cipher, augmenting AES security by introducing a key-dependent S-Box. By leveraging the RC4 key schedule, the proposed cipher generates unique S-Boxes for each encryption key, significantly increasing security against non-generic attacks. Theoretical analysis demonstrates the proposed cipher is capable of resisting brute force, linear cryptanalysis, and differential cryptanalysis. Furthermore, its adaptability to existing AES implementations ensures practicality and ease of adoption. © 2024 IEEE.

关键词： Ciphertext

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Image Based Feature Separation Using RBM Tech with ADBN Tech for Accurate Fruit Classification 5

Image Based Feature Separation Using RBM Tech with ADBN Tech...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Revathi, V. Kavin, Balasubramanian Prabhu Thirumalraj, Arunadevi Gangadevi, E. Balusamy, Balamurugan Gite, Shilpa New Horizon College of Engineering Department of Applied Sciences - Physics Ring Road Bellandur Post Bengaluru India School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Chengalpattu Tamil Nadu Kattankulathur603 203 India K.Ramakrishnan College of Technology Department of Computer Science Engineering Tamil Nadu Trichy India Loyola College Department of Computer Science Chennai600034 India Delhi India Symbiosis Institute of Technology Pune Aiml Department Pune412115 India

ISBN: (纸本)9798350383522

One of the fastest-growing subfields of computer vision and picture categorization is fruit identification. The fruit industry could start using a standardised categorization system so that shoppers can easily distinguish between different types and quality levels of fruit. From a health perspective, fruit quality is essential. The task of classifying fruits is crucial in many commercial contexts. A supermarket cashier may use a fruit categorization system to better recognise the varieties of fruit and their associated costs. It might also be used to determine whether or not a given kind of fruit is suitable for a person's diet. The rising use of intelligent imaging equipment may be directly attributed to the proliferation and development of AI software. Deep learning methods, such as CNN, are now being used by researchers for picture categorization. CNN's superior performance may be achieved without using any manually-crafted characteristics, in contrast to the older methods. For automated categorization, it employs a plethora of filters that strip relevant visual information. Classifying fruits accurately is a challenge in the horticulture sector and calls for the knowledge of an expert. For accurately and quantitatively analysing fruits, the currently mentioned categorization techniques fall short. Therefore, it is beneficial to review the latest suggestions for categorizing fruits. To solve this problem, we need a computerised system that can automatically sort fruits into their respective categories. Deep learning models, such as the Restricted Boltzmann Machine (RBM) and the Adam based Deep Belief Network (DBN), are used in this assignment to categorise fruits according to the attributes that have been determined to be optimum and generated. The proposed approach has been shown to have effective accuracy and quantitative analysis outcomes, and this is just the beginning of this realisation. In addition, the suggested scheme's relatively strong computational mo

关键词： Deep Belief Network Fruits Classification Fruits Grading Handcrafted features Restricted Boltzmann Machine

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Eco-Friendly IoT Solutions for Smart Cities Development: An Overview 1

Eco-Friendly IoT Solutions for Smart Cities Development: An ...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： El-Mawla, Nesma Abd Badawy, Mahmoud Nile Higher Institute for Engineering and Technology Communication and Electronics Engineering Department Mansoura Egypt Taibah University Applied College Computer Science and Information Department Medinah41461 Saudi Arabia

ISBN: (纸本)9781665472753

The last three years showed global systems' fragility in different disciplines, such as medicine, travel, and trade. Moreover, environmental changes are still happening in extreme weather phenomena, exacerbating COVID-19 in the world health sector. Therefore, the world needs to work hard in both traditional and modern technological ways to develop smart solutions for the development of smart cities to create open, interconnected networks for everyone. The focus of intense research worldwide is the development of standardized eco-friendly sensor networks enabled by IoT technology. This study presents a comprehensive overview of the challenges facing the world and humans in the current era and how modern technologies, including the Internet of Things (IoT), can make a real change in smart city development and save the global environment. These technological solutions and frameworks help to achieve innovative administration, high adaptability, better efficiency, and productivity. In particular, the paper spots light on top environmental challenges according to impact and likelihood classifications. Also, it discusses recent green technology solutions for smart city development in many applications, such as e-waste management, climate change adaptation, water crises, and natural disaster management. © 2023 IEEE.

关键词： Internet of things

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Linux-Based End-Station Design for Seamless TSN Plug -and - Play

Linux-Based End-Station Design for Seamless TSN Plug -and - ...

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International Conference on Advanced Systems and Electric Technologies (IC_ASET)

作者： Hamza Chahed Fredrik Hallström Héctor Blanco Alcaine Andreas Kassler Department of Mathematics and Computer Science Karlstad University Karlstad Sweden Intel Deutschland GmbH Germany Faculty of Applied Computer Science Deggendorf Institute of Technology Deggendorf Germany

ISBN: (数字)9798350384895

ISBN: (纸本)9798350384901

In TSN networks, proper end-station configuration is essential to ensure the timely and reliable delivery of time-sensitive data, meeting strict end- to-end Quality of Service (QoS) criteria. However, the complexity of the configuration process requires a significant manual effort, which makes real-time application development on standard Operating Systems such as Linux a challenge. In this paper, we propose a sim-ple yet functional approach to automate the configuration of Linux-based TSN end-stations within TSN networks by adding a TSN layer on top of the networking system services and defining a configuration protocol tailored for the centralized network/distributed user configuration mode. Evaluation results demonstrate minimal overhead during stream addition, achieving hundreds-of-millisecond-Ievel configuration times and enabling a hassle-free Plug-and-Play mode of operation.

关键词： Protocols Linux Operating systems Quality of service Reliability engineering Software Real-time systems

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Cumulants from short-range correlations and baryon number conservation at next-to-leading order

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Physical Review C 2023年第3期107卷 034914-034914页

作者： Michał Barej Adam Bzdak AGH University of Science and Technology Faculty of Physics and Applied Computer Science 30-059 Kraków Poland

We calculate the baryon number cumulants within acceptance with short-range correlations and global baryon number conservation in terms of cumulants in the whole system without baryon conservation. We extract leading and next-to-leading order terms of the large baryon number limit approximation. These approximations are checked to be very close to the exact results.

关键词： Particle correlations & fluctuations Relativistic heavy-ion collisions

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An Almost Singularly Optimal Asynchronous Distributed MST Algorithm 36

An Almost Singularly Optimal Asynchronous Distributed MST Al...

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36th International Symposium on Distributed Computing, DISC 2022

作者： Dufoulon, Fabien Kutten, Shay Moses, William K. Pandurangan, Gopal Peleg, David Department of Computer Science University of Houston HoustonTX United States Faculty of Industrial Engineering and Management Technion - Israel Institute of Technology Haifa Israel Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot Israel

ISBN: (纸本)9783959772556

A singularly (near) optimal distributed algorithm is one that is (near) optimal in two criteria, namely, its time and message complexities. For synchronous CONGEST networks, such algorithms are known for fundamental distributed computing problems such as leader election [Kutten et al., JACM 2015] and Minimum Spanning Tree (MST) construction [Pandurangan et al., STOC 2017, Elkin, PODC 2017]. However, it is open whether a singularly (near) optimal bound can be obtained for the MST construction problem in general asynchronous CONGEST networks. In this paper, we present a randomized distributed MST algorithm that, with high probability, computes an MST in asynchronous CONGEST networks and takes Õ(D1+Ε + √n) time and Õ(m) messages1, where n is the number of nodes, m the number of edges, D is the diameter of the network, and Ε > 0 is an arbitrarily small constant (both time and message bounds hold with high probability). Since (Equation presented)(D + √n) and Ω(m) are respective time and message lower bounds for distributed MST construction in the standard KT0 model, our algorithm is message optimal (up to a polylog(n) factor) and almost time optimal (except for a DΕ factor). Our result answers an open question raised in Mashregi and King [DISC 2019] by giving the first known asynchronous MST algorithm that has sublinear time (for all D = O(n1-Ε)) and uses Õ(m) messages. Using a result of Mashregi and King [DISC 2019], this also yields the first asynchronous MST algorithm that is sublinear in both time and messages in the KT1 CONGEST model. A key tool in our algorithm is the construction of a low diameter rooted spanning tree in asynchronous CONGEST that has depth Õ(D1+Ε) (for an arbitrarily small constant Ε > 0) in Õ(D1+Ε) time and Õ(m) messages. To the best of our knowledge, this is the first such construction that is almost singularly optimal in the asynchronous setting. This tree construction may be of independent interest as it can also be used for efficiently perfor

关键词： Distributed computer systems

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Investigating Trainee Perspectives on Virtual Reality Environments: An In-Depth Examination of Immersive Experiences with Haptic Feedback Vibration

Investigating Trainee Perspectives on Virtual Reality Enviro...

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Annual IEEE Systems Conference

作者： Ourania K. Xanthidou Nadine Aburumman Hanêne Ben-Abdallah Computer Science Brunel University London London U.K. Computer Information Science & Applied Media Higher Colleges of Technology Dubai U.A.E.

ISBN: (数字)9798350358803

ISBN: (纸本)9798350358810

This research investigates trainee reflections on Virtual Reality Environments (VREs) within educational training centers, aiming to understand their experiences, perceptions, and preferences. The study focuses on the impact of haptic feedback vibrations, examining both their general effects during VRE interactions and the specific influence of adaptable vibration configurations triggered by user errors. A convenience sample of 81 participants/trainees, 41 from the computer science and 40 from the aviation engineering departments of a major higher education institution in the U.A.E., was used to run four variations of the same VRE, with two from each field. Results indicate that participants largely embraced the VRE experience, reporting feelings of contentment, joy, and competence. Haptic feedback, particularly in non-adaptable forms, was acknowledged as enhancing the immersive experience. However, the study suggests that further research is needed to explore the nuanced role of adaptable vibration, especially in more complex interactions. Notably, participants expressed a preference for a blended approach, advocating for both VREs and physical labs in their training. The study acknowledges limitations, such as the predominantly single-user focus, and recommends future research extensions into collaborative VRE settings, more intricate interactions, and potential technical issues in multi-user scenarios. Overall, this research sheds light on the evolving landscape of educational training, emphasizing the importance of understanding trainee perspectives to optimize the integration of VREs in learning environments.

关键词： Vibrations Training computer science Collaboration Immersive experience Reflection Haptic interfaces

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Real-Time Scheduling for Energy Optimization: Smart Grid Integration with Renewable Energy 7

Real-Time Scheduling for Energy Optimization: Smart Grid Int...

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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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Automatic Modulation Recognition with Breast Cancer Classification for Medical Internet of Things Applications

Automatic Modulation Recognition with Breast Cancer Classifi...

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

作者： V. Nirmalrani D. Poornima S. Gowri Kumaresan Chandrasekaran Department of Computer Science and Engineering Sathyabama Institute of Science & Technology Chennai India Department of Computer Science and Engineering University of Technology and Applied Sciences Sultanate of Oman

Medical Internet of Things (MIoT) and healthcare have lately converged, which has led to novel techniques to identifying and treating medical troubles. This research gives a completely unique method that utilizes the Internet of Things (IoT) to combine Automatic Modulation Recognition (AMR) with Breast Cancer (BC) categorization. The loss of included answers arises from the reality that most current research ignores BC type in desire for both AMR or BC type. A unique Medical Internet of Things-based totally Breast Cancer (MIoT-BC) category method addresses the project. In order to facilitate automatic modulation identity, the proposed gadget first statistics indicators from clinical gadget and wireless verbal exchange channels. After that, it uses DBNs (deep belief networks) to categorize BCs. The MIoT-primarily based strategy ensures the secure transmission and instantaneous analysis of scientific facts, which improves the precision and timeliness of diagnosis. There are some of blessings to the use of MIoT for BC categorization, such as less difficult get entry to to health information, faster diagnostic instances, and the option for faraway monitoring. In addition, the potential for misclassification and human errors is reduced, making this a useful tool inside the realm of medical diagnostics. DBNs are used to give a dependable and correct type version for BC categorization. Several healthcare packages have found achievement with DBNs due to their suitability for processing excessive-dimensional, complicated scientific facts. Differentiating benign from malignant breast lesions is an area where their capability to extract sizeable facts from enter facts robotically shines. MIoT-primarily based processes the usage of DBNs enhance BC classification accuracy in comparison to traditional techniques, consistent with this paper. Evaluation criteria, such as sensitivity, specificity, accuracy, overall performance and F1 rating, measure the effectiveness of the suggeste

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Prediction of Hypertension Disease Using Machine Learning Techniques: Case Study from Jordan 25

Prediction of Hypertension Disease Using Machine Learning Te...

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25th International Arab Conference on Information technology, ACIT 2024

作者： Alazaidah, Raed Samara, Ghassan Katrawi, Anwar Hadi, Wael Al-Safarini, Maram Y. Al-Mamoori, Farah Elrashidi, Ali Alqaraleh, Muhyeeddin Alzoubi, Haneen Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Zarqa University Faculty of It Department of Computer Science Zarqa Jordan Petra University Faculty of It Information Security Department Amman Jordan Zarqa University Faculty of Pharmacy Department of Pharmaceutical Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Zarqa University Faculty of It Department of Software Engineering Zarqa Jordan Applied Science Private University Facultty of Information Technology Department of Computer Science Amman Jordan

ISBN: (纸本)9798331540012

Hypertension is one of the most common diseases in Jordan. It is one of the main reasons of death among Jordanian adult citizens. Worldwide, nearly 13% of all deaths are due to Hypertension with nearly 8 million deaths yearly. Hence, this research is interested in the early prediction of this disease among Jordanian people. To achieve this main objective, Machine Learning (ML) is utilized through a large number of classification models and considering five well-known evaluation metrics. These classification models have been trained on a primary dataset that has been collected for the purpose of the research. The results revealed that Random Forest, Random Committee, and MultilayerPerceptron are the best classification models to handle the task of the early prediction of Hypertension in Jordan. © 2024 IEEE.

关键词： Prediction models

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