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2023 IEEE East-West Design and Test Symposium, EWDTS 2023

作者： Adam, Shuaibu Musa Musa, Usman Ibrahim Siddique, Kamran Zhang, Jie Wang, Jingchen Hughes, Danny Man, Ka Lok Imec-DistriNet Ku Leuven Belgium School of Computer Applications Lovely Professional University Punjab India University of Alaska Anchorage Department of Computer Science and Engineering AnchorageAK99508 United States School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou215123 China Kazimieras Simonavičius University Lithuania Vytautas Magnus University Faculty of Informatics Lithuania

ISBN: (纸本)9798350314847

Memory latency presents a significant obstruction to computer performance. To hide the memory latency, prefetching mechanism is used to retrieve data from the memory before the processor requests it, anticipating near-term data demands. An effective prefetching scheme can reduce cache miss rates and hide memory latency. Prefetching has yielded major advances in both industry and academia;modern high-performance processors nearly universally implement hardware prefetchers. In this paper, we introduce the fundamental concepts underpinning hardware prefetching and survey state-of-the-art hardware prefetching schemes. We delineate common prefetching approaches, analyse their relative merits and limitations, identify key challenges and open research questions. Overall, this paper aims to provide a comprehensive overview of hardware prefetching that highlights critical trends and technologies in this crucial performance optimisation domain. © 2023 IEEE.

关键词： Cache memory

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Seafloor Imaging Maps by Incorporating Side Scan Sonar Technology on Hybrid Autonomous Underwater Glider

Seafloor Imaging Maps by Incorporating Side Scan Sonar Techn...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Ardian Budi Kusuma Atmaja Egi Muhammad Idris Hidayat Bambang Riyanto Trilaksono Simon Siregar Natsir Habibullah School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Department Computer Engineering Faculty of Applied Science Doctoral Program of Electrical Engineering Institut Teknologi Bandung Telkom University

This research aims to construct a two-dimensional image to represent an underwater geometry map with a Side Scan Sonar (SSS) mounted on a Hybrid Autonomous Underwater Glider (HAUG). Building the underwater map has two stages of the process. The first stage is preprocessing which includes Time-varying gains (TVG), slant range correction, and ground range correction. The second is HAUG navigation, the movement of HAUG orientation to the angle and distance of SSS readings and plotting the geometry of the grid map. With these two stages, a two-dimensional grid map will be formed. Meanwhile, the 3D plot utilizes slant range and ground range correction to obtain the surface height of the bottom of the pool. From the experiments conducted to conduct mapping in the pool, two-dimensional and three-dimensional visual forms of the pool floor and pool walls were produced. The results of the estimated measurement of the width of the pool obtained 14.05 meters and the height of the pool 3.199 meters with an SSS beam angle of 58 degrees.

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Enhancing School and College Attendance Using Advanced Technology

Enhancing School and College Attendance Using Advanced Techn...

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Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Mukti Budiarto Sipah Audiah Eka Dian Astuti Yulia Putri Ayu Sanjaya M. Zaki Firli Faculty of Informatics Engineering University of Raharja Tangerang Indonesia Faculty of Digital Business CAI Sejahtera Indonesia Tangerang Indonesia Faculty of Digital Business Asosiasi Dosen Indonesia Tangerang Indonesia Faculty of Retail Management Alfabet Inkubator Indonesia Tangerang Indonesia Faculty of Computer Systems REY Tangerang Indonesia

ISBN: (数字)9798350367492

ISBN: (纸本)9798350367508

In today digital era traditional manual attendance systems in educational institutions are both inefficient and outdated. This study assesses the impact of implementing technology-driven attendance systems developed using the Waterfall Model, which promises to revolutionize educational settings by enhancing operational efficiency and reducing administrative burdens. The research involved deploying automated attendance software across various institutions and comparing its efficacy with traditional methods in terms of time efficiency, accuracy, and cost. The results indicate that technology-based systems significantly reduce the time and effort required for attendance recording, minimize manual errors, and offer real-time data that helps quickly identify attendance patterns and issues. These improvements lead to enhanced student engagement and accountability. Moreover the transition to automated systems results in considerable administrative cost savings by decreasing reliance on extensive paperwork, thereby allowing for a better allocation of resources towards improving the educational experience. Ultimately, the study confirms that adopting modern attendance monitoring technologies not only streamlines processes but also enhances the educational framework, making institutions more efficient and responsive. This shift benefits administrative and educational outcomes and sets a robust foundation for future educational practices, highlighting the critical role of technology in advancing educational environments.

关键词： Costs Prototypes Manuals Transforms Real-time systems Software Systems support Recording Resource management Monitoring

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Knowledge Management Trends for Measuring Academic Performance: A New Conceptual Framework

Knowledge Management Trends for Measuring Academic Performan...

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International Conference on Electrical engineering and informatics (ICon EEI)

作者： Novi Yanti Sarjon Defit Okfalisa Department of Informatics Engineering Faculty of Science and Technology Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Department of Information Technology Faculty of Computer Science Universitas Putra Indonesia YPTK Padang Padang Indonesia

ISBN: (数字)9798331540791

ISBN: (纸本)9798331540807

Research in the area of knowledge management for improving academic performance has been on the rise in recent years. The effectiveness of knowledge management in improving the quality of decision-making in higher education is a new challenge in the drive for university performance improvement. Therefore, this paper tries to propose one conceptual framework used to measure academic performance in universities. By applying knowledge management process integrated with Business Intelligence System with data-driven analysis concept. This can provide new insights into the theme of academic knowledge management processes for future research opportunities. The model developed is based on comprehensive research developments on research trends and mapping of knowledge management articles and academic performance as well as a systematic literature review. This literature review examines the current state, clusters, and main topics of research related to knowledge management based on authors, sources, cited articles, countries of article authors, and journals that are productive in producing articles. Herein, 429 articles are utilized as metadata taken from the Scopus database published in 2018-2024. Hence, the data analysis of scientific articles is then reviewed using a bibliometric approach and article mapping with using tool VOSviewers to get a network visualization image. The findings of bibliometric analysis point to a rise in publications and a strong level of interest among academics in the field of knowledge management for performance evaluation in higher education institutions.

关键词： Surveys Performance evaluation Databases Bibliometrics Education Data visualization Metadata Market research Knowledge management Regulation

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Secured Heterogeneous Data Fusion Using Adversarial Autoencoders in Internet of Military Things

Secured Heterogeneous Data Fusion Using Adversarial Autoenco...

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Data Science and Business Systems (ICDSBS), International Conference on

作者： A Prabagaran S Kalavathi N M Balamurugan Angelin Blessy J Krishan Berwal John Ayeelyan Gupshup Technology India Pvt Ltd Bengaluru India Department of Computer Science and Engineering Sri Venkateswara College of Engineering Chennai Department of Artificial Intelligence & Machine Learning Rajalakshmi Engineering College Chennai Dept of Computer Science and Engineering Medicaps Univeristy Indore MP India Dept of Informatics Faculty of Communication Engineering Military College of telecommunicationn and Engineering Mhow Indore MP

ISBN: (数字)9798331585600

ISBN: (纸本)9798331585617

Internet of Military Things (IoMT) is the intervention of IoT technology in the Military applications to gain more knowledge on the unmanned areas, the battlefield, and for warfare purposes. As such, IoT, IoMT also uses sensors, communication networks and protocols, and userspecific application processes. However, the IoMT requires more security and integrity in the data collected from the sensor network, as a huge amount of data is required to predict the scenario on which a decision has to be made. On the other hand, as the volume of data increases, it reduces the QoS of the whole system. Heterogeneous data fusion plays a vital role in representing multi-modal data into a singular format that has to be replicable. Data from various sources need to be securely fused so that the used information is highly reliable. Therefore, Adversarial Autoencoder is proposed for heterogeneous data fusion in the IoMT application to reduce the data dimensionality with good integrity and security. The proposed work outperforms the conventional method in terms of evaluation parameters and efficiently detects the threats using the anomaly detection using the IR Small dataset and EMI dataset from Kaggle.

关键词： Protocols Military computing Autoencoders Data integration Quality of service Sensor fusion Sensors Security Reliability Anomaly detection

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A Novel Fuzzy Inference-Based Decision Support System for Crop Water Optimization

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Operations Research Forum 2023年第2期4卷 1-15页

作者： Hasan, Iqbal Srivastava, Azad Khan, Zishan Raza Rizvi, S.A.M. National Informatics Centre Delhi Secretariat IP Estate New Delhi 110003 India Aura Emanating Teknology Pvt Ltd New Delhi 110091 India Department of Civil Engineering Integral University Lucknow New Delhi 226026 India Department of Computer Science Faculty of Natural Science Jamia Millia Islamia New Delhi 110025 India

A logic-based decision support system (DSS) for agriculture support system is presented. The primary focus is on the algorithm used to correctly predict how much water should be poured to the agriculture for the optimal growth of the crops. Over-watering as well as under-watering has always been a big problem in farming. The proposed system uses three input parameters;namely field moisture, field humidity, and field temperature. However, for predicting the proper amount of water so as to get the optimized best growth of the crop, few more parameters also play a vital role, but in this work for simplicity purpose, we have taken these three parameters as input. Mamdani inference engine is used to deduce from the input parameters. Design of the proposed system is given with the fuzzy logic controller and simulation is being done using MATLAB (Matrix Laboratory) for solving the water irrigation issue. The proposed system is simple, using only three parameters (moisture, humidity, and temperature) as input. Through decision support system, the meaning of transferred data is translated into linguistic variables for use by the crop cultivation users. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Decision support systems Fuzzy logic Fuzzy rule base Inference engine Membership functions

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CRISP - DM Process in Smart Building Management

CRISP - DM Process in Smart Building Management

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International Conf on Electrical and Electronic engineering (ICEEE)

作者： Mahir Dursun Moldir Sabyrzhan Dept. of Electrical and Electronics Engineering Faculty of Technology Gazi University Ankara Türkiye Department of Computer Science Graduate School of Informatics Gazi University Ankara Türkiye

ISBN: (数字)9798350362541

ISBN: (纸本)9798350362558

Smart Buildings are buildings that integrate and combine intelligence, management, control, materials, and construction as a single system to meet all the needs of the building, such as comfort, energy efficiency, and durability. In universities, laboratories are used less frequently than other facilities and classrooms. Courses and students may change in the same classroom, and the period of use of such classrooms is much more extended than in laboratories. However, classrooms may only be used for classes during long breaks or for short periods of non-use between exams. In addition, there is no provision for when the heating system will start to heat up before classes or when it will be turned off after classes. By planning heating and lightning, energy and costs will be saved. This paper presents data acquisition methods for preparing a dataset to create a machine learning (ML) model to save energy loss in university laboratories. The developed model will predict energy consumption in classrooms and laboratories based on weather conditions. The study is being conducted in two stages. The first stage describes how to collect data using electronic devices such as Internet of Things (IoT) controllers of Smart Building Management Systems. The second stage gave possibilities for data analysis and preparation using the phases of the Cross-Industry Standard Process for Data Mining (CRISP-DM).

关键词： Heating systems Smart buildings Laboratories Energy conservation Decision making Process control Data models Planning Internet of Things Data mining

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Diesel Engine Fault Detection using Deep Learning Based on LSTM

Diesel Engine Fault Detection using Deep Learning Based on L...

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International Conference on Electrical, Telecommunication and computer engineering (ELTICOM)

作者： Rizqi Fitri Naryanto Mera Kartika Delimayanti Agustien Naryaningsih Bambang Warsuta Rizky Adi Bima Aji Setiawan Department of Mechanical Engineering Faculty of Engineering Universitas Negeri Semarang Indonesia Fisheries Reseach Center National Research and Innovation Agency Department of Computer and Informatics Engineering Politeknik Negeri Jakarta Indonesia

This work aims to create a deep learning model utilizing Long Short-Term Memory (LSTM) as a classification model to detect and diagnose potential problems in diesel engines. The default dataset comprises 3,500 data entries and four distinct dataset types. White noise in the dataset is attributed to additive Gaussian white noise (AWGN). The main contributions of this study comprise developing a classification model aimed at identifying damage in diesel engines, as well as evaluating the performance of the Long Short-Term Memory (LSTM) method. The K-Fold cross-validation technique was utilized, where the value of k was set at 5. This study examines the damage classification in diesel engines using four datasets: default 0dB Noise, 15dB Noise, 30dB Noise, and 60dB Noise. The K-Fold Cross Validation methodology was employed, utilizing a value of k=5. The training and validation processes were conducted iteratively, employing a learning rate of 0.00005 during the training phase. The training phase utilized a mini-batch size of 128, and the maximum number of epochs was set at 10,000. The results indicate that the dataset containing 60dB noise consistently demonstrates the highest level of accuracy, with an average accuracy of 97.91% across the five folds and the efficacy of Long Short-Term Memory (LSTM) models in classification tasks is improved in the presence of white noise.

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Smart Education with DNA Testing: A Pathway to Personalized Academic Success 11

Smart Education with DNA Testing: A Pathway to Personalized ...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Hermanus, Davy Ronald Sasmoko Indrianti, Yasinta Research Interest Group in Educational Technology Bina Nusantara University Computer Science Department Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Research Interest Group in Educational Technology Bina Nusantara University Faculty of Humanities Primary Teacher Education Department Jakarta Indonesia Universitas Agung Podomoro Entrepreneurship Department Jakarta Indonesia

ISBN: (纸本)9798350389654

This paper examines the incorporation of DNA testing into intelligent education systems to develop customized learning plans that respond to the specific needs of each student. Through the examination of distinct genetic indicators associated with personality traits, talent, and inborn abilities, educators can formulate approaches that optimize academic achievement and boost the overall welfare of students. This approach not only enhances learning outcomes but also promotes a supportive and inclusive educational environment. The study critically examines the present state of literature regarding the correlation between genetics and learning. It digs into the ethical implications surrounding this topic and shows case studies that provide evidence of the effectiveness of teaching methods informed by DNA analysis. The results underscore the capacity of DNA testing to transform education by enhancing its adaptability and responsiveness to the distinct genetic composition of individual students. This underscores the need for additional research and ethical guidelines to guarantee responsible integration in educational environments. © 2024 IEEE.

关键词： Federated learning

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A Proposed Machine Learning-Based Model to Improve Iot Systems' Security

SSRN

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SSRN 2023年

作者： El-Sofany, Hosam Sakr, Fatma El-Seoud, Samir A. Karam, Omar H. Taj-Eddin, Islam A.T.F. College of Computer Science King Khalid University Abha Saudi Arabia Cairo Higher Institute for Engineering Computer Science and Management Cairo Egypt Faculty of Computer and Artificial Intelligence Benha University Benha Egypt Faculty of Informatics and Computer Science Cairo Egypt Faculty of Computers and Information Assiut University Assiut Egypt

An intelligent machine network that can work together without human intervention is called the Internet of Things (IoT). The security issues around IoT devices grow in conjunction with their expansion. This work offers a novel method for enhancing the security of IoT-based systems utilizing machine learning (ML) techniques. This work analyzes recent technologies, security, intelligent solutions, and vulnerabilities in ML IoT-based intelligent systems as an essential technology to improve IoT security. It also illustrates the benefits and limits of applying ML in an IoT environment and provides a security model based on ML that manages autonomously the rising number of security issues related to the IoT domain. This work technique contrasted with other ML-based strategies in the field of IoT security. The existing work includes an ML-based spam detection approach for IoT devices. This work model achieved a 99.9% accuracy rate compared to previous research. The study demonstrates that the suggested model outperforms previous ML-based models in terms of execution time and accuracy. © 2023, The Authors. All rights reserved.

关键词： Internet of things

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