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REDUCTION OF CO2 EMISSION AS A RESULT OF THE USE OF 48-VOLT ELECTRICAL INSTALLATIONS IN PASSENGER CARS

Archives of Transport 2023年第2期66卷 7-20页

作者： Kozyra, Jacek Lukasik, Zbigniew Kuśmińska-Fijalkowska, Aldona Janota, Aleš Faculty of Transport Electrical Engineering and Computer Science University of Technology and Humanities Radom Poland Faculty of Electrical Engineering and Information Technology University of Žilina Žilina Slovakia

The introduction of a new standard of 48-volt electrical systems in cars comes at an additional cost to the vehicle. Acceptance of these costs is justified because it becomes a way to achieve lower CO2 emissions and lower fuel consumption. An important factor in favor of adopting 48-volt systems is the reduction in CO2 due to the use of a highly efficient 48-volt motor-generator unit coupled to a DC/DC converter. A methodology for testing new solutions to quantify CO2 savings and reductions therefore becomes crucial. This methodology must be capable of demonstrating the CO2 benefits primarily of the innovative technology proven in real-world driving conditions and with a large amount of realistic statistical data. The introduction of new eco-innovations must take into account the linkage and impact on other environmentally oriented ecoinnovative solutions. When implementing new technical solutions, a necessary aspect is the interaction with other innovations installed in vehicles with new electrical installation standards. Therefore, for the expected synergy of solutions to occur, two or more innovative technologies must be installed. Then the combined savings from one of them will affect the performance of the other technologies, and vice versa. The new technology of a high-efficiency 48-volt motor-generator unit cooperating with a 48V/12V DC/DC converter fits very well in creating interactions with other implemented solutions aimed at reducing CO2 emissions. The article discusses the problems of the introduced new technology of a high-efficiency 48-volt motor-generator unit cooperating with a 48V/12V DC/DC converter. The publication analyzes the impact of increasing the voltage rating of current passenger car installations to 48V. Based on the methodology for determining the reduction of CO2 emissions of a vehicle with a 48V/12V DC/DC voltage converter installed, the mass of fuel per unit of engine operation time was determined. The amount of fuel saved was determ

关键词： Carbon dioxide

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An Adaptive Framework for Classification and Detection of Android Malware

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第21期18卷 59-73页

作者： Al Sharah, Ashraf Alrub, Yousef Abu Owida, Hamza Abu Elsoud, Esraa Abu Alshdaifat, Nawaf Khtatnaha, Hamzah Department of Electrical Engineering College of Engineering Technology Al-Balqa Applied University Amman Jordan Department of Computer Information Systems Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Medical Engineering Faculty of Engineering Al-Ahliyya Amman University Amman Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Faculty of Information Technology Applied Science Private University Amman Jordan

The hardware and software of a computer are controlled by its operating system (OS), which performs essential tasks such as input and output processing, file and memory management, and the management of peripheral devices such as disk drives and printers. Application software refers to programs designed for specific purposes, these applications, often freely available and open source, contribute to the rising number of downloads. In the third quarter of 2022, combined downloads from the Apple App Store and Google Play Reached an estimated 35.3 billion. However, the prevalence of insecurity in these applications and technologies heightens the potential for cybercrimes. Protection against unauthorized intruders is crucial in identifying malicious applications. Machine learning (ML) serves as a promising avenue for detecting malware attacks, offering potential solutions to bolster cybersecurity measures. We propose a novel approach utilizing ML to enhance malware detection accuracy by segmenting datasets into distinct groups. Our research employs supervised ML techniques on the CICMaldroid2020 dataset, which includes comprehensive information such as intent actions, permissions, and sensitive APIs. The dataset was partitioned into four groups, each containing 150 features, and analyzed across four experiments to distinguish between attack and benign classes. Our proposed model demonstrated exceptional performance, with the random forest algorithm achieving an accuracy of 98.6% and a precision of 98.75%. These results highlight the effectiveness of our segmentation approach and its significant contribution to advancing malware detection in Android applications, offering a promising direction for future cybersecurity solutions. © 2024 by the authors of this article.

关键词： Android malware

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Multi-Task Learning Model with Data Augmentation for Arabic Aspect-Based Sentiment Analysis

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computers, Materials & Continua 2023年第5期75卷 4419-4444页

作者： Arwa Saif Fadel Osama Ahmed Abulnaja Mostafa Elsayed Saleh Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computer Sciences and EngineeringHodeidah UniversityYemen

Aspect-based sentiment analysis(ABSA)is a fine-grained *** fundamental subtasks are aspect termextraction(ATE)and aspect polarity classification(APC),and these subtasks are dependent and closely ***,most existing works on Arabic ABSA content separately address them,assume that aspect terms are preidentified,or use a pipeline *** solutions design different models for each task,and the output from the ATE model is used as the input to the APC model,which may result in error propagation among different steps because APC is affected by ATE *** methods are impractical for real-world scenarios where the ATE task is the base task for APC,and its result impacts the accuracy of ***,in this study,we focused on a multi-task learning model for Arabic ATE and APC in which the model is jointly trained on two subtasks simultaneously in a *** paper integrates themulti-task model,namely Local Cotext Foucse-Aspect Term Extraction and Polarity classification(LCF-ATEPC)and Arabic Bidirectional Encoder Representation from Transformers(AraBERT)as a shred layer for Arabic contextual text *** LCF-ATEPC model is based on a multi-head selfattention and local context focus mechanism(LCF)to capture the interactive information between an aspect and its ***,data augmentation techniques are proposed based on state-of-the-art augmentation techniques(word embedding substitution with constraints and contextual embedding(AraBERT))to increase the diversity of the training *** paper examined the effect of data augmentation on the multi-task model for Arabic *** experiments were conducted on the original and combined datasets(merging the original and augmented datasets).Experimental results demonstrate that the proposed Multi-task model outperformed existing APC *** results were obtained by AraBERT and LCF-ATEPC with fusion layer(AR-LCF-ATEPC-Fusion)and the proposed data augmentation

关键词： Arabic aspect extraction arabic sentiment classification AraBERT multi-task learning data augmentation

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Machine Learning and Clinical Insights Analysis of BMI Dataset Predictive Models 4

Machine Learning and Clinical Insights Analysis of BMI Datas...

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4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Kadyan, Laksh Neerugatti, Vikram Taranum, Ayesha Rani, Geetha Somasekar, J. Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350393354

Machine learning (ML) has developed at a superlative rate, accompanying requests spanning various fields. This research investigates the experience of strength data, exceptionally the request of machine learning (ML) algorithms to a Body Mass Index (BMI) dataset. The basic aim of searching out unwinds the dossier's many linkages and patterns, eventually chief to more thorough information of the variables deciding BMI. The study starts accompanying an initiation to the subject within reach, understood by a thorough study of appropriate work, a complex mechanics division, and an itemized reason of the reached results. However, because of advances in Machine Learning, we immediately have the talent to handle this issue in a more excellent manner. We've built an advance dossier-study system that can think a patient has diabetes, a suggestion of correction, admitting for early mediation. This predicting plan uses dossier analysis methods to extractable intuitions from a big number of diabetes-accompanying facts. Its basic aim is to correctly determine a patient's risk of diabetes. We've working categorization plans to a degree Decision Tree, Artificial Neural Networks (ANN), Naive Bayes, and Support Vector Machine (SVM) algorithms to cultivate the model. These outcomes show the influence of the subsystems in thinking diabetes risk admit a large size of veracity. This predictive finish can create a meaningful dissimilarity in labeling at-risk things early and providing bureaucracy with essential care and counseling before the ailment progresses. In summary, our machine intelligence-located scheme offers a natural still strong solution to call the risk of diabetes in subjects. By controlling the wherewithal of dossier reasoning and categorization algorithms, we can enhance early discovery and deterrent measures for this weighty affliction, eventually reconstructing patient consequences and reducing the burden of BMI-related complications. © 2024 IEEE.

关键词： Decision trees

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A Fast Algorithm for Computing the Deficiency Number of a Mahjong Hand

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Chinese Journal of Electronics 2024年第6期33卷 1383-1398页

作者： Xueqing YAN Yongming LI Sanjiang LI School of Computer Science Shaanxi Normal University School of Mathematics and Statistics Shaanxi Normal University Centre for Quantum Software and Information Faculty of Engineering and Information TechnologyUniversity of Technology Sydney

The tile-based multiplayer game Mahjong is widely played in Asia and has also become increasingly popular worldwide. Face-to-face or online, each player begins with a hand of 13 tiles and players draw and discard tiles in turn until they complete a winning hand. An important notion in Mahjong is the deficiency number(*** number in Japanese Mahjong) of a hand, which estimates how many tile changes are necessary to complete the hand into a winning hand. The deficiency number plays an essential role in major decision-making tasks such as selecting a tile to discard. This paper proposes a fast algorithm for computing the deficiency number of a Mahjong hand. Compared with the baseline algorithm, the new algorithm is usually 100 times faster and, more importantly,respects the agent's knowledge about available tiles. The algorithm can be used as a basic procedure in all Mahjong variants by both rule-based and machine learning-based Mahjong AI.

关键词： Machine learning algorithms Knowledge based systems Decision making Asia Games

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A Secure and Efficient Information Authentication Scheme for E-Healthcare System

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computers, Materials & Continua 2023年第9期76卷 3877-3896页

作者： Naveed Khan Jianbiao Zhang Ghulam Ali Mallah Shehzad Ashraf Chaudhry Faculty of Information Technology Beijing University of TechnologyBeijing100124China Department of Computer Science Shah Abdul Latif UniversityKhairpur66111Pakistan Department of Computer Engineering Faculty of Engineering ArchitectureNisantasi UniversityIstanbul34398Turkey

The mobile cellular network provides internet connectivity for heterogeneous Internet of Things(IoT)*** cellular network consists of several towers installed at appropriate locations within a smart *** cellular towers can be utilized for various tasks,such as e-healthcare systems,smart city surveillance,traffic monitoring,infrastructure surveillance,or sidewalk *** is a primary concern in data broadcasting,particularly authentication,because the strength of a cellular network’s signal is much higher frequency than the associated one,and their frequencies can sometimes be aligned,posing a significant *** a result,that requires attention,and without information authentication,such a barrier cannot be ***,we design a secure and efficient information authentication scheme for IoT-enabled devices tomitigate the flaws in the e-healthcare *** proposed protocol security shall check formally using the Real-or-Random(ROR)model,simulated using ProVerif2.03,and informally using pragmatic *** comparison,the performance phenomenon shall tackle by the already result available in the MIRACL cryptographic lab.

关键词： IoT-enable device e-healthcare authentication edge computing

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A Review on Role of IoT and Machine Learning in Agriculture 7

A Review on Role of IoT and Machine Learning in Agriculture

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Kumar, Gulshan Choudhary, Vishal Kumar Kumar, Shravan Kumar, Rajan Kumar, Mukesh Gourshettiwar, Palash M. Faculty of Engineering And Technology Department of Computer Science And Design Maharashtra Wardha India Faculty of Engineering And Technology Department of Computer Science And Medical Engineering Maharashtra Wardha India

ISBN: (纸本)9798350359299

The agricultural area has undergone a significant transformation owing to the progress made in IoT. It is imperative to have a dependable remote monitoring solution right now. This study aims to accomplish two goals. First, a solution based on an app is offered, which effectively assists in remotely administering the field by displaying the current sensor values. In an Internet of Things based prototype surveillance system, a multi-class categorization method utilizing machine learning and deep learning is incorporated to classify labels such as clear farm, cow, horse, wild boar, and wild elephant. The analysis involved Support Vector Machines and Convolutional Neural Networks, with the accuracy measure being employed to determine the optimal model. The advancements in the Internet of Things (IoT) have significantly transformed the daily lives of individuals. A comprehensive understanding of soil properties gives a variety of soil-related information that can be used to improve crop yields. Machine learning is a cutting-edge technology that aids with agricultural precision and provides answers to crop yield issues. Significant informative advances and Machine Learning (ML) open up new opportunities for deciphering, quantifying, and comprehending data-intensive prediction in agricultural settings. This study thoroughly examines a number of machine learning topics for Internet of Things-based smart agriculture systems. © 2024 IEEE.

关键词： Support vector machines

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A Comprehensive Review on Data Visualization Techniques for Real-Time Information 7

A Comprehensive Review on Data Visualization Techniques for ...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Choudhary, Vishal Kumar Kumar, Gulshan Suman, Saurabh Kumar, Abhishek Faculty of Engineering And Technology Department of Computer Science And Medical Engineering Maharashtra Wardha India Faculty of Engineering And Technology Department of Computer Science And Design Maharashtra Wardha India

ISBN: (纸本)9798350359299

Research on real-time data visualization methods is necessary to achieve the most accurate and clear representation of information. Creating specific boards and modifying current platforms are two key tasks in performing an appropriate information visualization. An overview of the studies on dashboard platforms, data visualization methods, and accessible strategies is given in this article. To evaluate the effects of the various data visualization approaches on the finished dashboard, a generic and dynamic dashboard based on real-time data was created. Consequently, a preliminary collection of cues, charts, tables, and reports will allow users to interact with the data. produced by the Dashboard itself. Making informed decisions across a variety of businesses requires the capacity to acquire and comprehend real-time information in today's data-driven environment. Real-time data visualization techniques are now essential for turning complex data into insights that can be put to use. This study examines the value of real-time data visualization and offers a summary of strategies and equipment that enable businesses to efficiently use real-time data. © 2024 IEEE.

关键词： Data visualization

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Enhanced Skin Cancer Diagnosis via Deep Convolutional Neural Networks with Ensemble Learning

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SN computer Science 2025年第2期6卷 1-14页

作者： Khan, Mohd Anas Alam, Shahzad Ahmed, Waseem Department of Computer Engineering Faculty of Engineering and Technology Jamia Millia Islamia New Delhi 110025 India

Skin cancer constitutes a third of all cancer diagnoses worldwide, with its incidence steadily increasing over recent decades. The introduction of dermoscopy has significantly improved the diagnostic accuracy for skin cancer. However, dermatologists face considerable challenges in accurately diagnosing skin cancer due to the similar appearances of different types. The diagnostic accuracy among dermatologist’s ranges between 62 and 80%. The research community has achieved significant progress in creating automated systems to aid dermatologists in decision-making. This work presents a very precise automated computer-aided diagnostic (CAD) system for multi-class skin cancer classification. Our suggested strategy improves the accuracy of both skilled dermatologist and existing deep learning techniques in MCS cancer classification. Utilizing the HAM10000 dataset, we fine-tuned models across seven skin cancer classes and conducted a comparative study to evaluate the performance of three pre-trained convolutional neural networks (CNNs) and three ensemble models. Our results demonstrated a maximum accuracy of 92.16% for the best-performing individual model and an impressive 94.17% accuracy for the ensemble model. We suggest for taking advantage of InceptionV3, which for MCS cancer classification owing to its improved architecture and enhanced accuracy. This research underscores the capability of sophisticated deep learning models to improve the diagnostic process for skin cancer, providing a significant resource to assist dermatologists in attaining more precise diagnoses. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks (CNNs) Deep Ensemble Model InceptionV3 Skin cancer

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Industrial revolution 4.0 in water supply, wastewater and stormwater management: opportunities, challenges, and impacts

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Environmental technology Reviews 2024年第1期13卷 144-168页

作者： Kuok, Kuok King Chiu, Po Chan Bakri, Muhammad Khusairy Bin Rahman, Md. Rezaur Yun, Chin Mei Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Campus Kuching Malaysia Faculty of Computer Science and Information Technology Universiti Malaysia Sarawak Kota Samarahan Malaysia Faculty of Engineering Universiti Malaysia Sarawak Kota Samarahan Malaysia

Industrial Revolution 4.0 (IR 4.0) is the revolution that promotes customized and flexible mass production technologies in automation and manufacturing processes. The application of industrial revolution 4.0, such as the Internet of Things (IoT), Cyber-Physical System (CPS), and big data, are used in the water industry to provide better water quality, accurate real-time data, etc. This paper compiles IR 4.0 technology applications, advantages, disadvantages, impacts, and challenges in the water sector, including water supply, wastewater treatment, and stormwater management. It is found that the current water industry is facing a few challenges, such as managing water services, balancing water costs with energy prices, maintaining aging water and wastewater infrastructure, and environmental issues. However, with the application of numerous IR 4.0 innovations, all the challenges can be solved or minimized. Meanwhile, the advantages and impacts of adopting IR 4.0 in water industries are thoroughly discussed. Despite the numerous advantages of IR 4.0, significant drawbacks have restricted the adoption of IR 4.0 in the water industry. The high start-up cost, reluctance, uncertainty, and problems of adopting IR 4.0 are some key challenges that need to be addressed. Generally, IR 4.0 provides more benefits to the community as it helps improve the water system’s performance and efficiencies and creates a more sustainable environment. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Water supply

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