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9th International Conference on Smart City Applications, SCA 2024

作者： Chegri, B.E. Rhajbal, Z. El Mettiti, A. Berhich, A. Saadane, R. Charif, M. Chehri, A. Mohammed V University in Rabat Rabat Morocco LRIT-GSCM Faculty of Sciences University Mohamed V Rabat Rabat Morocco AMIPS Research Team Computer Science Department EMI Mohammed V University in Rabat Rabat Morocco Laboratory Engineering System SIRC/LAGeS EHTP Casablanca Morocco Department of Mathematics and Computer Science Royal Military College Kingston Canada

ISBN: (纸本)9783031886522

The integration of Artificial Intelligence (AI) into sustainable mobility represents a transformative paradigm for transportation systems, particularly within the unique socioeconomic landscape of Morocco. This study delves into the dynamic intersection of AI and sustainable mobility, examining its implications for enhancing efficiency, reducing environmental impact, and improving overall accessibility in Moroccan transportation. Against the backdrop of Morocco’s commitment to sustainable development, the research navigates through the intricate web of challenges and opportunities posed by the fusion of AI technologies and mobility solutions. Through a comprehensive literature review, case studies, and recommendations, this article provides a roadmap for policymakers, researchers, and stakeholders to harness the potential of AI in shaping a more resilient and technologically advanced transportation infrastructure. With a focus on both global best practices and Morocco’s specific needs, the study not only underscores the benefits of AI integration but also addresses ethical considerations and potential socio-cultural implications. As Morocco advances towards a sustainable future, the synergies between AI and mobility emerge as a pivotal force in steering transportation towards efficiency, accessibility, and environmental responsibility. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Autonomous vehicles

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F-CNN: Faster CNN Exploiting Data Re-Use with Statistical Analysis 5

F-CNN: Faster CNN Exploiting Data Re-Use with Statistical An...

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5th IEEE International Conference on Artificial Intelligence Circuits and systems, AICAS 2023

作者： Alantali, Fatmah Halawani, Yasmin Mohammad, Baker Al-Qutayri, Mahmoud Khalifa University System-on-Chip Lab Electrical Engineering and Computer Science Department Abu Dhabi United Arab Emirates University of Dubai College of Engineering & Information Technology Dubai United Arab Emirates

ISBN: (纸本)9798350332674

Many of the current edge computing devices need efficient implementation of Artificial Intelligence (AI) applications due to strict latency, security and power requirements. Nonetheless, such devices, face various challenges when executing AI applications due to their limited computing and energy resources. In particular, Convolutional Neural networks (CNN) is a popular machine learning method that derives a high-level function from being trained on various visual input examples. This paper contributes to enabling the use of CNN on resource-constrained devices offline, where a trade-off between accuracy, running time and power efficiency is verified. The paper investigates the use of minimum pre-processing methods of input data to identify nonessential computations in the convolutional layers. In this work, Spatial locality of input data is considered along with an efficient pre-processing method to mitigate the accuracy loss caused by the computational re-use approach. This technique was tested on LeNet and CIFAR-10 structures and was responsible for 1.9% and 1.6% accuracy loss while reducing the processing time by 38.3% and 20.9% and reducing the energy by 38.3%, and 20.7%, respectively. The models were deployed and verified on Raspberry Pi 4 B platform using the MATLAB coder to measure time and energy. © 2023 IEEE.

关键词： Convolution

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Global mitigation efforts cannot neglect emerging emitters

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National science Review 2022年第12期9卷 82-89页

作者： Can Cui Dabo Guan Daoping Wang Jing Meng Vicky Chemutai Paul Brenton Shaohui Zhang Yuli Shan Qiang Zhang Steven J.Davis Department of Earth System Science Tsinghua University The Bartlett School of Sustainable Construction University College London Department of Computer Science and Technology University of Cambridge Centre for Nature and Climate World Economic Forum School of Urban and Regional Science Shanghai University of Finance and Economics The World Bank School of Economics and Management Beihang University International Institute for Applied Systems Analysis School of Geography Earth and Environmental Sciences University of Birmingham Department of Earth System Science University of California Irvine Department of Civil and Environmental Engineering University of California Irvine

International efforts to avoid dangerous climate change have historically focused on reducing energy-related CO2emissions from countries with either the largest economies（*** EU and the USA）and/or the largest populations（*** and India）.However,in recent years,emissions have surged among a different and much less-examined group of countries,raising concerns that a next generation of high-emitting economies will obviate current mitigation ***,we analyse the trends and drivers of emissions in each of the 59 countries where emissions in 2010-2018 grew faster than the global average（excluding China and India）,project their emissions under a range of longer-term energy scenarios and estimate the costs of decarbonization *** emissions from these 'emerging emitters' reach as much as 7.5 GtCO2/year in the baseline 2.5° scenari—substantially greater than the emissions from these regions in previously published scenarios that would limit warming to 1.5℃ or even 2℃.Such unanticipated emissions would in turn require non-emitting energy deployment from all sectors within these emerging emitters,and faster and deeper reductions in emissions from other countries to meet international climate ***,the annual costs of keeping emissions at the low level are in many cases 0.2%-4.1% of countries' gross domestic production,pointing to potential trade-offs with poverty-reduction goals and/or the need for economic support and low-carbon technology transfer from historically high-emitting *** results thus highlight the critical importance of ramping up mitigation efforts in countries that to this point have been largely ignored.

关键词： emerging emiters developing countries climate change mitigation CO_2 emissions net zero

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Enhancing Quality of Service in IoT-WSN through Edge-Enabled Multi-Objective Optimization

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IEEE Transactions on Consumer Electronics 2025年

作者： Singh, Shailendra Pratap Kumar, Naween Kumar, Gyanendra Balusamy, Balamurugan Bashir, Ali Kashif Al Dabel, Maryam M. Department of Computer Science and Engineering MMMUT UP Gorakhpur India SCSET Bennett University Greater Noida India Department of IoT and Intelligent Systems Manipal University Jaipur Jaipur India India Networks and Security Manchester Metropolitan University United Kingdom Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Saudi Arabia

The demand for real-time, high-quality services (QoS) is increasing with the proliferation of the resource-constrained nature of edge devices that facilitate the Internet of Things (IoT) and wireless sensor network (WSN) applications. Several existing multi-objective algorithms, such as MOPSO, Elitism MOGA, MODE, and others, are capable of balancing exploration and exploitation;they assist in efficient QoS management for WSN-IoT applications, address resource limitations, and align with the objectives of the applications. However, they suffer from showing robustness in the solution and efficient convergence rates on benchmark functions impacting overall QoS. This paper proposes a multi-objective optimization and edge-intelligent adaptation-based strategy to address QoS management issues, jointly optimize several competing objectives, like energy and latency, and maximize localization and coverage rates while considering the limitations of edge devices. The proposed work uses a novel Grey-wolf optimizer (GWO) Algorithm with an innovative bird-edge-computing adaptation approach to analyze the complex connections between input parameters, edge resources, and QoS indicators to generate Pareto-optimal solutions. The evaluation of the proposed edge intelligence technique with IoT applications demonstrates its effectiveness compared to conventional heuristic-based approaches. This approach enhances the QoS in IoT applications and improves resource utilization and scalability in edge computing environments. © 2025 IEEE.

关键词： Quality of service

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Intelligent Transmission Control for Efficient Operations in SDN

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computers, Materials & Continua 2022年第5期71卷 2807-2825页

作者： Reem Alkanhel Abid Ali Faisal Jamil Muzammil Nawaz Faisal Mehmood Ammar Muthanna Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Computer Science University of Engineering and TechnologyTaxila47080Pakistan Department of Computer Science Govt.Akhtar Nawaz Khan(Shaheed)Degree College KTSHaripur22620Pakistan Department of Computer Engineering Jeju National UniversityJeju-siJeju Special Self-Governing Province63243Korea School of Information and Communication Engineering Zhengzhou UniversityChina Department of Telecommunication Networks and Data Transmission The Bonch-Bruevich Saint-Petersburg State University of Telecommunications193232Saint PetersburgRussia Peoples Friendship University of Russia(RUDN University)6 Miklukho-Maklaya St Moscow117198Russian Federation

Although the Software-Defined Network(SDN)is a well-controlled and efficient network but the complexity of open flow switches in SDN causes multiple *** solutions have been proposed so far for the prevention of errors and mistakes in it but yet,there is still no smooth transmission of pockets from source to destination specifically when irregular movements follow the destination host in SDN,the errors include packet loss,data compromise *** accuracy of packets received at their desired destination is possible if networks for pockets and hosts are monitored instead of analysis of network snapshot statistically for the state,as these approaches with open flow switches,discover bugs after their *** article proposes a design to achieve the said objective by defining the Intelligent Transmission Control Layer(ITCL)*** monitors all the connections of end hosts at their specific locations and performs necessary settlements when the connection state changes for one or multiple *** layer informs the controller regarding any state change at one period and controller collects information of end nodes reported via ***,updates flow tables accordingly to accommodate a location-change scenario with a routechange *** is organized on prototype-based implementation using the popular POX *** this paper,it has been discovered that ITCL produces efficient performance in the trafficking of packets and controlling different states of SDN for errors and packet loss.

关键词： Software-defined network ITCL POX open flow

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Design and Implementation of an On-Board system for a Spider Robot 1st

Design and Implementation of an On-Board System for a Spider...

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1st International Conference on Computing and Emerging Technologies, ICCET 2023

作者： Nauman, Nasir Mustafa, Hirra Ashraf, Abrar Khalil, Hisham Department of Computer Science Superior University Lahore Pakistan Department of Computer Science Sharif College of Engineering and Technology Lahore Lahore Pakistan Embedded System and Robotics Lab Department of Technology The University of Lahore Lahore Pakistan

ISBN: (纸本)9783031776199

This paper presents the design of a system onboard spider robot for rescue services. The spider has the six-leg walking pattern that contains the total number of 18 servo motors. In order to reduce the payload, the abdomen of the spider is made with RF4 printed circuit board (PCB). Whereas, the abdomen is also used as the main structure of the robot to attach the rest of the system. Wherein, two pulse-width modulations (PWM) module are controlled by Arduino Nano, the Bluetooth module is used for communication link and bulk converter for the power supply. The spider robot is operated with an in-house developed Android application. It has been observed that the spider robot has a stable walking response along with the multiple excise performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Machine design

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When Computing Power Network Meets Distributed Machine Learning: An Efficient Federated Split Learning Framework

arXiv

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arXiv 2023年

作者： Yuan, Xinjing Pu, Lingjun Jiao, Lei Wang, Xiaofei Yang, Meijuan Xu, Jingdong Institute of Systems and Networks College of Computer Science Nankai University China Department of Computer Science University of Oregon United States School of Computer Science and Technology Tianjin University China

In this paper, we advocate CPN-FedSL, a novel and flexible Federated Split Learning (FedSL) framework over Computing Power Network (CPN). We build a dedicated model to capture the basic settings and learning characteristics (e.g., training flow, latency and convergence). Based on this model, we introduce Resource Usage Effectiveness (RUE), a novel performance metric integrating training utility with system cost, and formulate a multivariate scheduling problem that maximizes RUE by comprehensively taking client admission, model partition, server selection, routing and bandwidth allocation into account (i.e., mixed-integer fractional programming). We design Refinery, an efficient approach that first linearizes the fractional objective and non-convex constraints, and then solves the transformed problem via a greedy based rounding algorithm in multiple iterations. Extensive evaluations corroborate that CPN-FedSL is superior to the standard and state-of-the-art learning frameworks (e.g., FedAvg and SplitFed), and besides Refinery is lightweight and significantly outperforms its variants and de facto heuristic methods under a variety of settings. Copyright © 2023, The Authors. All rights reserved.

关键词： Integer programming

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Exploring Deep Insights from Vast Data: An Overview of Deep Learning Techniques for Big Data

Exploring Deep Insights from Vast Data: An Overview of Deep ...

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International Conference on Electrical Engineering and computer science (ICECOS)

作者： Sarhad Baez Hasan Shahab Wahhab Kareem Didar Dlshad Hamad Ameen Computer Science Department Soran University Faculty of Science Soran Iraq Department of Information System Engineering Department of Computer Engineering Erbil Polytechnic University College of Engineering Knowledge University Erbil Iraq

ISBN: (数字)9798350368253

ISBN: (纸本)9798350368260

Big data has emerged very fast, and this has brought both opportunities and problems that are related to the application of deep learning. This paper explores how deep learning can be implemented using big data and in particular the Apache Mahout library. Unlike most other machine learning tools, Mahout can perform distributed computation, especially while using the MapReduce frameworks including Apache Hadoop and Apache Spark. However, this study does not only focus on the explanation of the need for Mahout's integration and employment but also carries out a high-fidelity argument on how deep learning algorithms can be incorporated in the Mahout framework to optimize the manipulation of large and complicated datasets. Mahout-based contributions include the comparative analysis of key deep learning paradigms including distributed processing, parallel processing and transfer learning. This paper also includes examples of the Mahout deep learning integration use and its application in real contexts. This article describes the challenges that currently organizations face in handling big data and indeed, the potential threats these challenges pose to the success of deep learning techniques when used in organizations’ big data processing and analysis.

关键词： Deep learning Scalability Transfer learning Cluster computing Organizations Big Data Parallel processing Sparks Security Research and development

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Water Wave Optimization with Deep Learning Driven Smart Grid Stability Prediction

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computers, Materials & Continua 2022年第12期73卷 6019-6035页

作者： Anwer Mustafa Hilal Aisha Hassan Abdalla Hashim Heba G.Mohamed Mohammad Alamgeer Mohamed K.Nour Anas Abdelrahman Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University MalaysiaKuala Lumpur53100Malaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science and Bioinformatics Singhania UniversityPacheri BariJhnujhunuRajasthanIndia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Mechanical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11835Egypt

Smart Grid(SG)technologies enable the acquisition of huge volumes of high dimension and multi-class data related to electric power grid operations through the integration of advanced metering infrastructures,control systems,and communication *** SGs,user demand data is gathered and examined over the present supply criteria whereas the expenses are then informed to the clients so that they can decide about electricity *** the entire procedure is valued on the basis of time,it is essential to perform adaptive estimation of the SG’s *** advancements inMachine Learning(ML)andDeep Learning(DL)models enable the designing of effective stability prediction models in *** this background,the current study introduces a novel Water Wave Optimization with Optimal Deep Learning Driven Smart Grid Stability Prediction(WWOODL-SGSP)*** aim of the presented WWOODL-SGSP model is to predict the stability level of SGs in a proficient *** attain this,the proposed WWOODL-SGSP model initially applies normalization process to scale the data to a uniform ***,WWO algorithm is applied to choose an optimal subset of features from the pre-processed ***,Deep Belief Network(DBN)model is followed to predict the stability level of ***,Slime Mold Algorithm(SMA)is exploited to fine tune the hyperparameters involved in DBN *** order to validate the enhanced performance of the proposedWWOODL-SGSP model,a wide range of experimental analyses was *** simulation results confirmthe enhanced predictive results of WWOODL-SGSP model over other recent approaches.

关键词： Smart grid stability prediction deep learning energy systems machine learning metaheursitics

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Modified Snapshot Ensemble Algorithm for Skin Lesion Classification 6th

Modified Snapshot Ensemble Algorithm for Skin Lesion Classi...

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6th International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2023

作者： Parisapogu, Samson Anosh Babu Durga, Mastan Mohammed Meera Reddy, Vallela Kaushik Shashank Chakravarthi, Boyapati Kalyan Sena, P. Vasanth Department of AI and DS Chaitanya Bharathi Institute of Technology Telangana Hyderabad500075 India Department of Computer Science and Engineering Bapatla Engineering College Andhra Pradesh Bapatla522102 India Arista Networks Bellandur Karnataka Bengaluru560103 India Microsoft Telangana Nalagandla500019 India

ISBN: (纸本)9783031530845

Skin cancer prediction has become an essential task in dermoscopic image analysis. For automatic diagnosis of the skin lesions, the Scientific community. The most crucial part in the cure of skin cancer is the exact identification and classification of the skin lesion types. This paper proposes a modified snapshot ensemble algorithm for skin lesion classification. This method utilizes the advantages of transfer learning approach with ResNet50. The method is shown efficient results for the popular metrics, such as accuracy, F1-score, recall and precision. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diseases

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