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IEEE International Symposium on Innovations in Intelligent systems and Applications (INISTA)

作者： Abdelouafi Ikidid Abdelaziz El Fazziki Naima Baba Computer Science Dept Computing Systems Engineering Laboratory Cadi Ayyad University Marrakesh Morocco

Agriculture consumes a significant proportion of water reserves in irrigated areas. Improving irrigation is becoming essential to reduce this high-water consumption by adapting supplies to crop needs and avoiding losses. This global issue has prompted many scientists to think about sustainable solutions using innovative technologies. This study aims to solve the problem of equitable distribution of irrigation water to crops using the PSO (Particle Swarm Optimization) algorithm. The problem involves the allocation of water between N crops, taking into account the priorities of each crop, the minimum and maximum water thresholds required, and the total amount of water available. A precise modeling of the problem is proposed, followed by a description of the PSO algorithm adapted to this situation. The implementation of the algorithm in Python is also presented. The results and performance of the algorithm are discussed, showing its ability to find a fair and efficient solution for irrigation water allocation.

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Adaptive Deep Learning Model to Enhance Smart Greenhouse Agriculture

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computers, Materials & Continua 2023年第11期77卷 2545-2564页

作者： Medhat A.Tawfeek Nacim Yanes Leila Jamel Ghadah Aldehim Mahmood A.Mahmood Department of Computer Science College of Computer and Information SciencesJouf UniversityP.O.Box 72314SkakaSaudi Arabia Department of Computer Science Faculty of Computers and InformationMenoufia UniversityP.O.Box 6121890MenoufiaEgypt Department of Software Engineering College of Computer and Information SciencesJouf UniversityP.O.Box 72314SkakaSaudi Arabia RIADI Laboratory La Manouba UniversityP.O.Box2010ManoubaTunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems and Technology Faculty of Graduate Studies and ResearchCairo UniversityP.O.Box 3753450GizaEgypt

The trend towards smart greenhouses stems from various factors,including a lack of agricultural land area owing to population concentration and housing construction on agricultural land,as well as water *** study proposes building a full farming adaptation model that depends on current sensor readings and available datasets from different agricultural research *** proposed model uses a one-dimensional convolutional neural network(CNN)deep learning model to control the growth of strategic crops,including cucumber,pepper,tomato,and *** proposed model uses the Internet of Things(IoT)to collect data on agricultural operations and then uses this data to control and monitor these operations in real *** helps to ensure that crops are getting the right amount of fertilizer,water,light,and temperature,which can lead to improved yields and a reduced risk of crop *** dataset is based on data collected from expert farmers,the photovoltaic construction process,agricultural engineers,and research *** experimental results showed that the precision,recall,F1-measures,and accuracy of the one-dimensional CNN for the tested dataset were approximately 97.3%,98.2%,97.25%,and 97.56%,*** new smart greenhouse automation system was also evaluated on four crops with a high turnover *** system has been found to be highly effective in terms of crop productivity,temperature management and water conservation.

关键词： Greenhouse wireless sensor network deep learning Internet of Things strategic crops monitoring smart irrigation

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Multi-objective Optimisation of RISC-V CV32A6 for ML application

Multi-objective Optimisation of RISC-V CV32A6 for ML applica...

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IEEE International Symposium on Embedded Multicore Socs (MCSoC)

作者： Bastien Hubert Omar Hammami Computer Science and Systems Engineering Laboratory (U2IS) ENSTA Paris Palaiseau France

RISC-V architectures are rapidly gaining in popularity in embedded systems, in which each mW counts. Since AI-related applications such as image recognition or neural networks tend to be highly energy consuming, low-power techniques are required to optimise the autonomy of systems using SoCs to run such ***, the trade-off between energy consumption, application performance and resource use requires a multi-objective optimisation with a potentially very important number of optimisation parameters to be performed on the SoC. As they rely on heuristics that are likely to only return locally optimal solutions, empirical methods must be *** address this issue, a technology-agnostic mathematical model is introduced to represent how optimisations are applied to a SoC, and a workflow designed to perform an intelligent exhaustive exploration of the optimisation space has been developed to highlight a subset of optimal processor *** of the ARIANE/CV32A6 RISC-V processor, running a CNN propagation on a Xilinx Zynq 7020 FPGA, has shown very encouraging results using low degree configurations, and is likely to perform even better with higher degree configurations.

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Multi-objective Optimisation of RISC-V CV32A6 for ML application

Multi-objective Optimisation of RISC-V CV32A6 for ML applica...

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IEEE International Conference on Design & Test of Integrated Micro & Nano-systems (DTS)

作者： Bastien Hubert Omar Hammami Computer Science and Systems Engineering Laboratory (U2IS) ENSTA Paris Palaiseau France

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Open-Ended Coaxial Probe Based Chloride Concentration Prediction in Water Using Machine Learning Algorithms 7

Open-Ended Coaxial Probe Based Chloride Concentration Predic...

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7th International Youth Conference on Radio Electronics, Electrical and Power engineering, REEPE 2025

作者： Hanima Kannan, C.H. Venugopal, Veena Gupta, Deepa Mathur, Parul Kurup, Dhanesh G. Mathur, Priyanka Amrita School of Engineering Amrita Vishwa Vidyapeetham Rf and Wireless Systems Laboratory Department of Electronics and Communication Engineering Bengaluru India Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Physical Science Bengaluru India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India School of Engineering and Applied Science Benette University UP Greater Noida India

ISBN: (纸本)9798331531836

Monitoring chloride levels in water is essential for environmental health, industrial processes, and safe drinking water, as both high and low concentrations can cause harm for living being. This paper presents a novel approach to detecting chloride concentrations using Open-Ended Coaxial Probe (OECP) sensing combined with machine learning algorithms. OECP sensing offers real-time, non-destructive, and precise monitoring, enabling early detection of contaminants. Scattering parameter (S11) data across a broad frequency range was collected with varying chloride concentrations in controlled laboratory experiments. Thirteen machine learning algorithms were trained to predict chloride concentrations, with different datasets for training and testing to ensure robustness. Among these, CatBoost demonstrated the best performance. After fine tuning and cross-validation, the CatBoost model achieved a test Mean Square Error (MSE) of 0.00627 and Coefficient of Determination (R2) of 0.9936, showcasing its accuracy for applications in water monitoring, corrosion prevention, and safeguarding public health. © 2025 IEEE.

关键词： Chloride detection Machine learning No-destructive testing Open-Ended Coaxial Probe (OECP) Water quality monitoring

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VidEvent: A Large Dataset for Understanding Dynamic Evolution of Events in Videos 39

VidEvent: A Large Dataset for Understanding Dynamic Evolutio...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liang, Baoyu Su, Qile Zhu, Shoutai Liang, Yuchen Tong, Chao School of Computer Science and Engineering Beihang University China State Key Laboratory of Virtual Reality Technology and Systems Beihang University China

ISBN: (纸本)157735897X

Despite the significant impact of visual events on human cognition, understanding events in videos remains a challenging task for AI due to their complex structures, semantic hierarchies, and dynamic evolution. To address this, we propose the task of video event understanding that extracts event scripts and makes predictions with these scripts from videos. To support this task, we introduce VidEvent, a large-scale dataset containing over 23,000 well-labeled events, featuring detailed event structures, broad hierarchies, and logical relations extracted from movie recap videos. The dataset was created through a meticulous annotation process, ensuring high-quality and reliable event data. We also provide comprehensive baseline models offering detailed descriptions of their architecture and performance metrics. These models serve as benchmarks for future research, facilitating comparisons and improvements. Our analysis of VidEvent and the baseline models highlights the dataset’s potential to advance video event understanding and encourages the exploration of innovative algorithms and models. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Organization-wide IOC Monitoring and Security Compliance in Endpoints using Open Source Tools 3

Organization-wide IOC Monitoring and Security Compliance in ...

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3rd IEEE Global Conference for Advancement in Technology, GCAT 2022

作者： Divya, P.J. George, Reenu Sara Madhusudhan, G. Padmasree, S. Jss Science and Technology University Department of Computer Science and Engineering Mysore India Indian Space Research Organization Information Technology Cell Laboratory for Electro-Optics Systems Bengaluru India

ISBN: (数字)9781665468558

ISBN: (纸本)9781665468534

Endpoint Security is an important layer in cyber security and it is one of the first places organizations look to secure their enterprise network. Proactively and iteratively monitoring endpoints for threats and Indicators of Compromise (IOC), helps security analysts to detect malware/attacks and to respond to incidents quickly. An Indicator of Compromise (IOC) refers to data that indicates that a system may have been infiltrated by a cyber-threat. Cyber security agencies send regular cyber-alerts to various organizations nation-wide and provide IOC list in the form of hashes, IPs, URLs, domains, yara rules, etc. for monitoring the presence of IOCs in their respective network. Periodic auditing of endpoints for security compliance is also a challenging and time-consuming task for every organization. To overcome these challenges, a solution based on client-server architecture is proposed in organizational network using open-source tools named Osquery, Fleet, Filebeat and Graylog. Using these tools, endpoints can be monitored for IOCs on demand or on scheduled manner from a central server. Yara based scan can also be performed efficiently in endpoints to monitor for IOCs using this architecture. In addition to IOC monitoring, this paper explains verifying compliance of endpoints with the organization's security policy using the proposed setup. Impact on performance of the endpoints during query execution was also analyzed. This solution will contribute in strengthening the endpoint security which in turn enhances overall cyber-security posture of the organization. © 2022 IEEE.

关键词： Network architecture

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Impact of Mobile Phone Usage on Delivery and Private Riders’ Safety in Marrakech

Impact of Mobile Phone Usage on Delivery and Private Riders...

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Innovative Research in Applied science, engineering and Technology (IRASET),Conference on

作者： Ayoub Charef Zahi Jarir Mohamed Quafafou LAMIGEP Research Laboratory EMSI Moroccan School of Engineering Sciences Marrakech Laboratory of Computer Systems Engineering (LISI) Cadi Ayyad University Marrakech Marrakech Morocco Department of Computer Science University of Aix-Marseille Marseille France

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

This study examines the safety behaviors of delivery and private motorcycle riders in Marrakech, Morocco, with a particular focus on mobile phone distractions. Data were collected over a two-week period at five key intersections, recording 5731 riders during peak traffic hours. Trained observers captured rider characteristics, traffic context, and unsafe behaviors at different times of day morning, noon, and evening to account for variations in traffic conditions. Decision tree analysis was used to explore the relationships between rider type, road conditions, time of day, gender, and mobile phone usage while riding. The findings revealed that private riders were more likely to use mobile phones while riding compared to delivery riders. Unsafe behaviors were influenced by environmental factors, rider demographics, and the timing of the behaviors. Significant differences in behavior were observed between private and delivery riders, with gender also playing a role in safety practices. These results highlight the need for targeted interventions, including educational campaigns to address mobile phone and policies designed for specific rider groups.

关键词： Visualization Head Motorcycles Mobile handsets Weaving Road safety Safety Timing Decision trees Vehicle dynamics

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Comprehensive Overview of Multi-agent systems for Controlling Smart Grids

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CSEE Journal of Power and Energy systems 2022年第1期8卷 115-131页

作者： Om Prakash Mahela Mahdi Khosravy Neeraj Gupta Baseem Khan Hassan Haes Alhelou Rajendra Mahla Nilesh Patel Pierluigi Siano Power System Planning Division Rajasthan Rajya Vidyut Prasaran Nigam Ltd.Jaipur-302005India Media Integrated Communication Laboratory Graduate School of EngineeringOsaka UniversityOsakaJapan Computer Science and Engineering Department Oakland UniversityRochesterUSA Department of Electrical and Computer Engineering Hawassa UniversityHawassa 1530Ethiopia Faculty of Mechanical and Electrical Engineering Tishreen UniversityLattakiaSyria Department of Electrical Engineering National Institute of Technology Kurukshetra Department of Management&Innovation Systems University of Salerno84084 SalernoItaly

Agents are intelligent entities that act flexibly and autonomously and make wise decisions based on their intelligence and experience.A multi-agent system(MAS)contains multiple,intelligent,and interconnected collaborating agents for solving a problem beyond the ability of a single agent.A smart grid(SG)combines advanced intelligent systems,control techniques,and sensing methods with an existing utility power *** controlling smart grids,various control systems with different architectures have already been ***-based control of power system operations has been shown to overcome the limitations of time required for analysis,relaying,and protection;transmission switching;communication protocols;and management of plant *** systems provide an alternative for fast and accurate power network *** paper provides a comprehensive overview of MASs used for the control of smart *** paper provides a wide-spectrum view of the status of smart grids,MAS-based control techniques and their implementation for the control of smart *** of MASs in the control of various aspects of smart grids-including the management of energy,marketing energy,pricing,scheduling energy,reliability,network security,fault handling capability,communication between agents,SG-electrical vehicles,SG-building energy systems,and soft grids—have been critically *** than a hundred publications on the topic of MAS-based control of smart grids have been critically examined,classified,and arranged for fast reference.

关键词： Coordinated control multi-agent systems renewable energy sources smart energy infrastructure smart grid

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FEAT: Towards Fast Environment-Adaptive Task Offloading and Power Allocation in MEC 42

FEAT: Towards Fast Environment-Adaptive Task Offloading and ...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Ren, Tao Hu, Zheyuan He, Hang Niu, Jianwei Liu, Xuefeng Institute of Software Chinese Academy of Sciences Laboratory for Internet Software Technologies Beijing China Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing China Zhongguancun Laboratory Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (纸本)9798350334142

Mobile edge computing (MEC) has been proposed to provide mobile devices with both satisfactory computing resources and latency. Key issues in MEC include task offloading and power allocation (TOPA), for which deep reinforcement learning (DRL) is becoming a popular methodology. However, most DRL-based TOPA approaches are typically developed in a certain environment, lacking the adaptability to unseen environments. Motivated by this, this paper proposes a Fast Environment-Adaptive TOPA (FEAT) approach that could adapt to unseen environments with little fine-tuning. Specifically, we first split MEC states into the internal state and environmental state. Then, based on these two types of states, we develop two main components of FEAT - a group of internal state-dependent TOPA meta-policies and an environmental state-embedded steerer. Meta-policies learn TOPA skills within the internal state space (allowing reusing meta-policies in different environments), while the steerer learns to choose appropriate meta-policies according to embedded environmental states. When encountering an unseen environment with the same internal state space, FEAT only needs to fine-tune the steerer using the newly embedded environmental state with few internal state explorations. Extensive experimental results on simulation and testbed demonstrate that FEAT outperforms the state-of-the-art by more than 16.4% in terms of fine-tuning speeds. © 2023 IEEE.

关键词： Mobile edge computing

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