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Modeling of Sensor Enabled IrrigationManagement for Intelligent Agriculture Using Hybrid Deep Belief Network

Computer systems Science & engineering 2023年第8期46卷 2319-2335页

作者： Saud Yonbawi Sultan Alahmari B.R.S.S.Raju Chukka Hari Govinda Rao Mohamad Khairi Ishak Hend Khalid Alkahtani JoséVarela-Aldás Samih M.Mostafa Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Aditya Engineering College Affiliated to JNTUK(Kakinada) SurampalemIndia Department of MBA Vignan’s Institute of Information and Technology(A)DuvvadaVisakhapatnamAP530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11564Saudi Arabia SISAu Research Group Universidad Tecnológica IndoaméricaAmbato180103Ecuador Faculty of Computers and Information South Valley UniversityQena83523Egypt

Artificial intelligence(AI)technologies and sensors have recently received significant interest in intellectual *** the application of AI technologies and agriculture sensors in intellectual agriculture is urgently required for the growth of modern agriculture and will help promote smart *** irrigation scheduling systems were highly required in the agricultural field due to their capability to manage and save water deficit irrigation *** learning systems devise an alternative to conventional irrigation management through the automatic elaboration of predictions related to the learning of an *** this motivation,this study develops a modified black widow optimization with a deep belief network-based smart irrigation system(MBWODBN-SIS)for intelligent *** MBWODBN-SIS algorithm primarily enables the Internet of Things(IoT)based sensors to collect data forwarded to the cloud server for examination ***,the MBWODBN-SIS technique applies the deep belief network(DBN)model for different types of irrigation classification:average,high needed,highly not needed,and not *** MBWO algorithm is used for the hyperparameter tuning process.A wideranging experiment was conducted,and the comparison study stated the enhanced outcomes of the MBWODBN-SIS approach to other DL models with maximum accuracy of 95.73%.

关键词： Agriculture smart farming hyperparameter tuning artificial intelligence irrigation management sensors deep learning

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BACKLIGHT-LEAKER: Data Leak from Air-Gapped Computers by Exploiting the LCD Backlight Hardware

BACKLIGHT-LEAKER: Data Leak from Air-Gapped Computers by Exp...

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IEEE International Conference on Microwaves, Communications, Antennas and Electronic systems, COMCAS

作者： Mordechai Guri Dima Bykhovsky Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Israel Department of Electrical and Electronics Engineering Shamoon College of Engineering Israel

ISBN: (数字)9798350348187

ISBN: (纸本)9798350348194

Modern LCD screens are equipped with a special controllable light source at the back of the screen called a backlight. This hardware allows dynamic control of the level of light intensity on the screen, resulting in better contrast ratios. It exists on all modern LCD screens, including desktop, TV, and laptop screens. This paper demonstrates how attackers can exploit air-gapped computers' backlights to exfiltrate sensitive information. Malware on a compromised computer can steal sensitive data and encode it over small changes in screen brightness by controlling the backlight illumination levels. Small changes in screen illumination can be detected within video streams taken by cameras, such as local security or smartphone cameras tens of meters away. The data concealed in the video stream is then demodulated, decoded, and delivered to the attacker. We present technical details and discuss backlight control software and stealth characteristics. We examine the channel's limitations under various parameters and evaluate it with different types of displays and at several distances. We also present relevant countermeasures for this type of optical attack.

关键词： Computers Microwave antennas TV Lighting Streaming media Cameras Liquid crystal displays

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The Balance Model for V2X System with Variable Infrastructure

The Balance Model for V2X System with Variable Infrastructur...

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2025 systems of Signals Generating and Processing in the Field of on Board Communications, SOSG 2025

作者： Plotnikov, Pavel V. Tambovtsev, Gleb I. Vladyko, Andrei G. The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Faculty of Information Technology and Software Engineering Saint Petersburg Russia The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Faculty of Radioelectronic Systems and Robotics Saint Petersburg Russia

ISBN: (纸本)9798331532635

This paper addresses the optimization problem and seeks a balance equation between key performance-influencing parameters in a Vehicle-to-Everything (V2X) network. A novel mathematical optimization model is proposed, focusing on the interaction between On-Board Units (OBUs) and both stationary and mobile Roadside Units (RSUs/mRSUs). The optimization problem is expressed through a multi-objective function considering critical parameters such as data transmission latency, energy consumption, and resource availability. The study demonstrates how RSU density and mobility impact key network performance indicators, including system stability and data loss percentage during peak hours. This work provides a structured analytical approach to optimizing Intelligent Transportation System (ITS) infrastructure, ensuring more reliable and efficient communication between vehicles. © 2025 IEEE.

关键词： Motor transportation

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Data-Driven Goal Recognition in Transhumeral Prostheses Using Process Mining Techniques 5

Data-Driven Goal Recognition in Transhumeral Prostheses Usin...

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5th International Conference on Process Mining, ICPM 2023

作者： Su, Zihang Yu, Tianshi Lipovetzky, Nir Mohammadi, Alireza Oetomo, Denny Polyvyanyy, Artem Sardina, Sebastian Tan, Ying Van Beest, Nick The University of Melbourne School of Computing and Information Systems Australia The University of Melbourne Department of Mechanical Engineering Australia Rmit University School of Computing Technologies Australia Data61 | Csiro Software Systems Group Australia

ISBN: (纸本)9798350358391

A transhumeral prosthesis restores missing anatomical segments below the shoulder, including the hand. Active prostheses utilize real-valued, continuous sensor data to recognize patient target poses, or goals, and proactively move the artificial limb. Previous studies have examined how well the data collected in stationary poses, without considering the time steps, can help discriminate the goals. In this case study paper, we focus on using time series data from surface electromyography electrodes and kinematic sensors to sequentially recognize patients' goals. Our approach involves transforming the data into discrete events and training an existing process mining-based goal recognition system. Results from data collected in a virtual reality setting with ten subjects demonstrate the effectiveness of our proposed goal recognition approach, which achieves significantly better precision and recall than the state-of-the-art machine learning techniques and is less confident when wrong, which is beneficial when approximating smoother movements of prostheses. © 2023 IEEE.

关键词： Metadata

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Smart Diagnostic System for Early Detection and Prediction of Polycystic Ovary Syndrome

Smart Diagnostic System for Early Detection and Prediction o...

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2023 International Conference on Computer Communication and Informatics, ICCCI 2023

作者： Harish, K.P. Dhivyanchali, M.N. Devi, K.Nirmala Krishnamoorthy, N. Dhana Sree, R. Dharanidharan, R. Kongu Engineering College Department of Computer Science & Engineering Erode India Swamy Vivekanandha Medical College Hospital and Research Institute Elayampalayam Tamilnadu Tiruchengode637 205 India Vellore Institute of Technology Vellore Campus School of Information Technology and Engineering Department of Software Systems and Engineering Tamilnadu 632014 India

ISBN: (纸本)9798350348217

PCOS are the serious condition which affects female ovaries during their reproductive age of 15 to 45. This disease affects 5 to 10% of reproductive-age females. Although it is difficult to fully resolve this issue,the PCOS affected women can be mitigated through proper exercise, by taking proper nutritious diet and maintaining the healthy BMI. Until they take a pregnancy test, the majority of women are unaware of the disease. The clinical dataset has the 541 instances and 45 attributes of unbalanced classes of 0 and 1 (no and yes) which has 364 instances of 0 (no) class and 177 instances of 1 (yes) class. Preprocessing is done for the unbalanced dataset by filling the null values and changing the datatype of all attributes to numeric datatypes. The unbalanced dataset is balanced by the balancing techniques of SMOTE and Random Over Sampling. Comparing the both balanced techniques through the accuracy the random oversampling gives the *** supervised learning algorithms are Decision tree, KNN,Random Forest,AdaBoost, Logistic regression,Gradient boosting, cat boosting, XGBoosting, Linear SVM, Radial SVM, Linear discriminant analysis and Quadratic discriminant analysis are *** supervised learning algorithms are trained and tested by splitting the dataset to 70% for training and 30% for testing. The ensemble stacking techniques are used by implementing the all models at the cross validation of *** xgboost gives the accuracy of 96% for the balanced dataset. © 2023 IEEE.

关键词： Adaptive boosting

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Maturity Evaluation of Domain-Specific Language Ecosystems for Cyber-Physical Production systems 28

Maturity Evaluation of Domain-Specific Language Ecosystems f...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Greiner, Sandra Wiesmayr, Bianca Feichtinger, Kevin Meixner, Kristof Konersmann, Marco Pfeiffer, Jérôme Oberlehner, Michael Schmalzing, David Wortmann, Andreas Rumpe, Bernhard Rabiser, Rick Zoitl, Alois University of Bern Software Engineering Group Switzerland Technische Universität Wien CDL SQI Institute of Information Systems Engineering Austria RWTH Aachen University Software Engineering Germany Germany Johannes Kepler University Linz LIT CPS Lab Austria Johannes Kepler University Linz CDL VaSiCS LIT CPS Lab Austria

ISBN: (纸本)9798350339918

engineering Cyber-Physical Production systems (CPPSs) heavily relies on Domain-Specific Languages (DSLs), which are tailored to a specific class of problems inherent to CPPSs. DSLs enable non-programming experts to solve problems in their domain, such as modeling production processes, implementing control software, or managing the variability of production systems. A DSL ecosystem encompasses the entire infrastructure (e.g., libraries and tools) built around its language and contributes to the successful and easy adoption thereof by domain experts. We present a maturity evaluation model for DSL ecosystems serving two aims: to developers, it reveals missing but essential aspects of the ecosystem, and users (e.g., industrial companies) can evaluate the maturity of a DSL ecosystem. We propose criteria to evaluate the maturity of DSL ecosystems and apply them to existing, publicly available DSLs which have been adopted in CPPSs. The results demonstrate that all components of the model are covered and they allow for deriving hypotheses about DSL ecosystems used in CPPSs. © 2023 IEEE.

关键词： Ecosystems

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Exploring Ambidiguity: Ui Item Direction Interpretation by Arabic and Hebrew Users

SSRN

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SSRN 2024年

作者： Goldenberg, Yulia Tractinsky, Noam Software and Information Systems Engineering Department Ben-Gurion University Beer Sheva8410501 Israel

Bidirectional user interfaces serve more than half a billion users worldwide. Despite increasing diversity-driven approaches to interface development, bidirectional interfaces still use UI elements inconsistently. In particular, professionals should investigate UI items containing ambiguous information that can be processed both from right-to-left and left-to-right by BiDi users. We use the term amBiDiguous to denote such items and suggest that they are susceptible to ineffective *** paper reports on an empirical study with 1705 Arabic and Hebrew users, in which we collected explicit and implicit data about ambiguous UI items in bidirectional interfaces. We explored the directional interpretation of amBiDiguous UI items and investigated the influence of individual, linguistic, and UI design factors on how people perceive them. The findings suggest a complex picture in which various factors affect ambiguous items’ interpretation. While the analysis indicates that preventing all interpretation errors is probably impossible, a large portion of those errors can be addressed by proper design. © 2024, The Authors. All rights reserved.

关键词： User interfaces

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ThyroidNet:A Deep Learning Network for Localization and Classification of Thyroid Nodules

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Computer Modeling in engineering & Sciences 2024年第4期139卷 361-382页

作者： Lu Chen Huaqiang Chen Zhikai Pan Sheng Xu Guangsheng Lai Shuwen Chen Shuihua Wang Xiaodong Gu Yudong Zhang Ultrasonic Department Zhongda Hospital Affiliated to Southeast UniversityNanjing210009China School of Physics and Information Engineering Jiangsu Second Normal UniversityNanjing211200China School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia State Key Laboratory of MillimeterWaves Southeast UniversityNanjing210096China Jiangsu Province Engineering Research Center of Basic Education Big Data Application Jiangsu Second Normal UniversityNanjing211200China School of Software Engineering Quanzhou Normal UniversityQuanzhou362000China Department of Biological Sciences Xi’an Jiaotong-Liverpool UniversitySuzhou215123China

Aim:This study aims to establish an artificial intelligence model,ThyroidNet,to diagnose thyroid nodules using deep learning techniques ***:A novel method,ThyroidNet,is introduced and evaluated based on deep learning for the localization and classification of thyroid ***,we propose the multitask TransUnet,which combines the TransUnet encoder and decoder with multitask ***,we propose the DualLoss function,tailored to the thyroid nodule localization and classification *** balances the learning of the localization and classification tasks to help improve the model’s generalization ***,we introduce strategies for augmenting the ***,we submit a novel deep learning model,ThyroidNet,to accurately detect thyroid ***:ThyroidNet was evaluated on private datasets and was comparable to other existing methods,including U-Net and *** results show that ThyroidNet outperformed these methods in localizing and classifying thyroid *** achieved improved accuracy of 3.9%and 1.5%,***:ThyroidNet significantly improves the clinical diagnosis of thyroid nodules and supports medical image analysis *** research directions include optimization of the model structure,expansion of the dataset size,reduction of computational complexity and memory requirements,and exploration of additional applications of ThyroidNet in medical image analysis.

关键词： ThyroidNet deep learning TransUnet multitask learning medical image analysis

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Time Series Processing-Based Malicious Activity Detection in Scada systems

SSRN

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SSRN 2024年

作者： Zaslavski, Michael Kalech, Meir Cyber Security Research Center Software and Information Systems Engineering Ben-Gurion University of the Negev

Many critical infrastructures, essential to modern life, such as oil and gas pipeline control and electricity distribution, are managed by SCADA systems. In the contemporary landscape, these systems are interconnected to the internet, rendering them vulnerable to numerous cyber-attacks. Consequently, ensuring SCADA security has become a crucial area of research. This paper focuses on detecting attacks that manipulate the timing of commands within the system, while maintaining their original order and content. To address this challenge, we propose several machine-learning-based methods. The first approach relies on Long-Short-Term Memory model, and the second utilizes Hierarchical Temporal Memory model, both renowned for their effectiveness in detecting patterns in time-series data. We rigorously evaluate our methods using a real-life SCADA system dataset and show that they outperform previous techniques designed to combat such attacks. © 2024, The Authors. All rights reserved.

关键词： Intrusion detection

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Multimodal Fusion for Enhanced Parkinson's Disease Screening: Integrating Brain MRI, Hand Drawing, Facial Expressions, and Voice Analysis 5

Multimodal Fusion for Enhanced Parkinson's Disease Screening...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Warnakulasuriya, Naveen De Silva, Shalinika Madushika, Janani Gamage, Sadali Jayawardena, Sanath Karunasena, Anuradha Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Parkinson's disease (PD) profoundly impacts millions in Sri Lanka, emphasizing the importance of early detection for better patient outcomes. We introduce 'NeuraTrace PD,' an innovative application for early PD screening, combining brain MRI, hand-drawn image analysis, facial expression assessment, and voice analysis. For brain MRI analysis, we employed a Graph Convolutional Network (GCN), achieving an impressive accuracy of 91% and identifying distinct graph connectivity patterns using graph theory. We also use a deep Convolutional Neural Network (CNN) to reach 90% accuracy in analyzing hand-drawn images, while optimizing image resolution for efficiency and information retention. Facial expression analysis is a vital component, leveraging CNN and Constrained Local Models (CLM) to accurately extract Facial Action Units (FAUs), which contribute to a remarkable 95% accuracy using the XGBoost machine learning model. We introduce a Machine Learning (ML) algorithm and advanced signal processing to enhance voice feature analysis, offering a thorough understanding and classification of PD patients. It reaches 99% accuracy. In summary, our approach holds significant promise for advancing early PD diagnosis, ultimately improving the quality of life for those affected by this challenging condition. © 2023 IEEE.

关键词： Image resolution

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