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Fruits and Vegetable Diseases Recognition Using Convolutional Neural Networks

Computers, Materials & Continua 2022年第1期70卷 619-635页

作者： Javaria Amin Muhammad Almas Anjum Muhammad Sharif Seifedine Kadry Yunyoung Nam University of Wah WahCanttPakistan National University of Technology(NUTECH) IJP RoadIslamabadPakistan COMSATS University Islamabad Wah CampusPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

As they have nutritional,therapeutic,so values,plants were regarded as important and they’re the main source of humankind’s energy *** pathogens will affect its leaves at a certain time during crop cultivation,leading to substantial harm to crop productivity&economic selling *** the agriculture industry,the identification of fungal diseases plays a vital ***,it requires immense labor,greater planning time,and extensive knowledge of plant *** approaches are developed and tested by different researchers to classify plant disease identification,and that in many cases they have also had important results several ***,the proposed study presents a new framework for the recognition of fruits and vegetable *** work comprises of the two phases wherein the phase-I improved localization model is presented that comprises of the two different types of the deep learning models such asYouOnly Look Once(YOLO)v2 and Open Exchange Neural(ONNX)*** localizationmodel is constructed by the combination of the deep features that are extracted from the ONNX model and features learning has been done through the convolutional-05 layer and transferred as input to the YOLOv2 *** localized images passed as input to classify the different types of plant *** classification model is constructed by ensembling the deep features learning,where features are extracted dimension of 1×1000 from pre-trained Efficientnetb0 model and supplied to next 07 layers of the convolutional neural network such as 01 features input,01 ReLU,01 Batch-normalization,02 *** proposed model classifies the plant input images into associated labels with approximately 95%prediction scores that are far better as compared to current published work in this domain.

关键词： Efficientnetb0 open exchange neural network features learning softmax YOLOv2

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Securing Autonomous Vehicles with Smart AI Security Agents

Securing Autonomous Vehicles with Smart AI Security Agents

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International Conference on New Trends in computing Sciences (ICTCS)

作者： Haidar Jabbar Samir Al-Janabi Francis Syms Faculty of Applied Science and Technology Humber Polytechnic Toronto Canada Department of Computing and Software McMaster University Hamilton Canada

ISBN: (数字)9798331523411

ISBN: (纸本)9798331523428

As Autonomous Vehicles (AVs) become increasingly integrated into modern transportation systems, they face rising exposure to complex cybersecurity threats. Existing security frameworks, based on static rules, signature detection, and conventional Artificial Intelligence (AI), are insufficient in addressing the dynamic and adaptive nature of cyberattacks in AVs environment. This study presents a multi-layered defense approach powered by autonomous AI agents capable of independently detecting, interpreting, and responding to a wide range of cyber threats in real time. These agents employ Machine Learning (ML), Deep Learning (DL), anomaly detection, and adversarial resilience techniques to counter sophisticated attacks such as sensor manipulation, AI corruption, GPS spoofing, and Vehicle-to-Everything (V2X) based intrusions. The proposed architecture contributes to the advancement of intelligent, self-adjusting security mechanisms tailored for the evolving cybersecurity landscape of autonomous systems.

关键词： Prevention and mitigation Transportation Market research Threat assessment Real-time systems Artificial intelligence Vehicle dynamics Autonomous vehicles Vehicle-to-everything Resilience

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AI-Integrated Sensor Data Analytics for Real-Time Decision-Making in Wireless Sensor Networks 2

AI-Integrated Sensor Data Analytics for Real-Time Decision-M...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Wasim Haidar, S.K. Wilfred Blessing, N.R. Chaubey, Sudhakar Kumar Mehmood, Zahid Kavitha, S.J. Sutherlin Subitha, G. College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Oman Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Tamil Nadu Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Aruthenganvilai India

ISBN: (纸本)9798331505745

The study compares the performance of Wireless Sensor Networks (WSNs) with 20, 30, 40, and 50 nodes in real-time AI-enhanced sensor analytics using the AODV routing protocol. All configurations showed a consistent Packet Delivery Ratio (PDR) of 100%. However, the end-to-end delay increased slightly from 0.2 sec (20 nodes) to 0.3 sec (30, 40, 50 nodes), indicating that larger networks result in longer paths. Throughput decreased from 2560 bps at 20 nodes to 1706.67 bps in larger configurations, probable due to network congestion. Energy efficiency also decreased as the number of nodes increased, suggesting higher energy consumption per transmission. © 2025 IEEE.

关键词： Sensor nodes

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Parameter identification in PDEs by the solution of monotone inclusion problems

arXiv

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arXiv 2024年

作者： Gautam, Pankaj Grasmair, Markus Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee India Department of Mathematical Sciences Norwegian University of Science and Technology Trondheim Norway

In this paper we consider the solution of monotone inverse problems using the particular example of a parameter identification problem for a semilinear parabolic PDE. For the regularized solution of this problem, we introduce a total variation based regularization method requiring the solution of a monotone inclusion problem. We show well-posedness in the sense of inverse problems of the resulting regularization scheme. In addition, we introduce and analyze a numerical algorithm for the solution of this inclusion problem using a nested inertial primal dual method. We demonstrate by means of numerical examples the convergence of both the numerical algorithm and the regularization *** Codes 65J20, 47H05, 47J06, 49J20 © 2024, CC BY.

关键词： Parameter estimation

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LLCNN-based Image Enhancement with Squeeze and Excitation Network for Improved Visual Quality 16

LLCNN-based Image Enhancement with Squeeze and Excitation Ne...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Subburaj, Maheswari Shanmugam, Thirumurugan Sivaraman, Arun Kumar Nithiyanantham, Janakiraman Ravindran, Priya Sivam, Babu School of Cse Vellore Institute of Technology Chennai India College of Computing and Information Sciences University of Technology and Applied Sciences Suhar Oman Digital Engineering Solution Centre Photon Interactive Inc. Ontario Canada Department of Ece K L N College of Engineering Tamilnadu India

ISBN: (纸本)9798331505264

Image enhancement, particularly in low-light conditions, has long been a research focus to improve visual quality. Low-light settings often cause visibility issues and detail loss, which recent deep-learning techniques have effectively addressed. This study explores the combination of a low-light convolutional neural network (LLCNN), a convolutional neural network designed explicitly for low-light image enhancement, with a Squeeze and Excitation Network (SEN), which recalibrate features to emphasise critical details and suppress noise. Integrating SEN into LLCNN enhances image details while reducing noise and artefacts typically present in low-light conditions. Experimental results show that this combined approach outperforms existing methods in objective metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), as well as visual quality. © 2024 IEEE.

关键词： Convolutional neural networks

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Natural Language Processing Revolution From Humble Beginnings to Deep Learning and Beyond

Natural Language Processing Revolution From Humble Beginning...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Nyalalani Smarts Rajalakshmi Selvaraj Venumadhav Kuthadi Department of Computing and Informatics School of Pure and Applied Sciences Botswana International University of Science and Technology) Palapye Botswana

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

This paper explores the intricate development of Natural Language Processing (NLP), a domain which is centered at the core of computational intelligence and human communication. This research is inspired by the need to understand the foundations of NLP and how it has influenced the development of human-computer interactions, language comprehension systems and information retrieval techniques. It explores the transformational effect of deep learning and attention-based models such as the third-generation Generative Pre-trained Transformer (GPT-3), Text-to-Text Transfer Transformer (T5) and Bidirectional Encoder Representations from Transformers (BERT). The models mentioned above have greatly improved the capability to comprehend and generate human-like language using computers. The originality of the work is also reflected on its comprehensive overview of key NLP concepts, tracing chronological advancements, exploring deep learning innovations, and discussing effective pruning techniques to optimize model performance. By reviewing the history of NLP, this research not merely brings the past into focus but suggests future developments for scholars to investigate and find solutions for current challenges in NLP. Its results not only act as a guide for practitioners today but also suggest new directions for research and development that will help meet the challenges of tomorrow in this dynamic discipline of NLP.

关键词： Deep learning Technological innovation Computational modeling Bidirectional control Transformers Natural language processing Encoding Trajectory History Computational intelligence

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Explainable-AI for DoS Attacks Detection in 5G Network Using Deep Learning Models 5

Explainable-AI for DoS Attacks Detection in 5G Network Using...

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5th International Conference on Intelligent computing, Communication, Networking and Services, ICCNS 2024

作者： Albashayreh, Amjad Tashtoush, Yahya Aldosary, Abdallah Darwish, Omar Albalas, Firas The University of Jordan Department of Computer Science Amman Jordan Jordan University of Science and Technology Department of Computer Science Irbid Jordan Prince Sattam bin Abdulaziz University The Department of Computer Engineering Ar Riyadh Saudi Arabia Eastern Michigan University Information Security and Applied Computing Department YpsilantiMI48197 United States

ISBN: (纸本)9798350354690

With the emergence of the fifth-generation (5G) net-work, numerous revolutionary applications are enabled, including low-latency and machine-type communications. This great in-crease creates a broader security threat concerns, such as denial-of-service (DoS) attacks, which can disrupt network functionality. The complexity and decentralization of 5G networks create new vulnerabilities for adversaries, necessitating comprehensive security procedures to identify, mitigate, and prevent DoS attacks in 5G networks. This paper introduces a novel approach for DoS detection in 5G networks, utilizing deep learning and machine learning models, along with Local Interpretable Model-Agnostic Explanations (LIME), to interpret model predictions and identify the significant role of data features in detecting DoS attacks. The results revealed that the random forest model demonstrated superior recall of 99.98, while BiLSTM demonstrated exceptional performance with a recall of 98.02. © 2024 IEEE.

关键词： 5G mobile communication systems

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Implementation and Use of Clinical Decision Support System in Malawi National ART EMR System

Implementation and Use of Clinical Decision Support System i...

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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Pachawo Bisani Goodall Nyirenda Department of Computing University of Malawi Zomba Malawi Department of Computing and Information Technology Malawi University of Business and Applied Sciences Blantyre Malawi

The introduction of Malawi national antiretroviral therapy (NART) electronic medical record (EMR) system has improved the management and reporting of ART patient data. Designed to aid in decision-making by providers for individual patients in their care, the system has been integrated with a Clinical Decision Support System (CDSS). NART EMR system was introduced in 2006, and by 2021, it had been extended to over 206 healthcare facilities in the country. Despite several evaluations conducted on the NART EMR, there has been limited attention given to evaluating the implementation and usage of the CDSS within the NART EMR system. Hence, the study sought to establish how CDSS in the NART EMR system has been implemented and used by healthcare providers in Malawi to improve decision making on an individual patient on antiretroviral therapy (ART) at any point in time. The study employed a qualitative methodology, gathering data through observations, interviews, and an in-depth analysis of the system. The study establishes that smooth integration of CDSS in EMR system into regular and normal clinical workflow contributes to the success of CDSS use. Despite the successes of CDSS in EMR system, issues of system quality and information quality must be addressed during design and implementation of the CDSS to enhance efficiency and effectiveness of the system. The study suggests that those responsible for implementation should prioritize improving the underlying logic of the clinical decision support intervention in order to address existing concerns and improve the accuracy of the provided information. Additionally, the study recommends active consultation with the system's real users during the implementation process.

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Design and Implementation of an IoT-Based Smart Irrigation System for Optimized Water Management in Wheat Cultivation 12

Design and Implementation of an IoT-Based Smart Irrigation S...

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12th International Conference on System Modeling and Advancement in Research Trends, SMART 2023

作者： Shukla, Ravi Shankar George, Remya P. Hussain, Sajithunisa Ahmad, Nazia Babu, Prakash Jain, Vishal College of Computing and Informatics Saudi Electronic University Department of Computer Science Saudi Arabia The Applied College Imam Abdulrahman Bin Faisal University Computer Science Department Saudi Arabia Acharya Institute of Technology Dept of It Bangalore India

ISBN: (纸本)9798350369861

The current study presents the planning and implementation of a custom-built, Internet of Things (IoT)-based smart irrigation system for wheat cultivation that helps to combat problems such as water scarcity and inefficiency in traditional irrigation methods. The proposed device employs a network of environmental sensor that ensures optimal crop development with minimum water waste. These sensors monitor soil temperature and moisture levels and shows significant improvements in water management efficiency. When compared to standard irrigation approaches, the system's pilot installation results reveal considerable water usage reductions and benefits in the health of the wheat crop. These findings prove that the system is efficient at optimizing resource use and show how it can change the way wheat is farmed forever. © 2023 IEEE.

关键词： Internet of things

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Optimizing the Clinical Decision Support System (CDSS) by Using Recurrent Neural Network (RNN) Language Models for Real-Time Medical Query Processing

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Computers, Materials & Continua 2024年第12期81卷 4787-4832页

作者： Israa Ibraheem Al Barazanchi Wahidah Hashim Reema Thabit Mashary Nawwaf Alrasheedy Abeer Aljohan Jongwoon Park Byoungchol Chang College of Computing and Informatics Universiti Tenaga Nasional(UNITEN)Kajang43000Malaysia College of Engineering University ofWarith Al-AnbiyaaKarbala56001Iraq Faculty of Information Science&Technology Universiti Kebangsaan MalaysiaBangi43600Malaysia Department of Computer Science Applied CollegeUniversity of HailHail55424Saudi Arabia Department of Computer Science and Informatics Taibah UniversityMedina42353Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Republic of Korea

This research aims to enhance Clinical Decision Support Systems(CDSS)within Wireless Body Area Networks(WBANs)by leveraging advanced machine learning ***,we target the challenges of accurate diagnosis in medical imaging and sequential data analysis using Recurrent Neural Networks(RNNs)with Long Short-Term Memory(LSTM)layers and echo state *** models are tailored to improve diagnostic precision,particularly for conditions like rotator cuff tears in osteoporosis patients and gastrointestinal *** diagnostic methods and existing CDSS frameworks often fall short in managing complex,sequential medical data,struggling with long-term dependencies and data imbalances,resulting in suboptimal accuracy and delayed *** goal is to develop Artificial Intelligence(AI)models that address these shortcomings,offering robust,real-time diagnostic *** propose a hybrid RNN model that integrates SimpleRNN,LSTM layers,and echo state cells to manage long-term dependencies ***,we introduce CG-Net,a novel Convolutional Neural Network(CNN)framework for gastrointestinal disease classification,which outperforms traditional CNN *** further enhance model performance through data augmentation and transfer learning,improving generalization and robustness against data scarcity and *** validation,including 5-fold cross-validation and metrics such as accuracy,precision,recall,F1-score,and Area Under the Curve(AUC),confirms the models’***,SHapley Additive exPlanations(SHAP)and Local Interpretable Model-agnostic Explanations(LIME)are employed to improve model *** findings show that the proposed models significantly enhance diagnostic accuracy and efficiency,offering substantial advancements in WBANs and CDSS.

关键词： Computer science clinical decision support system(CDSS) medical queries healthcare deep learning recurrent neural network(RNN) long short-term memory(LSTM)

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