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IEEE Transactions on AgriFood Electronics 2025年第1期3卷 121-133页

作者： Bera, Asish Krejcar, Ondrej Bhattacharjee, Debotosh Birla Institute of Technology and Science Department of Computer Science and Information Systems Pilani333031 India University of Hradec Kralove Center for Basic and Applied Science Faculty of Informatics and Management Hradec Kralove50003 Czech Republic Skoda Auto University Mlada Boleslav29301 Czech Republic Universiti Teknologi Malaysia Malaysia Japan International Institute of Technology Kuala Lumpur81310 Malaysia Jadavpur University Department of Computer Science and Engineering Kolkata700032 India

Deep convolutional neural networks (CNNs) have facilitated remarkable success in recognizing various food items and agricultural stress. A decent performance boost has been witnessed in solving the agro-food challenges by mining and analyzing region-based partial feature descriptors. Also, computationally expensive ensemble learning schemes fusing multiple CNNs have been studied in earlier works. This work proposes a region attention scheme for modeling long-range dependencies by building a correlation among different regions within an input image. The attention method enhances feature representation by learning the usefulness of context information from complementary regions. Spatial pyramidal pooling and average pooling pairs aggregate partial descriptors into a holistic representation. Both pooling methods establish spatial and channelwise relationships without incurring extra parameters. A context gating scheme is applied to refine the descriptiveness of weighted attentional features, which is relevant for classification. The proposed region attention network for food items and agricultural stress recognition method, dubbed RAFA-Net, has been experimented on three public food datasets, and has achieved state-of-the-art performances with distinct margins. The highest top-1 accuracy of RAFA-Net is 91.69%, 91.56%, and 96.97% on the UECFood-100, UECFood-256, and MAFood-121 datasets, respectively. In addition, better accuracies have been achieved on two benchmark agricultural stress datasets. The best top-1 accuracies on the Insect Pest (IP-102) and PlantDoc-27 plant disease datasets are 92.36%, and 85.54%, respectively;implying RAFA-Net's generalization capability. © 2023 IEEE.

关键词： Plant diseases

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Advancing Early Detection of Colorectal Adenomatous Polyps via Genetic Data Analysis: A Hybrid Machine Learning Approach

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Journal of computer and Communications 2024年第7期12卷 23-38页

作者： Ahmed S. Maklad Mohamed A. Mahdy Amer Malki Noboru Niki Abdallah A. Mohamed Computer Science Department College of Computer Science and Engineering in Yanbu Taibah University Medina Saudi Arabia Information Systems Department Faculty of Computers and Artificial Intelligence Beni-Suef University Beni-Suif Egypt Computer Science Department Faculty of Computers and Artificial Intelligence Beni-Suef University Beni-Suif Egypt Institute of Advanced Science and Technology Tokushima University Tokushima Japan Information Systems Department College of Computer Science and Engineering in Yanbu Taibah University Medina Saudi Arabia Mathematics and Computer Science Department Faculty of Science Menoufia University Menoufia Egypt

In this study, a hybrid machine learning (HML)-based approach, incorporating Genetic data analysis (GDA), is proposed to accurately identify the presence of adenomatous colorectal polyps (ACRP) which is a crucial early detector of colorectal cancer (CRC). The present study develops a classification ensemble model based on tuned hyperparameters. Surpassing accuracy percentages of early detection approaches used in previous studies, the current method exhibits exceptional performance in identifying ACRP and diagnosing CRC, overcoming limitations of CRC traditional methods that are based on error-prone manual examination. Particularly, the method demonstrates the following CRP identification accuracy data: 97.7 ± 1.1, precision: 94.3 ± 5, recall: 96.0 ± 3, F1-score: 95.7 ± 4, specificity: 97.3 ± 1.2, average AUC: 0.97.3 ± 0.02, and average p-value: 0.0425 ± 0.07. The findings underscore the potential of this method for early detection of ACRP as well as clinical use in the development of CRC treatment planning strategies. The advantages of this approach are highly expected to contribute to the prevention and reduction of CRC mortality.

关键词： Colorectal Adenoma Detection Colorectal Cancer Diagnosis Hybrid Machine Learning Genetics Analysis

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RLStop: A Reinforcement Learning Stopping Method for TAR

arXiv

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arXiv 2024年

作者： R., Bin-Hezam M., Stevenson Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia Department of Computer Science Faculty of Engineering University of Sheffield Sheffield United Kingdom

We present RLStop, a novel Technology Assisted Review (TAR) stopping rule based on reinforcement learning that helps minimise the number of documents that need to be manually reviewed within TAR applications. RLStop is trained on example rankings using a reward function to identify the optimal point to stop examining documents. Experiments at a range of target recall levels on multiple benchmark datasets (CLEF e-Health, TREC Total Recall, and Reuters RCV1) demonstrated that RLStop substantially reduces the workload required to screen a document collection for relevance. RLStop outperforms a wide range of alternative approaches, achieving performance close to the maximum possible for the task under some circumstances. © 2024, CC BY.

关键词： Reinforcement learning

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AI LipReader-Transcribing Speech from Lip Movements

AI LipReader-Transcribing Speech from Lip Movements

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： D Ajitha Disha Dutta Falguni Saha Parus Giri Rohan Kant Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Vellore Vellore India Department of Database Systems School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Information Security School of Computer Science and Engineering Vellore Institute of Technology Vellore Vellore India

Lip Reading AI is a discipline that is rapidly changing and has numerous applications in security, accessibility and human-computer interaction. This paper proposes a model which combines Convolutional Neural Networks (CNNs) to capture spatial capabilities, Long Short-Term Memory (LSTM) networks to examine temporal dependencies, and an adaptive interest mechanism. Meticulous preprocessing of the MIRACL VC-l dataset addressing challenges including one of a kind lip moves and occlusions accompanied with the aid of transitioning this study effortlessly to LRS2 dataset to complement lexemic versatility is one of its key function. The effects verify its robustness throughout unique datasets with superior overall performance towards cutting-edge techniques. Ablation checks suggest the crucial significance of every element in phrases of improving lip analyzing accuracy. Our proposed model version additionally suggests flexibility in restricted and naturalistic language situations.

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High-Accuracy Hand Segmentation for Palmprint Biometric systems Improving Auto-Encoder Techniques

High-Accuracy Hand Segmentation for Palmprint Biometric Syst...

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Innovative Approaches in Smart Technologies (ISAS), International Symposium on

作者： Kadir Yalcin Ramazan Ozgur Dogan Hulya Dogan Ozkan Bingol Department of Artificial Intelligence and Intelligent Systems Graduate Education Institute Gumushane University Gumushane Türkiye Department of Artificial Intelligence Engineering Faculty of Computer and Information Sciences Trabzon University Trabzon Türkiye Department of Software Engineering Faculty of Engineering Karadeniz Technical University Trabzon Türkiye Department of Software Engineering Faculty of Eng. and Natural Sciences Gumushane University Gumushane Türkiye

ISBN: (数字)9798331540104

ISBN: (纸本)9798331540111

Biometric technologies have been widely adopted in various commercial products, ranging from security systems to personal devices, due to their exceptional reliability and user-friendliness. Among these, palmprint biometrics has gained increasing attention for its advantages, such as ease of use, a larger surface area, and the ability to capture more features compared to other biometric methods. A typical palmprint biometric system involves five key stages: (a) Image Acquisition, (b) Hand Segmentation, (c) Pattern Generation, (d) Feature Extraction, and (e) Verification/Identification. The overall success of the system depends on the effectiveness of each stage, with hand segmentation being crucial for identifying the most relevant features for accurate verification. This study aims to achieve high-accuracy biometric recognition by segmenting hands from images captured in uncontrolled environments with complex backgrounds. Accuracy (ACC), Intersection over Union (IoU), F1-Score, and Dice Coefficient are the four critical metrics used to assess the performance of segmentation techniques. The findings demonstrate that the U-Net + ViT and MA-Net + ResNet34 techniques outperform traditional auto-encoder methods in both quantitative performance metrics and visual analysis, leading to significantly improved hand segmentation for biometric systems.

关键词： Biometrics Hands Image segmentation Visualization Accuracy Image recognition Biological system modeling Feature extraction Reliability Security

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Impact of Artificial Intelligence on software engineering Phases and Activities (2013–2024): A Quantitative Analysis Using Zero- Truncated Poisson Model

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IEEE Access 2025年 13卷 95535-95547页

作者： Durrani, Usman Khan Akpinar, Mustafa Bektas, Hakan Saleh, Mohammed Higher Colleges of Technology Faculty of Computer and Information Sciences Abu Dhabi United Arab Emirates Sakarya University Software Engineering Department Sakarya54050 Turkey Istanbul University Department of Econometrics Istanbul34452 Turkey

This paper presents the results of a quantitative analysis derived from data collected in our earlier systematic literature review, focusing on integrating Artificial Intelligence (AI) techniques across various phases of software engineering (SE). Employing a Zero-Truncated Poisson (ZTP) model, we analyzed 120 selected research papers to assess the impact of AI methodologies on SE phases. The findings indicate significant improvements attributable to AI in different phases of SE. Specifically, AI techniques have been found to enhance the accuracy of the planning, requirement engineering, and development phases and improve the efficiency of the requirement engineering and design phases. Notably, AI’s integration has markedly improved the accuracy and efficiency of the requirement engineering phase, underscoring AI’s critical role in advancing SE practices. This study contributes to a deeper understanding of the quantitative impact of AI on SE, providing valuable insights for practitioners and researchers in understanding AI technologies to enhance SE phases. © 2013 IEEE.

关键词： software design

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Medical Expert System to Diagnose the Most Common Psychiatric Diseases 21

Medical Expert System to Diagnose the Most Common Psychiatri...

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7th International Conference on engineering and MIS, ICEMIS 2021

作者： Alsagheer, Eman A. Rajab, Hamzah A. Elnajar, Khadiga M.Y. Department of Computer Science Faculty of Information Technology University of Benghazi Benghazi Libya Department of Software Engineering Faculty of Information Technology University of Benghazi Benghazi Libya

ISBN: (纸本)9781450390446

Expert systems are programs that employ artificial intelligence and mimic the performance of human experts in a certain field by gathering and capturing expert information. In this paper, we present a rule-based expert system to provide the diagnosis for the most common psychiatric diseases to facilitate the diagnosis process. This system diagnoses four types of psychiatric diseases Anxiety Disorder (generalized anxiety, panic disorder, OCD, and phobic disorder), Stress-related disorder, Depression, and Schizophrenia using the International Classification of Diseases 10th Revision. The knowledge used in this system is acquired from domain experts, and relevant medical books and documents to capture knowledge that is valid as much as possible. Then, the acquired knowledge is demonstrated using a diagnostic chart, and decision table that represents the signs and symptoms involved in the diagnosis. A rule-based expert system and production rules that represent the domain knowledge are built using SWI-PROLOG Editor. The system is based on backward chaining which starts with possible goals and attempts to collect information that confirms it. The testing result shows the average accuracy of the system register 80% in diagnosing patients with psychiatric diseases. © 2021 ACM.

关键词： Expert systems

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Double-SP Method to Enhance Privacy and Security of Smart City Applications 10

Double-SP Method to Enhance Privacy and Security of Smart Ci...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Bahbouh, Nour Mahmoud Hakami, Abdulelah Alhussain Hassan Aljohani, Yaser Mohammed Sen, Adnan Ahmed Abi Alkhodre, Ahmad Alsughayyir, Aeshah Granada University Department of Information and Communication Sciences Granada Spain Saudi Electronic University Al-Madinah Saudi Arabia YasRef Program Yanbu Saudi Arabia Computing and Information Technology Islamic University Al-Madinah Saudi Arabia Islamic University Faculty of Computer and Information Systems Al-Madinah Saudi Arabia College of Computer Science and Engineering Taibah University Al-Madinah Saudi Arabia

ISBN: (纸本)9789380544472

Smart cities promise a lot of well-being to their users in all areas of life through millions of applications and services. Smart services rely heavily on collecting data and the preferences of users. But on the dark side, the users' information is posed to threat and penetration during transmission or stored far in the clouds. Depending on encryption only is not sufficient if the attacker has strong resources or if the attacker is the service provider (SP) itself. In addition, changing data before sending is not a practical solution in many systems because of the adverse impact on the quality of a main service. This research presented a new idea to address the issue of protection. The proposed method enhanced the privacy and security of users' data without affecting the accuracy of service. The core of the suggested solution relies on a knowledge base of services that are managed by experts. Also, the solution depends on fog nodes to measure the level of security and privacy of users' queries without delay. Moreover, the fog nodes manage contact with SPs. Finally, the proposed method divided the SP into two, one for user queries and the other for user data. The simulation and analytical discussion on a practical case in smart cities demonstrated the superiority of the proposed approach over previous methods in the level of protection by maintaining the quality of services, and the resistance to attacks. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Fog

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LED there be DoS: Exploiting variable bitrate IP cameras for network DoS

arXiv

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arXiv 2025年

作者： Goldberg, Emmanuel Brodt, Oleg Elyashar, Aviad Puzis, Rami Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Israel Cyber Labs@BGU Ben-Gurion University of the Negev Israel Department of Computer Science Shamoon College of Engineering Israel

Variable-bitrate video streaming is ubiquitous in video surveillance and CCTV, enabling high-quality video streaming while conserving network bandwidth. However, as the name suggests, variable-bitrate IP cameras can generate sharp traffic spikes depending on the dynamics of the visual input. In this paper, we show that the effectiveness of video compression can be reduced by up to 6× using a simple laser LED pointing at a variable-bitrate IP camera, forcing the camera to generate excessive network traffic. Experiments with IP cameras connected to wired and wireless networks indicate that a laser attack on a single camera can cause significant packet loss in systems sharing the network with the camera and reduce the available bandwidth of a shared network link by 90%. This attack represents a new class of cyber-physical attacks that manipulate variable bitrate devices through changes in the physical environment without a digital presence on the device or on the network. We also analyze the broader view of multidimensional cyberattacks that involve both the physical and digital realms and present a taxonomy that categorizes attacks based on their direction of influence (physical-to-digital or digital-to-physical) and their method of operation (environment-driven or device-driven), highlighting multiple areas for future research. © 2025, CC BY-NC-ND.

关键词： Video streaming

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Modified aquila optimizer for forecasting oil production

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Geo-Spatial information Science 2022年第4期25卷 519-535页

作者： Mohammed A.A.Al-qaness Ahmed A.Ewees Hong Fan Ayman Mutahar AlRassas Mohamed Abd Elaziz State Key Laboratory for Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanChina Systems University of Bisha Department of e-BishaKingdom of Saudi Arabia Department of Computer Damietta UniversityDamiettaEgypt School of Petroleum Engineering China University of Petroleum(East China)QingdaoChina Department of Mathematics Faculty of ScienceZagazig UniversityZagazigEgypt Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman 346United Arab Emirates Faculty of Computer Science&Engineering Galala UniversitySuze 435611Egypt

Oil production estimation plays a critical role in economic plans for local governments and ***,many studies applied different Artificial Intelligence(AI)based meth-ods to estimate oil production in different *** Adaptive Neuro-Fuzzy Inference System(ANFIS)is a well-known model that has been successfully employed in various applica-tions,including time-series ***,the ANFIS model faces critical shortcomings in its parameters during the configuration *** this point,this paper works to solve the drawbacks of the ANFIS by optimizing ANFIS parameters using a modified Aquila Optimizer(AO)with the Opposition-Based Learning(OBL)*** main idea of the developed model,AOOBL-ANFIS,is to enhance the search process of the AO and use the AOOBL to boost the performance of the *** proposed model is evaluated using real-world oil produc-tion datasets collected from different oilfields using several performance metrics,including Root Mean Square Error(RMSE),Mean Absolute Error(MAE),coefficient of determination(R2),Standard Deviation(Std),and computational ***,the AOOBL-ANFIS model is compared to several modified ANFIS models include Particle Swarm Optimization(PSO)-ANFIS,Grey Wolf Optimizer(GWO)-ANFIS,Sine Cosine Algorithm(SCA)-ANFIS,Slime Mold Algorithm(SMA)-ANFIS,and Genetic Algorithm(GA)-ANFIS,***,it is compared to well-known time series forecasting methods,namely,Autoregressive Integrated Moving Average(ARIMA),Long Short-Term Memory(LSTM),Seasonal Autoregressive Integrated Moving Average(SARIMA),and Neural Network(NN).The outcomes verified the high performance of the AOOBL-ANFIS,which outperformed the classic ANFIS model and the compared models.

关键词： Oil production ANFIS opposition-based learning(OBL) Aquila Optimizer(AO) time series forecasting Tahe oilfield Sunah oilfield

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