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Plant Phenome Journal 2024年第1期7卷

作者： Bongomin, Ocident Lamo, Jimmy Guina, Joshua Mugeziaubwa Okello, Collins Ocen, Gilbert Gilibrays Obura, Morish Alibu, Simon Owino, Cynthia Awuor Akwero, Agnes Ojok, Samson National Crops Resources Research Institute (NaCRRI) Kampala Uganda Department of Manufacturing Industrial and Textile Engineering School of Engineering Moi University Eldoret Kenya Department of Information and Communication Technology National Agricultural Research Organisation (NARO) Secretariat Entebbe Uganda Department of Biosystems Engineering Faculty of Agriculture and Environment Gulu University Gulu Uganda Department of Computer Engineering & Informatics Faculty of Engineering Busitema University Tororo Uganda Department of Electrical and Computer Engineering School of Engineering Makerere University Kampala Uganda School of Agricultural Sciences Makerere University Kampala Uganda

We are in a race against time to combat climate change and increase food production by 70% to feed the ever-growing world population, which is expected to double by 2050. Agricultural research plays a vital role in improving crops and livestock through breeding programs and good agricultural practices, enabling sustainable agriculture and food systems. While advanced molecular breeding technologies have been widely adopted, phenotyping as an essential aspect of agricultural research and breeding programs has seen little development in most African institutions and remains a traditional method. However, the concept of high-throughput phenotyping (HTP) has been gaining momentum, particularly in the context of unmanned aerial vehicle (UAV)-based phenotyping. Although research into UAV-based phenotyping is still limited, this paper aimed to provide a comprehensive overview and understanding of the use of UAV platforms and image analytics for HTP in agricultural research and to identify the key challenges and opportunities in this area. The paper discusses field phenotyping concepts, UAV classification and specifications, use cases of UAV-based phenotyping, UAV imaging systems for phenotyping, and image processing and analytics methods. However, more research is required to optimize UAVs’ performance for image data acquisition, as limited studies have focused on the effect of UAVs’ operational parameters on data acquisition. © 2024 The Authors. The Plant Phenome Journal published by Wiley Periodicals LLC on behalf of American Society of Agronomy and Crop Science Society of America.

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Enhancing IoT Security Through AI-Based Anomaly Detection and Intrusion Prevention

Enhancing IoT Security Through AI-Based Anomaly Detection an...

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International Conference on Contemporary Computing and informatics (IC3I)

作者： Narayana Swamy Ramaiah S. Kevin Andrews A. Shenbagharaman M. S. Gowtham Bandi Bhaskar Mohit Tiwari Dept of Computer Engineering SPCE Sankalchand Patel University Visnagar Gujarat India Department of Computer Applications Dr. Mgr Educational and Research Institute Chennai Tamil Nadu Department of CSE National Engineering College Kovilpatti Tamilnadu India ECE Department Karpagam Institute of Technology Coimbatore Tamilnadu India School of CS & AI SR University Warangal Telangana India Department of Computer Science and Engineering Bharati Vidyapeeth's College of Engineering Delhi India

Enhancing the Internet of Things (IoT) security is among the most pressing concerns confronting the information technology sector. With significant numbers of loT systems being created and established, it is difficult for these systems to interact securely without affecting performance. The difficulties arise since the majority of loT systems are resource restricted and so possess restricted processing capability. In, this study, we intend to study the enhancement of IoT security of anomaly detection and intrusion prevention. To enhance anomaly detection and intrusion prevention performance, a binary categorization of typical and unusual IoT traffic is created. In this paper, we carefully evaluate the specificity and complication of IoT security protection, and then discover that Artificial Intelligence (AI) approaches like Machine Learning (ML) and ensemble classifiers may offer new strong abilities to satisfy IoT security demands. This enhancement can be associated with ensemble learning methods that make use of a variety of learning processes with variable capacities. We were capable of improving the predictability of our predictions while decreasing the likelihood of classification errors through the combination of these methods. The outcomes of the experiments suggest that the architecture could enhance the effectiveness of the anomaly detection and intrusion prevention System, with an accuracy level of 0.9863.

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Multidetector CT in the Analysis of Pulmonary Thromboembolism

Multidetector CT in the Analysis of Pulmonary Thromboembolis...

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Mobile Computing and Sustainable informatics (ICMCSI), International Conference on

作者： Rohini M. S Balaji V Vijai R Rajesh V D. ChandraMohan Ajay Singh Yadav Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu India Department of Mechanical Engineering Sri Sairam College of Engineering Karnataka India Department of Mechanical Engineering St. Peter’s Institute of Higher Education and Research Chennai India Department of Mathematics Delhi-NCR Campus SRM Institute of Science and Technology Uttar Pradesh India

This study aims to investigate the utility of Multidetector computed tomography (MDCT) with a focus on the application of reformats in maximum intensity projection (MIP) in enhancing the diagnostic accuracy of PTE. A retrospective analysis was conducted on a cohort of [insert number] patients who underwent MDCT for suspected PTE. The MDCT images were reconstructed using MIP, allowing for comprehensive visualization of vascular structures. The diagnostic performance of MDCT with MIP reformats was compared to standard MDCT images. The inclusion of MIP reformats significantly improved the detection of PTE, providing enhanced visualization of thrombi within the pulmonary vasculature. The sensitivity and specificity of MDCT with MIP were [insert percentage] and [insert percentage], respectively, compared to [insert percentage] and [insert percentage] for standard MDCT. The findings highlight the added diagnostic value of MDCT with reformats in MIP for the evaluation of PTE. The improved visualization of thromboemboli within the pulmonary arteries (PA) enhances diagnostic accuracy, aiding clinicians in making timely and informed decisions. MDCT with MIP reformats stands as a valuable technique in the comprehensive assessment of pulmonary thromboembolism, contributing to better patient outcomes.

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A Systematic Overview on Simulation of Connected Vehicles Infrastructure in Smart Cities

A Systematic Overview on Simulation of Connected Vehicles In...

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Manik Gupta Imed Ben Dhaou Kamal Deep Garg Bhisham Sharma Chitkara University School of Engineering and Technology Chitkara University Himachal Pradesh India Department of Computer Science Hekma School of Engineering Computing and Informatics Dar Al-Hekma University Jeddah Saudi Arabia Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

The development and use of connected vehicles are becoming increasingly important areas of study in the wireless networking and transportation research communities. These networks can provide various services such as collision warning. Unlike other types of devices, such as hand-held devices, the vehicles’ nodes are not limited by their energy consumption and storage capacity. Therefore, the development of connected networks can be carried out with minimal resources. It is necessary to run a simulation in order to determine whether they are feasible. The availability of multiple devices and protocols has led to the creation of new utilization scenarios. Due to the complexity of the task, many simulation tools were developed during the past few years. This paper systematically reviews the current state of these tools and discusses their capabilities to evaluate the various scenarios related to the development of connected vehicles network.

关键词： Wireless communication Technological innovation Connected vehicles Systematics Smart cities Vehicular ad hoc networks Transportation

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Evaluating approval-based multiwinner voting in terms of robustness to noise 29

Evaluating approval-based multiwinner voting in terms of rob...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Caragiannis, Ioannis Kaklamanis, Christos Karanikolas, Nikos Krimpas, George A. Department of Computer Engineering and Informatics University of Patras Rion26504 Greece Computer Technology Institute "Diophantus" Rion26504 Greece

ISBN: (纸本)9780999241165

Approval-based multiwinner voting rules have recently received much attention in the Computational Social Choice literature. Such rules aggregate approval ballots and determine a winning committee of alternatives. To assess effectiveness, we propose to employ new noise models that are specifically tailored for approval votes and committees. These models take as input a ground truth committee and return random approval votes to be thought of as noisy estimates of the ground truth. A minimum robustness requirement for an approval-based multiwinner voting rule is to return the ground truth when applied to profiles with sufficiently many noisy votes. Our results indicate that approval-based multiwinner voting can indeed be robust to reasonable noise. We further refine this finding by presenting a hierarchy of rules in terms of how robust to noise they are. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

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Modeling blockage in high directional wireless systems

arXiv

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arXiv 2024年

作者： Koutsonas, Evangelos Boulogeorgos, Alexandros-Apostolos A. Trevlakis, Stylianos E. Ali, Tanweer Tsiftis, Theodoros A. Department of Electrical and Computer Engineering University of Western Macedonia Kozani50100 Greece Research & Development Department InnoCube P.C. Thessaloniki55535 Greece Department of Electronics and Communication Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India Department of Informatics & Telecommunications University of Thessaly Lamia35100 Greece Department of Electrical and Electronic Engineering University of Nottingham Ningbo China Ningbo315100 China

While the wireless word moves towards higher frequency bands, new challenges arises, due to the inherent characteristics of the transmission links, such as high path and penetration losses. Penetration losses causes blockages that in turn can significantly reduce the signal strength at the receiver. Most published contributions consider a binary blockage stage, i.e. either fully blocked or blockage-free links. However, in realistic scenarios, a link can be partially blocked. Motivated by this, in this paper, we present two low-complexity models that are based on tight approximations and accommodates the impact of partial blockage in high-frequency links. To demonstrate the applicability of the derived framework, we present closed-form expressions for the outage probability for the case in which the distance between the center of the receiver plane and the blocker’s shadow center follow uniform distribution. Numerical results verify the derived framework and reveal how the transmission parameters affect blockage. Copyright © 2024, The Authors. All rights reserved.

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Blockchain for AI-Enabled Industrial IoT with 5G Network 14

Blockchain for AI-Enabled Industrial IoT with 5G Network

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14th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2022

作者： Pant, Piyush Rajawat, Anand Singh Goyal, S.B. Bedi, Pradeep Verma, Chaman Enescu, Florentina Magda Raboaca, Maria Simona Mihaltan, Traian Candin School of Computer Sciences and Engineering Sandip University Nashik India City University Faculty of Information Technology Petaling Jaya Malaysia Galgotias University Department of Computer Sciences & Engineering Greater Noida India Eötvös Loránd University Budapest Department of Media and Educational Informatics Budapest Hungary University of Pitesti Department of Electronics Communications and Computers Pitesti Romania National Research and Development Institute for Cryogenic and Isotopic Technologies-ICSI Râmnicu Vâlcea Romania Technical University of Cluj-Napoca Faculty of Building Services Cluj-Napoca Romania

ISBN: (数字)9781665495356

ISBN: (纸本)9781665495356

The world is advancing to a new digitalized world that has one of the most powerful technologies that ever existed in human history. These technologies are making humans create such objects that were only told in the fairy tales in the past. This research proposes a model that integrates the latest and one of the most powerful technologies of the decade. This study integrated the 5G network with the IIoT (Industrial Internet of Things) which is based on artificial intelligence to develop an intelligent machine capable of mimicking humans. Such a system is so powerful yet so vulnerable to problems like hacking, cyber-attacks and so on. This problem is solved with the blockchain. The research adds blockchain to the model to make the model more secure and efficient as it provides a decentralized system to ensure transparency. Previous researches have covered the IoT with blockchain but this research is the advanced version which includes the industrial IoT using Artificial intelligence to make intelligent Internet of Things. © 2022 IEEE.

关键词： Blockchain

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GLANCE: Global Actions in a Nutshell for Counterfactual Explainability

arXiv

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arXiv 2024年

作者： Kavouras, Loukas Psaroudaki, Eleni Tsopelas, Konstantinos Rontogiannis, Dimitrios Theologitis, Nikolaos Sacharidis, Dimitris Giannopoulos, Giorgos Tomaras, Dimitrios Markou, Kleopatra Gunopulos, Dimitrios Fotakis, Dimitris Emiris, Ioannis Institute for the Management of Information Systems Athena Research Center Athens Greece Department of Electrical and Computer Engineering National Technical University of Athens Athens Greece Université Libre de Bruxelles Bruxelles Belgium Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens Greece Department of Informatics Athens University of Economics and Business Athens Greece Archimedes Research Unit Athena Research Center Athens Greece

The widespread deployment of machine learning systems in critical real-world decision-making applications has highlighted the urgent need for counterfactual explainability methods that operate effectively. Global counterfactual explanations, expressed as actions to offer recourse, aim to provide succinct explanations and insights applicable to large population subgroups. Effectiveness is measured by the fraction of the population that is provided recourse, ensuring that the actions benefit as many individuals as possible. Keeping the cost of actions low ensures the proposed recourse actions remain practical and actionable. Limiting the number of actions that provide global counterfactuals is essential to maximize interpretability. The primary challenge, therefore, is balancing these trade-offs—maximizing effectiveness, minimizing cost, while maintaining a small number of actions. We introduce GLANCE, a versatile and adaptive framework, comprising two algorithms, that allows the careful balancing of the trade-offs among the three key objectives, with the size objective functioning as a tunable parameter to keep the actions few and easy to interpret. C-GLANCE employs a clustering approach that considers both the feature space and the space of counterfactual actions, thereby accounting for the distribution of points in a way that aligns with the structure of the model. T-GLANCE provides additional features to enhance flexibility. It employs a tree-based approach, that allows users to specify split features, to build a decision tree with a single counterfactual action at each node that can be used as a subgroup policy. Our extensive experimental evaluation demonstrates that our method consistently shows greater robustness and performance compared to existing methods across various datasets and models. Copyright © 2024, The Authors. All rights reserved.

关键词： Decision trees

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Exploring Machine Learning's Role in Intrusion Detection Systems for Network Security

Exploring Machine Learning's Role in Intrusion Detection Sys...

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Emerging Smart Computing and informatics (ESCI), Conference on

作者： P.R. Rege Aarti Kalnawat Anishkumar Dhablia Rahul Sharma Rupali Sachin Kaldoke Rashmi Ashtagi Computer VIIT Pune Maharashtra India Symbiosis Law School Nagpur Campus Symbiosis International (Deemed University) Pune India Altimetrik India Pvt Ltd Pune Maharashtra India Auricle Global Society of Education and Research Bikaner Rajasthan India Nutan Maharashtra Institute of Engineering and technology Pune Maharashtra India Department of Computer Engineering Vishwakarma Institute of Technology Pune Maharashtra India

The proliferation of network-based services has resulted in an expanded threat landscape within the domain of cybersecurity. In the realm of network security, Intrusion Detection System (IDS) assume a critical function in the protection of networks by identifying instances of unauthorized access and malicious behaviors. This research study does a comprehensive investigation into the effectiveness of different machine learning algorithms within the domain of IDS with the purpose of enhancing network security. The scope of our study centers around four key machine learning algorithms, namely Support Vector Machine (SVM), Random Forest, Naive Bayes, and ensemble techniques incorporating stacking. By utilizing the NSL-KDD dataset, which depicts a practical and demanding network traffic situation, we assess the efficacy of these algorithms in terms of accuracy, a pivotal measure for the effectiveness of IDS. The experimental findings demonstrate that the machine learning models exhibit diverse levels of effectiveness in the task of intrusion detection. Support Vector Machines (SVM) have been shown to possess the ability to effectively define intricate decision boundaries and get a high level of accuracy in detecting network anomalies. The Random Forest algorithm demonstrates resilience and flexibility, effectively managing a wide range of patterns present in the dataset. The Naive Bayes algorithm, renowned for its simplicity and computational efficiency, has a commendable level of performance in comparison to other methods. In addition, we explore ensemble strategies, with a specific focus on stacking, in order to leverage the combined capabilities of these algorithms. The results of our study demonstrate that an ensemble of machine learning models, including Support Vector Machines (SVM), Random Forest, and Naive Bayes, when combined using stacking approaches, may achieve a remarkably high accuracy rate of 98.66%. The utilization of an ensemble technique showcases enhan

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Applications of Machine Learning in Diabetic Foot Ulcer Diagnosis using Multimodal Images: A Review

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IAENG International Journal of Applied Mathematics 2023年第3期53卷 75页

作者： Mayya, Veena Tummala, Venkat Reddy, Chaduvula Upendra Mishra, Pranjal Boddu, Rajasekhar Olivia, Diana Kamath, S Sowmya Department of Information & Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India Department of Information & Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India Department of Software Engineering College of Computing and Informatics Haramaya University Dire Dawa Ethiopia Department of Information & Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India Department of Information Technology National Institute of Technology Karnataka SurathkalMangalore575025 India

Diabetes related complications such as Diabetic Foot Ulcers (DFU) may necessitate recurrent hospitalisations and expensive treatments. Uncontrolled diabetes can result in severe DFUs, resulting in amputation of lower limbs or feet, prolonged debilitation and diminished quality of life. Early diagnosis and proactive management are reported to significantly enhance the prognosis and reduce the onset of further complications. In this study, research works on developing clinical decision support systems (CDSS) for the identification and segmentation of DFU are systematically reviewed. The techniques employed range from traditional image processing techniques to approaches based on deep learning (DL). A taxonomy of DFU CDSSs is presented, categorised into two groups: RGB-based techniques and thermal imaging-based approaches. To the best of our knowledge, this is the first attempt at a comprehensive study of CDSSs for DFU related investigative tasks, based on different imaging modalities. We also delve into the difficulties experienced in the process of creating efficient, reliable, and accurate models for the early detection of DFU, and highlight the vast potential for further research in this emerging domain. © (2023), (International Association of Engineers). All Rights Reserved.

关键词： Diseases

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