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Advances in Electrical, Electronics and Computational Intelligence (ICAEECI), International Conference on

作者： Mohammed Mahdi Hashim Aqeel A. Al-Hilali Haitham Bashar Qasim Omar Raad Salah Abdullah A. Nahi Ministry of Higher Education and Scientific Research Baghdad Iraq Department of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad Iraq Department of Computer Science AL-Esraa University College of Science Baghdad Iraq Department of Computer Technology Engineering Al-Hadba University College Mosul Iraq Department of Information Technologies Management Technical College Southern Technical University Basrah Iraq

The inconceivable volume of online images produced by websites and personal collections has made it difficult to retrieve images from vast databases accurately. Practically, consumers need help to get the relevant information they seek. Humans are able to identify images according to their apparent characteristics. In addition, the image pixels cannot offer enough detail for creating semantic ideas, rendering image annotation a difficult endeavor. A particularly more challenging assignment in computer Vision (CV) and multimedia studies is image annotation (IA), in which the method assigns keywords from the dictionary to a given digital image. The major aim of IA is to convert the given digital image into single or different labels. To address this issue, this study focused on the extraction of automatic features to improve image annotation. Different techniques have been used with the suggested method such as YCbCr, Local Binary Pattern (LBP), and Slantlet Transform (ST) to improve a Feature Extraction (FE). In addition, the most widely used datasets, such as Flickr8k, ESP-Game, and Corel-5k, were used to test the proposed study. The evaluation results demonstrated superior annotation flexibility, enhanced accuracy, and decreased computation cost.

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Intelligently driven performance management: an enabler of real-time research forecasting for innovative commercial agriculture

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SN Social sciences 2022年第9期2卷 1-36页

作者： Abeysiriwardana, Prabath Chaminda Jayasinghe-Mudalige, Udith K. Kodituwakku, Saluka R. Madhushani, K.B. Department of Agribusiness Management Faculty of Agriculture and Plantation Management Wayamba University of Sri Lanka Makandura Gonawila (NWP) Sri Lanka State Ministry of Skills Development Vocational Education Research and Innovation Sethsiripaya Stage I Battaramulla Sri Lanka Department of Statistics and Computer Science Faculty of Science University of Peradeniya Peradeniya Kandy 20400 Sri Lanka National Institute of Fundamental Studies (NIFS) Kandy Sri Lanka

Research in commercial agriculture is instrumental to achieve the targets set under the second Sustainable Development Goal (SDG), i.e., ‘Zero Hunger by 2030.’ Execution of research for the success of commercial agriculture becomes a tedious task and research organizations have been long struggling to assess their performance unequivocally in the face of COVID-19. Any evacuation plan in place to improve the performance, monitoring, and evaluation of a research institute must guarantee that the institute is on the right momentum and let it evades metrics-obsessed research drives during this pandemic. A survey was conducted through the participation of the topmost administrators attached to key research institutes working on agriculture in Sri Lanka to explore the current performance management practices deeply. The conclusions derived from a thematic analysis of the survey data were used to propose a set of solutions that facilitate a well-thought research agenda in a digitally transformed performance management system. The solutions imply that intelligently driven key performance measurements worked by artificial intelligence and big data could be used with policy innovations to support research integrity and assessment security within the coexistence of humans and machines for the well-being of research development in the commercial agriculture sector. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Artificial intelligence Commercial agriculture Digital transformation KPI Performance management Research institutes

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AI-enabled Crop Health Monitoring and Nutrient management in Smart Agriculture

AI-enabled Crop Health Monitoring and Nutrient Management in...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Suman Kumar Swarnkar Leelkanth Dewangan Omprakash Dewangan Tamanna Manishkumar Prajapati Fazle Rabbi Department of Computer Science and Engineering Shri Shankaracharya Institute of Professional Management and Technology Raipur India Department of CSE Datta Meghe Institute of Higher Education and Research (Deemed to be University) Faculty of Engineering & Technology (FEAT) Swangi Wardh Department of CS & IT Kalinga University Raipur Chhattisgarh India Department of Computer Science Shri D. N. Institute of Computer Applications Gujarat India Australian Computer Society Australia

The rapid advancement of artificial intelligence (AI) has paved the way for transformative applications in various fields, and agriculture is no exception. In this research paper, we explore the integration of AI technologies in smart agriculture to enhance crop health monitoring and nutrient management. By leveraging AI algorithms and techniques, we aim to improve the efficiency and sustainability of farming practices. The paper delves into the utilization of AI for early detection of crop diseases and pests, as well as the development of predictive models for precise nutrient management. Remote sensing and imaging technologies play a crucial role in real-time monitoring, allowing for informed decision-making. Through a series of case studies and evaluations, we demonstrate the effectiveness and potential benefits of AI-enabled crop health monitoring and nutrient management in modern agriculture. Despite the promising results, challenges and limitations are discussed, and future directions for research and application are suggested.

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Design of Intelligent Decision Support System With Ensembled Machine Learning to Predict Students' Performance

Design of Intelligent Decision Support System With Ensembled...

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2024 International Conference on Communication, computer sciences and Engineering, IC3SE 2024

作者： Deepan, S. Rajendran, Dineshkumar Dhaliwal, Navdeep Praveena, K. Lourens, Melanie Anandhi, R.J. School Of Cs & Ai Sr University Telangana Warangal India School Of Computing And Innovative Technologies British University Computer Games Design And Programming Viet Nam Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Dundigal Hyderabad India Durban University Of Technology Faculty Of Management Sciences South Africa New Horizon College Of Engineering Department Of Information Science Engineering Bangalore India

ISBN: (纸本)9798350366846

Evaluating student performance is important for universities and institutions in the current student education landscape because it helps them create models that work better for students. The automation of various features related to fundamental student traits and behaviours that manage massive amounts of data efficiently processes these. To handle student records that included information about students' behaviour and how it related to their academic performance, the companies employed models of classification with mining concepts. Additionally, the quality of result classification can be substantially improved by using learning analytics and educational Data Mining (EDM). The educational establishments are making an effort to lower the low student performance. To address this issue, numerous methods for assessing student performance have been devised, allowing the relevant faculties to intervene and enhance the final product. Three classes - Low Performance Student, Average Student, and Smart Student - were created using the K-Mean Clustering methodology for classifying student records. Features including grade point, number of deficits, student attendance, medium of education, and board of education are taken into account when classifying the data. In this case, the WEKA tool is also utilized for implementing the model and outcome assessments. © 2024 IEEE.

关键词： Decision support systems

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Trajectory analysis of the coronavirus pandemic and the impact of precautionary measures in the Kingdom of Bahrain

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Arab Journal of Basic and Applied sciences 2021年第1期28卷 135-144页

作者： Al Dallal, Ahmed Al Dallal, Jehad Alnaser, Waheeb E. Ashqar, Mohammed Y. Al-Anzi, Bader S. Department of Electrical and Computer Engineering University of Alberta Canada Information Science Department Kuwait University Kuwait Department of Physics College of Science University of Bahrain Bahrain Department of Mathematics College of Science University of Bahrain Bahrain Department of Environment Technologies and Management Kuwait University Kuwait

The Kingdom of Bahrain announced its first case of COVID-19 infection on February 24, 2020. Since that time, the government has imposed several restrictions such as closures, banning gatherings, and closing border crossings to limit the spread of the pandemic and relieve pressure on the healthcare system. This article provides an overview of the current trajectory of the pandemic in the Kingdom of Bahrain. In addition, the article introduces and applies a methodology to analyze the impact of the interventions and precautionary measures enforced by the government to limit the COVID-19 disease propagation. The results show that most of the enforced precautionary measures were effective in reducing the spread of the disease by a percentage ranging from 20.2% to 41.8%. A religious occasion in Bahrain—involving large gatherings—had increased the spread of the disease by 28.7%. Not enough evidence is found to conclude that reopening interventions had caused the disease to spread again. © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the University of Bahrain.

关键词： Coronavirus COVID-19 Kingdom of Bahrain precautionary measures trajectory

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Using the Cooperative Nodes for Diminishing the Packet Drops in Mobile WSN

Using the Cooperative Nodes for Diminishing the Packet Drops...

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Artificial Intelligence and Smart Systems (ICAIS), International Conference on

作者： Vetrimani Elangovan Dhawaleswar Rao C. H A. Arul Prasath G. Padmapriya Department of Computer Science and Engineering Presidency University Bangalore Karnataka India Department of Computer Science and Engineering Centurion University of Technology and Management Sitapur Gajapati Odisha India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India

In Mobile Wireless Sensor Network (MWSN), sensor nodes build an active wireless network lacking existing structure. The MWSN contains several mobile sensor nodes that move randomly in the topology. In MWSN, Diminishing the packet drops is a significant challenge. Several approaches are introduced to handle packet losses. However, these approaches can’t be able to manage the packet losses efficiently. To solve this issue, the Cooperative nodes for diminishing the packet drops in MWSN are used. In this approach, the cooperative nodes decrease packet loss during data transmission. In this approach, the suitable nodes observe every data transmission inside the communication range nodes. If suppose, any packet losses during data transmission, these cooperative nodes are captured the lost packets and then forward them to the near neighbor nodes. Finally, the data reach the destination via a regular route. As a result, avoids packet losses in the MWSN. Simulation results show that the proposed method performs better than the existing mechanisms.

关键词： Wireless sensor networks Network topology Wireless networks Simulation Packet loss Throughput Routing

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Deep learning-driven defense strategies for mitigating DDoS attacks in cloud computing environments

Cyber Security and Applications

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Cyber Security and Applications 2025年 3卷

作者： Afraji, Doaa Mohsin Abd Ali Lloret, Jaime Peñalver, Lourdes Department of Computer Engineering Universitat Politècnica de València Valencia Spain Department of Computer Science College of Education Mustansiriyah University Baghdad Iraq Integrated Management Coastal Zones Research Institute Universitat Politècnica de València Valencia Spain

The kind of cyber threat prevalent and most dangerous to networked systems is the Distributed Denial of Service (DDoS), especially with expanded connection of Internet of Things (IoT) devices. This article categorizes DDoS attacks into three primary types: volumetric, protocol based and application layer of cyber attacks. It discusses the application of security threats that arise from the use of the DL models, accusing recently introduced ideas and stressing pitfalls: the issues of data and methods scarcity. There is the same need for the greater use of explainable and transparent AI to improve confidence in such security systems as is noted in the review. It also reveals that present detection performance is constrained and frequently obstructed by the poor quality of the datasets. The future work is proposed to build superior datasets and use accurate algorithm to improve the security models. This paper focuses on explainability as a way of making the AI model creation process and any consequent decisions explainable and transparent. The use of deep learning enhances the capability of cybersecurity in handling DDoS attacks and preventing or controlling them. But it has to be a part of a more large-scope platform, based on multiple types of longitudinal or cross-sectional data combined with high efficiency, explainable AI. The article ends with call to proceed with studying and advancing the AI application in response to new threats, and make the most of it to enhance protection of the contemporary networked environment. © 2025

关键词： Cybersecurity Dataset limitations DDoS attacks Deep learning Explainable AI Internet of things Machine learning algorithms

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A Machine Learning-Based Forecasting Model with Deep Learning Capabilities for Unbalanced Time Series Data 2

A Machine Learning-Based Forecasting Model with Deep Learnin...

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2nd IEEE Global Conference on Communications and Information technologies, GCCIT 2024

作者： Amudha, A. Suri, Sahil Sindhu, M. Goyal, Shubhi Kukreja, Mansi Pathak, Kishor R. Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Quantum University Research Center Quantum University Uttarakhand Roorkee247667 India School of Mangement - Pg Management Karnataka Bangalore India Vishwakarma Institute of Technology Department of Information Technology Pune India

ISBN: (纸本)9798350388916

During the last several years, deep learning capabilities have surpassed those of more conventional models on a wide variety of machine learning-related tasks. Neural networks with deep layers have indeed been effectively used to time series forecasting issues, which are crucial to the field of data mining. Their ability to instantly understand the temporal connections contained in time series has made them a viable option. Yet, deciding on a deep neural network architecture and then optimizing its parameters calls for a high level of skill. Thus, it is essential to conduct comprehensive studies of the predictive capacities of all current architectural frameworks. The completion of this work is hampered by two primary factors: (1) a complete examination of the most recent research on depth learning's application to time series forecasting, and (2) an empirical inquiry analysing the effectiveness of the most commonly used architectures. Seven distinct deep learning models are evaluated and compared for their efficiency and accuracy. We present scores and distributions based on our analysis of the suggested models trained with various architectures and hyperparameter settings. The 50,000 time series in the databases are used to solve 12 unique forecasting issues. Using this data to train approximately 38,000 models, we present the largest deep learning research to date for forecasting time series. While LSTMs were shown to be the most significantly predictive, both LSTMs & CNNs showed promise in this research. Comparable performance is attained by CNNs, but with less variance in results as well as a more based on the best available overall. © 2024 IEEE.

关键词： Prediction models

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Effectual LDA Approach for Optimal Topic Modelling from Social Network Data 2

Effectual LDA Approach for Optimal Topic Modelling from Soci...

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2nd IEEE Global Conference on Communications and Information technologies, GCCIT 2024

作者： Dhabliya, Dharmesh Singh, K. Ranjith Singh, Neelesh Revathi, K. Goyal, Shobhit Shalini Vishwakarma Institute of Technology Department of Information Technology Maharashtra Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Chennai127 India Quantum University Research Center Quantum University Uttarakhand Roorkee247667 India School of Mangement - Pg Management Karnataka Bangalore India

ISBN: (纸本)9798350388916

Methods like document clustering & topic modelling in OSNs allow for the classification, annotation, and analysis of massive volumes of user-generated content. Using these methods, you may finally comprehend the material. Among the various approaches that have been developed throughout time are text mining and clustering techniques. Other techniques, such as hidden subject models & neural embedding approaches, have also been developed. Prior Inference for the Use of Latent Dirichlet Allocation is included as a comparison to our other topic models. Given the many accessible methods of assessment, we review the research to determine which extrinsic measures will provide the best results and then provide guidance on how to use them. Furthermore, we demonstrate the efficacy of the methods on data sets consisting of documents of varied lengths. Our study found that, when using appropriate extrinsic evaluation measures, the best results were obtained across all data sets by using clustering techniques to neural embedding versions of features. Using tf-idf weights & embedding distance measurements, we also provide a top-words based approach to make sense of the clusters. This strategy is predicated on the notion that a sentence's most important words are also the ones that occur most often in it. © 2024 IEEE.

关键词： Embeddings

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Blockchain Enabled Privacy- Preserved Secure e-voting System for Smart Cities

Blockchain Enabled Privacy- Preserved Secure e-voting System...

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science, Technology, Engineering and management (ICSTEM), International Conference on

作者： P. Chinnasamy Ramesh Kumar Ayyasamy Poovendran Alagarsundaram S. Dhanasekaran B.Santhosh Kumar Ajmeera Kiran Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Malaysia Humetis Technologies Inc Kingston NJ USA Department of Information Technology Kalasalingam Academy of Research and Education (Deemed to be University) Srivilliputtur Tamilnadu India Department of Computer Science & Engineering Guru Nanak Institute of Technology Ibrahimpatnam Telangana

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

In today's world, voting online is becoming increasingly popular. It has a lot of power to reduce administrative costs and increase the number of voters. Eliminates the need for ballot papers or polling stations, allowing citizens to vote anywhere via the Internet. Apart from these benefits, online voting methods are viewed with extreme caution as they pose additional risks. One mistake can easily lead to disaster. When used in elections, electronic voting systems must be legitimate, accurate, secure, and competent. However, the ability to have difficulty with computer voting methods can reduce acceptance. Blockchain technology is created to address these concerns and provides separate nodes for electronic voting. It is used to create electronic voting systems because of its ultimate verification benefits. With distributed, non-disposable features, and security protection, this technology is a great way to establish common electronic voting solutions. Blockchain is a system where each item is treated as a block, with a link that connects all these blocks, hence the name Block-Chain. Each block contains all possible data for one business, as well as a timestamp and, in exceptional cases, once. The hashing function is used to determine the hash value of all data in a block. All data blocks are accelerated with the same function. The field across all blockchain blocks contains the precedent block address. Improving security Face recognition is used. Face recognition helps to ensure that the voter is legal or not. It helps election planners identify fraudulent voters and removes them from participating in the voting process. Voting with E using the blockchain facilitates the proper distribution of votes and ensures the security of total data throughout the process. It makes the election clean and protects the vote of the affected people.

关键词： Electronic voting systems Costs Smart cities Law Face recognition Disasters Blockchains

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