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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Kadhim, Aqeel Kamil Najim, Ali Hamza Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisa Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

In VANETs, the important and effective applications of vehicle localization include safety and communication applications. Thus, it is difficult to get very accurate localization in the dynamic and often very fluctuating environment of VANET. This research responds to this challenge with a keen thrust on improving vehicle localization using the Mobility Data Verification process. The primary goal of this work is to achieve high accuracy of vehicle positioning within VANET by checking the coherence of mobility data of moving vehicles. Hence, we present a concept that builds upon Mobility Data Verification to provide real-time location details from vehicles. Both GPS and onboard sensors feed this method, which uses a multi-level verification to flag out or eliminate the errors of deliberately spoofed data points. We use simulations to evaluate and validate the effectiveness of the proposed verification approach. As the proposed system, our previously developed and fine-tuned weighted localization (OWL) strategy does so. The performance evaluation results suggest that our proposed solution is feasible to distinguish automobiles that are not trustworthy and improve the performance of the localization algorithm. © 2024 IEEE.

关键词： Vane pumps

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Smart Water-Powered Renewable Energy System with IoT Integration

Smart Water-Powered Renewable Energy System with IoT Integra...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Najim, Ali Hamza Ibraheem, Ahmed Salman Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq Department of Computer Technical Engineering Baghdad Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

An advanced hybrid renewable energy collecting and storage system prototype that utilizes water flow as its primary power source. Additionally, the system incorporates an energy monitoring system based on the Internet of Things (IoT) technology. The device effectively harnesses energy from the movement of water, storing it for future use in low-voltage applications. It demonstrates a maximum daily energy capture capacity of about 100Wh. The energy monitoring system, based on the Internet of Things (IoT), enables the real-time and remote monitoring of energy harvesting and usage. We thoroughly assessed the energy harvesting component across different situations to identify the crucial parameters that impact energy output. The results of our study demonstrate that several elements, including the spatial separation 'between the energy harvester and the water source, Water flow' speed, sensor orientation, and sensor density, all play significant roles in determining the quantity of energy collected. This research shows that it is possible to integrate IoT technology with hydropower renewable energy systems. It provides significant perspectives on enhancing energy extraction efficiency in practical scenarios. © 2024 IEEE.

关键词： harvesting renewable energy IoT storage energy water flow energy

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Tomato Leaf Diseases Prediction Using Deep Learning Algorithms

Tomato Leaf Diseases Prediction Using Deep Learning Algorith...

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2024 International Conference on Advances in Data engineering and Intelligent Computing systems, ADICS 2024

作者： Ananthi, P. Devi, K. Nirmala Gopinath, D. Shanmugapriya, P. Gowtham, S. Kongu Engineering College Department Of Computer Technology Tamilnadu Perundurai India Bangaluru India Kristu Jayanti College Department Of Computer Science Bangaluru India Software Systems Kongu Engineering College Erode India

ISBN: (纸本)9798350364828

In the realm of agriculture, infections on tomato leaves pose a worldwide danger to established tomato production, impacting a large number of farmers worldwide. To ensure healthy tomato plant growth and food security for the world's expanding population, early disease detection, treatment, and solution identification are essential. The use of computer-assisted technologies for disease detection in plant leaf is prevalent in modern agriculture. The goal was to develop an effective deep learning model for the detection of diseases in tomato plant leaf. To address memory and computational resource constraints, the proposed model incorporates a lightweight 'Region Convolutional Neural Network (R-CNN)' head and conducted experiments with various neural network architectures, including Densenet-169, optimized Mobilenet-V2 and Resnet-50, to enhance detection accuracy and computational efficiency. The research extends the capabilities of these neural network architectures by optimizing anchor proportions in the Conventional Neural Network (CNN) and modifying feature extraction topologies. These optimizations resulted in improved detection accuracy and computational efficiency and compared proposed technique against cutting-edge models to evaluate its viability and robustness. The outcomes of the research were promising. Impressive outcomes were obtained by the proposed approach regarding accuracy, F1-score, recall, and precision. © 2024 IEEE.

关键词： Computational efficiency

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Accuracy in Wireless Communication: Improved Arrival Direction Estimation in Adaptive Antennas via Expanded MUSIC Beamforming

Accuracy in Wireless Communication: Improved Arrival Directi...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Kumar, Kapil Pandey, Priyank Khari, Manju Jawaharlal Nehru University School of Computer & Systems Sciences New Delhi India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350382723

Smart antennas are now widely expended in transmission systems due to their vital plusses. To boost the performance of smart antenna operation, it is crucial to design an efficient beamforming pattern based on specific antenna parameters. Due to their numerous benefits, smart antennas are now often utilized in communication systems. It is essential to create a successful beamforming pattern based on certain antenna parameters with the objective of improvement in the performance of smart antenna operation. Beamforming strategies utilizing a combination of soft computing techniques were proposed in earlier works. These methods, however, did not take signal Direction of Arrival (DOA) precision into account. While determining the beamforming pattern for smart antennas, this research addresses the DOA issue. The researchers use a variety of techniques and conduct a comparison analysis to determine an exact DOA estimate. The authors proposed a method that shows an optimal result after resolving the existing problems for estimating the DOA with precision. In comparison to the traditional DOA pattern, this innovation produces a more accurate DOA pattern with reduced undesired side lobes. The researchers derive the antenna beamforming pattern in accordance with the necessary direction of angles using the predicted DOA pattern. The experimental results demonstrated that the proposed beamforming technology performs better than earlier techniques. In this work, advancement in beamforming precision can lead to better communication system performance and overall efficiency of smart antennas. © 2024 IEEE.

关键词： Beamforming

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Computational Design of Fly Ash Geopolymer Mortar Using Experimental and Attribute Evaluation Approaches 1st

Computational Design of Fly Ash Geopolymer Mortar Using Expe...

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1st International Conference on Advanced Materials Manufacturing and Structures, ICAMMS 2024

作者： Mishra, Anant Lahoti, Mukund Bajpai, Rishabh Narang, Pratik Srivastava, Anshuman Sharma, Akshay Department of Civil Engineering BITS-Pilani Pilani333031 India Department of Computer Science Science and Information Systems BITS-Pilani Pilani333031 India

ISBN: (纸本)9783031725265

Geopolymer is a ceramic-like inorganic material synthesized at room temperature and is a potential sustainable replacement of Portland cement. In the present work, a comprehensive experimental program was designed to evaluate the relative importance of mix design factors controlling the strength of fly ash geopolymer mortar. Restrained factors, namely, temperature of curing;alkaline solution to fly ash (L/FA) ratio;sodium silicate to sodium hydroxide (SS/SH) ratio;sodium hydroxide molarity;and fly ash to sand (FA/Sand) ratio, and unrestrained factors, namely, H2O/Na2O;SiO2/Al2O3;SiO2/Na2O;and Al2O3/Na2O molar ratios, were considered for evaluation. Feature subset selection and multivariate adaptive regression splines (MARS) techniques were used to determine the significance of these factors. Results show that temperature of curing is the most significant factor. FA/Sand and L/FA are found to affect compressive strength more significantly than sodium hydroxide molarity and SS/SH. Except for H2O/Na2O molar ratio, other molar ratios were observed to be very less significant. It is noted that mix design of geopolymer mortar should not be based on the molar ratios, instead mix design must be prepared by controlling the restrained factors. The findings of this study should be helpful in optimization of design factors leading to a robust geopolymer mix. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Mortar

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Facial Expression Recognition Model Depending on Optimized Support Vector Machine

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computers, Materials & Continua 2023年第7期76卷 499-515页

作者： Amel Ali Alhussan Fatma M.Talaat El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Doaa Sami Khafaga Mona Alnaggar Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Machine Learning&Information Retrieval Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Robotics and Intelligent Machines Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt

In computer vision,emotion recognition using facial expression images is considered an important research *** learning advances in recent years have aided in attaining improved results in this *** to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of *** is feasible and useful to convert face photos into collections of visual words and carry out global expression *** main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is *** uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos *** FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization *** discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously *** search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.

关键词： Facial expression recognition machine learning linear dis-criminant analysis(LDA) support vector machine(SVM) grid search

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Detection of Diabetic Retinopathy Severity by Image Classification Using a Hybrid Model and Attention Mechanism 7th

Detection of Diabetic Retinopathy Severity by Image Classi...

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7th International Conference on Advanced Computing and Intelligent engineering, ICACIE 2022

作者： Nunna, Bhargava Satya Tiwari, Ashutosh Department of Computer Science and Systems Engineering Andhra University Visakhapatnam India Geosciences Virginia Tech Blacksburg United States

ISBN: (纸本)9789819950140

Diabetic Retinopathy (DR) is an eye condition, caused by the complications of diabetes mellitus and this could even lead to extent of vision loss. DR is an irreversible process, early diagnosis and proper treatment could help to sustain vision from any further loss. It is diagnosed through colored fundus images, which require an ophthalmologist to identify the presence of lesions in images and detect the severity of the disease. As with the exponential growth of the population, people suffering with DR were also increasing rapidly and in this case, a manual diagnosis process by clinicians would be time-intensive, tedious, and cost-consuming. An effective automated DR detection using image analysis can prevent misdiagnosis, reduce time, cost, and effort. In this work, we present a novel deep learning architecture that comprises a state-of-the-art Convolutional Neural Networks (CNN) integrated with a self-attention module recently originated from a transformers network. A self-attention mechanism was applied to the image feature maps extracted from the two state-of-art architectures ResNet and DenseNet, such that the system learns from the features extracted from both the models and with respect to each other. The proposed method has obtained a propitious performance with an accuracy of 89.09%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image classification

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A Metamodel for a DSL to Design Decision Support systems in Smart Tourism

A Metamodel for a DSL to Design Decision Support Systems in ...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ouaddi, Charaf Benaddi, Lamya Souha, Adnane Naimi, Lahbib Bouziane, El Mahi Jakimi, Abdeslam Moulay Ismail University Software Engineering and Information Systems Engineering Team Faculty of Science and Technology Errachidia Department of Computer Sciences Morocco

ISBN: (纸本)9798350369106

The development of Decision Support systems (DSS) for several companies operating in sectors such as tourism, healthcare, or others, presents significant challenges due to the nature of their multi-component architecture. A typical DSS consists of a data component, a model component incorporating machine learning and deep learning techniques, and a user interface component, each one requires diverse technologies. This complexity increases development time and effort, hindering the efficient design of DSS tailored to specific domains like tourism, where real-time decision-making is essential. One promising approach to overcome these challenges is the use of Domain-Specific Languages (DSLs). DSLs allow developers to model and generate code more efficiently by providing high-level abstractions suited to the domain. By leveraging DSLs, it is possible to simplify the design process and automate the generation of code or parts of the DSS, thereby reducing the overall complexity and time required for development. This paper proposes a metamodel for designing DSS, specifically for the Smart Tourism sector. The metamodel serves as a foundation for the creation of DSL. The goal is to provide a solution to facilitate the development of DSS for the demands of tourism. The paper outlines the key concepts, approach, and results, presenting the first element of the proposed DSL which is the metamodel capturing the essential components and relationships for DSS in Smart Tourism. © 2024 IEEE.

关键词： Digital subscriber lines

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Density Based Spatial Clustering of Applications with Noise and Sentence Bert Embedding for Indonesian Utterance Clustering

Density Based Spatial Clustering of Applications with Noise ...

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2023 International Conference on computer science, Information Technology and engineering, ICCoSITE 2023

作者： Hasani, Muhammad Fikri Heryadi, Yaya Arifin, Yulyani Lukas Suparta, Wayan Bina Nusantara University BINUS Graduate Program Computer Science School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350320954

Task oriented chatbots are a sub-topic related to chatbots, where chatbots will perform certain tasks with specific goals. One part of creating a task-oriented chatbot is doing intent classification. Intent classification is a task of text classification. As in general text classification, the required dataset requires a label to carry out the classification process. To speed up and help the utterance analysis process, there is already a method, namely clustering, and Density-based clustering is a part of clustering that can determine cluster patterns based on arbitrary data, with DBScan as one of its algorithms. This research used 10000 client utterance data of awhatsapp based e-commerce conversation. SentenceBert also used as a state of art sentence embedding. This research yield silhouette score of 0.327 as the best result from eps of 0.1 and MinPts of 95. However, based on the cluster result, sentences labelled as noise can be further clustered. Text Preprocessing, text augmentation and sentence embedding techniques can be explored to increase the cluster performance. © 2023 IEEE.

关键词： Embeddings

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Arabic text sentiment analysis and emotion classification using transformers 11

Arabic text sentiment analysis and emotion classification us...

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11th International Japan-Africa Conference on Electronics, Communications, and Computations, JAC-ECC 2023

作者： Mossad, Nada Mohamed, Youssef Fares, A. Zaky, Ahmed B. Alexandria21934 Egypt Benha University The Computer Systems Engineering Program Faculty of Engineering at Shoubra The Department of Electrical Engineering Egypt

ISBN: (纸本)9798350369328

Sentiment analysis and emotion classification are two crucial components of natural language processing (NLP), which have been widely explored in recent years due to their broad applications. Sentiment analysis aims to identify the polarity of written texts, ranging from positive to negative. Meanwhile, emotion classification is focused on recognizing and categorizing the emotional states expressed in the text. To achieve a deeper understanding of sentiments and emotions, it's essential to utilize models like BERT transformers that can effectively interpret the context. The process begins with data preprocessing, including tokenization and noise removal, followed by fine-tuning techniques to adapt the BERT model to the proposed tasks. We employed the BERT model on four datasets obtained from various sources, including Twitter, news websites, and restaurant reviews, where each dataset represents a distinct Arabic dialect. Our proposed model outperforms commonly used techniques like LSTM and CNN, yielding superior results. Despite the progress made, there are still challenges to overcome, such as dealing with Arabic diacritics, the new Arabic Arabizi, which uses Latin characters, and handling Arabic idioms. Further research is required to address these challenges adequately. © 2023 IEEE.

关键词： Sentiment analysis

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