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Hybrid ConvLSTM with Attention for Precise Driver Behavior Classification

Hybrid ConvLSTM with Attention for Precise Driver Behavior C...

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Artificial Intelligence and Knowledge engineering (AIKE)

作者： Ghulam Mustafa Seong-Je Cho Youngsup Hwang Dept. of Computer Engineering Sun Moon University Asan-si South Korea Dept. of Software Science Dankook University Yongin South Korea

Driving behavior classification plays an important role in many real-world applications, including traffic accident prevention, driver safety, usage-based insurance, and optimizing ridesharing services. In this research, we introduce a novel approach called Hybrid ConvLSTM with Attention for Driver Behavior Classification (HCLA-DBC), designed to achieve precise and reliable driver behavior classification. Our main focus is to distinguish three fundamental driving behavior classes: normal, drowsy, and aggressive, which play a vital role in supporting advanced driver-assistance systems (ADAS). To evaluate the performance of our proposed models, we conducted experiments using the publicly available UAH-DriveSet dataset. The evaluation comprised six distinct models, encompassing two machine learning models (SVM and Gradient Boosting) and four deep learning models (CNN, MLP, LSTM, and HCLADBC). Our HCLA-DBC model outperformed all other models and previous results on the same data. On the test data, it achieved an accuracy of 94.12%, precision of 94.24%, recall score of 94.12%, F1-score of 94.12%, and a macro-average ROC AUC of 98.83. These exceptional outcomes underscore the efficacy of our hybridized approach and its potential to significantly enhance driver behavior classification. The HCLA-DBC model not only promises new possibilities for driver behavior analysis but also paves the way for elevated ADAS capabilities and safer driving experiences.

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SWIN and Vision Transformer-Driven Crack Detection in the Al Qattara Oasis, UAE: Towards Sustainable Infrastructure Management

SWIN and Vision Transformer-Driven Crack Detection in the Al...

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IEEE/ACM International Symposium on Big Data Computing (BDC)

作者： Luqman Ali Medha Mohan Ambali Parambil Muhammed Swavaf Fady Alnajjar Hamad Aljassmi Adriaan De Man Dept. Computer Science and Software Engineering Emirates Centre for Mobility Research United Arab Emirates University Al Ain UAE Dept. Computer Science and Software Engineering United Arab Emirates University Al Ain UAE Dept. Civil and Enviromental Engieering Engieering Emirates Centre for Mobility Research Al Ain UAE Dept. Tourism and Heritage United Arab Emirates University Al Ain UAE

ISBN: (数字)9798350367300

ISBN: (纸本)9798350367317

Heritage sites are central to the cultural identity and historical narrative of a community. The Al Qattara Oasis, located in the United Arab Emirates (UAE), illustrates this function well. This study addresses the pressing need to preserve and maintain built heritage by employing modern technological solutions for infrastructure upkeep. Specifically, it focuses on crack detection, a critical aspect of ensuring the structural integrity of heritage buildings. Utilizing data collected from various structures across the UAE, obtained through handheld cameras, the effectiveness of Vision Transformers (ViTs) and Swin Transformers in identifying cracks within Al Qattara is assessed. The research thoroughly evaluates models, with particular attention to different input patch sizes. Through systematic experimentation and analysis over 100 epochs, ViT models with a patch size 16 exhibit significant promise in crack detection. Notably, the ViT-16 model achieves good performance metrics, including training and validation accuracies of 85% and (82%) respectively, and correctly classifies 791 out of 957 cracks and 772 out of 903 non-cracks. In contrast, Swin Transformers show lower validation accuracies (70-74%) and higher misclassification rates. The outcomes underscore the potential of ViT models in enhancing infrastructure maintenance efforts within heritage sites like Qattara Oasis. By combining advanced technology with a profound respect for historical preservation, this study aims to contribute to the sustainable conservation and protection of cultural heritage, ensuring that future generations can continue to appreciate the enduring legacy of sites such as Qattara Oasis.

关键词： Training Measurement computer vision Accuracy Systematics Pressing Transformers Maintenance Cultural differences Protection

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Stock Price Prediction Using LSTMSeq2Seq: A Deep Learning Approach with Advanced Feature engineering

Stock Price Prediction Using LSTMSeq2Seq: A Deep Learning Ap...

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International Conference on computer and Information Technology (ICCIT)

作者： Md. Kalim Amzad Chy Abdul Kadar Muhammad Masum Mohammed Shamsul Alam Md. Abul Kalam Azad Md Zia Uddin Dept. of AI Innovation Liberate Labs Texas USA Dept. of Software Engineering Daffodil International University Dhaka Bangladesh Dept. of Computer Science and Engineering International Islamic University Chittagong Chittagong Bangladesh Dept. of Business and Technology Management Islamic University of Technology Gazipur Bangladesh Software and Service Innovation Department SINTEF Digital Oslo Norway

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

This paper explores stock price prediction in the context of the Bangladesh stock market using advanced deep learning methodologies, specifically the LSTMSeq2Seq model. Traditional analytical techniques often fail to capture the complex, non-linear patterns inherent in financial data. To address this limitation, our research leverages the strengths of the LSTM-Seq2Seq architecture, which is tailored to the unique dynamics of the Bangladesh market. A robust feature engineering process is employed, utilizing 67 diverse features, including market trends, stock-specific indicators, fundamental and technical variables, and key macroeconomic factors such as gold prices and foreign exchange reserves. The dataset encompasses historical stock market data from 2012 to 2022, and extensive data preprocessing is performed to handle inconsistencies. Hyperparameter optimization is conducted using grid search to ensure model performance. The LSTMSeq2Seq model is evaluated based on key metrics such as MAE, MAPE, MSE, and the R 2 score, demonstrating superior predictive capabilities compared to traditional methods. This research not only advances the application of deep learning in financial forecasting but also provides valuable insights into stock price prediction in emerging markets, addressing ethical concerns to mitigate risks like market manipulation.

关键词： Deep learning Measurement Sentiment analysis Computational modeling Time series analysis Predictive models Macroeconomics Stock markets Forecasting Long short term memory

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Automating the Diagnosis of Cucumber Plant Diseases Using Machine Learning

Automating the Diagnosis of Cucumber Plant Diseases Using Ma...

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Information Technology (ACIT)

作者： Ahmed BaniMustafa Hazem Qattouz Sattam Almatarneh Khalid Mansour Raed Alazaidah Olla Bulkrock Computer Information Science Dept Higher Colleges of Technology Abu Dhabi UAE Software Engineering Dept Princess Sumaya University for Technology Amman Jordan Data Science & Artificial Intelligence Dept Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah KSA Faculty of Computer Studies Arab-Open University Bahrain Bahrain

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Farmers all over the world struggle to fight plant diseases. However, the incorrect use of pesticides can waste time, effort, and money and can also be very harmful to the environment and human health. In this work, we present an empirical study that involves identifying two pathogens related to Cucumbers plant diseases: Powdery Mildew and Alternaria Cucumerina. The study aims to develop a computer-based solution based on deep learning and machine learning algorithms to detect these diseases. To achieve this, a dataset of hundreds of samples was collected from ordinary farms in Beqaa, an agricultural region 15 KM north of Amman, Jordan. Three machine learning models were created in this study using Support Vector Machines (SVM), Logistic Regression (LR), and Multi-layer perceptron algorithms. The constructed models were applied to the dataset after embedding the dataset images using the Convolutional Neural Networks (CNN) InceptionV3 pre-trained model. The model was tested and evaluated using cross-evaluation and F1-Score, CA, AUC, Precision, Recall, and Specificity, which all showed excellent results, particularly using the SVM algorithm. The constructed methods are intended to be deployed to farms in their field condition environments using a CCTV system that can capture images of plants during growth, which can help identify plant diseases at that right stage, which would save time, effort, and money and reduce the impact of wrong and overuse of pesticides on consumers and the environment.

关键词： Support vector machines Plant diseases Pathogens Machine learning algorithms Pesticides Soil Cameras Real-time systems Mobile applications Convolutional neural networks

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Harnessing LLMs for VQA: A Prompted Benchmark with Animate/Inanimate Keywords

Harnessing LLMs for VQA: A Prompted Benchmark with Animate/I...

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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Chanwoo Lee Hyunjeong Lee Minsang Kim Hyun Kim Haneol Jang Cheoneum Park Dept. of Software Convergence Seoul Woman's University Seoul South Korea Dept. of Computer Engineering Hanbat National University Daejeon South Korea Dept. of Computer Science and Engineering Korea University Seoul South Korea Superintelligence Creative Research Lab. Electronics and Telecommunications Research Institute Daejeon South Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

In the field of NLP, Large Language Models (LLMs) have recently achieved significant advancements, leading to the development of various benchmarks for their evaluation. Along-side NLP, Vision Language Models (VLMs) have also VLM have also significantly progressed, similar to LLMs. However, benchmarks for VLMs are still relatively underdeveloped compared to those for NLP, and their construction is often costly. In this work, we propose an automatically generated benchmark for evaluating VLMs based on LLMs and conduct a visual question answering task to assess this benchmark. The benchmark includes multiple-choice questions that not only distinguish between animate and inanimate objects but also generate these distinctions automatically, along with entity and object information within images. We evaluate the performance of open VLM using the generated multiple-choice questions, demonstrating the model's capabilities and the significance of the automatically generated benchmark. Finally, we discuss the necessity and future directions for benchmark research in this area.

关键词： Visualization Annotations Large language models Natural languages Benchmark testing Information filters Question answering (information retrieval) Cognition Information and communication technology Convergence

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Macula Swelling Detection using Retinal Fundus Images

Macula Swelling Detection using Retinal Fundus Images

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IEEE International Workshop on Imaging Systems and Techniques (IST)

作者： Rukham Mumtaz Khan Fazal Saadat Ali Khan Fauzia Khan Rafia Hassan Dept. of Computer Engineering National University of Sciences and Technology Islamabad Pakistan Dept. of Information Security National University of Sciences and Technology Islamabad Pakistan Dept. of Computer Science University of Tartu Tartu Estonia Dept. of Software Engineering Bahria University Islamabad Pakistan

Diabetic Macular Edema (DME) is an advanced symptom of Diabetic Retinopathy (DR) and can lead to irreversible vision loss. Macular Edema (ME) is the swelling of the macula, the part of the eye accountable for thorough central vision. The utilization of image processing in the detection of diabetic macular edema (DME) plays a crucial role in the field of medical imaging, effectively reducing the time and manual effort required by medical specialists to identify this disease. In this paper, we proposed a method that uses digital fundus images to analyze the swelling of the macula. The proposed algorithm consists of four steps, i.e., (i) retinal image dataset collection, (ii) retinal image pre-processing, (iii) feature extraction, and (iv) classification to categorize the disease into three different stages, i.e., normal, stage 1 (mild) and stage 2 (severe). The proposed method is tested on three databases, i.e., MESSIDOR, DMED, and the local dataset AFIO obtained from the local hospital in Pakistan. We use three parameters to check the validity of the proposed algorithm, i.e., visual inspection, accuracy, and area under calculation time. Our method achieves an average accuracy of 96.07 % with a sensitivity of 93.74 % and specificity of 97.32%. We also compare our results with other proposed methods, which shows our method performs better.

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Aerial Vehicle Detection and Classification Through Fusion of Multi-Domain Features of Acoustic Signals

Aerial Vehicle Detection and Classification Through Fusion o...

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engineering & Computing Technologies (ICECT), International Conference on

作者： Malaika Sumble Sumair Aziz Muhammad Umar Khan Khushbakht Iqtidar Raul Fernandez-Rojas Department of Electronics Engineering University of Engg. & Tech. Taxila Taxila Pakistan Faculty of Science and Technology University of Canberra Canberra ACT Australia Dept. of Computer & Software Engineering National University of Sciences & Tech. Islamabad Pakistan

ISBN: (数字)9798350349719

ISBN: (纸本)9798350349726

Detection of aerial vehicles is a challenging task. Over time, the significance of developing such vehicles has increased rapidly. However, an utmost concern arises from the potential misuse of these small-sized vehicles in illicit activities such as unauthorized surveillance, smuggling contraband, or disrupting critical infrastructure significant security threats. Recognizing the gravity of this issue, we have opted to introduce an innovative approach to automate the detection processes for aerial vehicles. This initiative aims to deploy effective drone detection systems to mitigate risks and safeguard against illegal activities. We put use to two datasets from GitHub depot. Cepstral, spectral, and time domain features were extracted from the data, followed by classification. We conducted two experiments, with the first (Drone, No-Drone) yielding 98.6% accuracy, using the Ensemble (Bagged Trees) classifier. The second set of experimentation addressed five classes: Background Noises, Bebop Drone, Drone, Helicopter, and Mambo Drone. Ensemble (Bagged trees) again outperformed all other classifiers and achieved 98.3% accuracy. The results highlight that our proposed framework gives effective results based on audio signals of different aerial vehicles.

关键词： Machine learning algorithms Accuracy Cepstral analysis Surveillance Signal processing algorithms Feature extraction Time-domain analysis

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Designing and Simulating a Smart Water Harvesting System Using PLC Techniques

Designing and Simulating a Smart Water Harvesting System Usi...

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Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), International Conference o

作者： Hassaan Th. H. Thabet Aqeel A. Al-Hilali Haitham B. Qasim Thabit H. Thabit Firas M. Mahmood Dept. of Medical Instruments Techniques Engineering Al-Hadba University College Mosul Iraq Dept. of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad Iraq Dept. of Computer Science Al-Esraa University College Baghdad Iraq Dept. of Computer Engineering and Information Ninevah University Mosul Iraq Dept. of Informatics and Software Engineering Cihan University-Erbil Iraq

The presence of water in human life is crucial, and God has blessed humans much with its availability. This paper tries to reduce the amount of water that people use for washing in all aspects of daily life and instead utilize it to irrigate gardens and maintain hygienic facilities. The importance of water as a fundamental ingredient and the range of social and age groups that regularly take advantage of the washing process made it an ideal location for water waste during the washing process. This is why the authors choose this region to conduct their research. The monitoring and management of water usage during the washing process are simulated using PLC techniques.

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Sankalpa - Interactive Digital Learning Platform to Enrich Students with Special Needs

Sankalpa - Interactive Digital Learning Platform to Enrich S...

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Advancements in Computing (ICAC), International Conference on

作者： A.I Basnayaka E.K.G.D. Epitakaduwa C.P.U Jayawardena Ashan Zoysa Uditha Dharmakeerthi Sanath Jayawardena Dept of Computer Science and Software Engineering Sri Lanka Institute Of Information Technology Malabe Sri Lanka Dept of Computer Science and Information Technology Sri Lanka Institute Of Information Technology Malabe Sri Lanka Dept of Computer Science and Network Engineering Sri Lanka Institute Of Information Technology Malabe Sri Lanka

The term special needs mean the special educational needs that the students who suffer from learning disabilities must have due to several reasons. Learning impairment is a condition of the neurological system that impairs one's capacity to efficiently acquire and process knowledge. Failures in diagnosing the people with learning disabilities have been repeatedly highlighted and remain unpopular among people in the developing countries like Sir Lanka. Dyscalculia (difficulties in math), dysgraphia (difficulties in writing) and dyslexia (difficulties in reading) can be considered as some of the major learning disability issues. The ‘mobile application based’ solution “Sankalpa Math Master” has been created to offer a game based all-in-one solution including screening, and intervention to identify one of the above-mentioned learning disabilities called ‘Dyscalculia’. Dyscalculia is generally distinguished by challenges in learning math, including organizing problems, multi-step calculations, transposing numbers, distinguishing directions, and understanding mathematical signs, leading to confusion with basic operations and facts. The provided solution has been accurate with the use of machine learning and deep learning techniques. In the process of screening with the use of Support Vector Machine (SVM) machine learning algorithm 99.77% of accuracy level has been obtained. The skill level prediction process has acquired an accuracy level of 97% with the use of Random Forest machine learning algorithm. Sri Lankan traditional education system will be vastly benefited by the high level of accuracy in screening, skill predicting, and other game and voice chat-based activities. The students with special needs will gain more effective and personalized learning experience which will improve their learning abilities and help to close the technology gap in education.

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Design and Simulation of an Affordable Vehicle Speed Detection System

Design and Simulation of an Affordable Vehicle Speed Detecti...

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Modeling & E-Information Research, Artificial Learning and Digital Applications (ICMERALDA), International Conference on

作者： Hassaan Th. H. Thabet Aqeel A. Al-Hilali Mohammed G. S. Al-safi Thabit H. Thabit Firas M. Mahmood Dept. of Computer Technology Engineering Al-Hadba University College Mosul Iraq Dept. of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad Iraq Dept. of Accounting Al-Esraa University College Baghdad Iraq Dept. of Computer Engineering and Information Ninevah University Mosul Iraq Dept. of Informatics and Software Engineering Cihan University-Erbil Kurdistan Region Iraq

The objective of this paper is to detect the over speed of vehicles on the roads. Although maximum speed limit signs exist on the roads, many accidents keep on because of high-speed driving. It is necessary to solve these problems through automation techniques. This paper describes a speed detection system for vehicles, the traffic police can easily check the speed of vehicles by using this system. This system consists of a PLC (Programmable Logic Controller) LOGO! V8.3, two Laser sensors, an HMI (Human Machine Interface), a speed camera, and a LOGO! CMR (Communication Module Radio). The detected speed is displayed on the HMI display, moreover, if a vehicle crosses the limited speed, this system displays the over-speed on HMI, and the speed camera is activated to capture an image of that vehicle. Also, an SMS (Short Messages Service) will be sent by the CMR to the traffic authority informing them about the violation to take the necessary legal actions. The system can also store the collected data for documentation purposes. The authors describe a simple, affordable technique for monitoring vehicles' speeds in this paper.

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