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The effect of visual advanced driver assistance systems on a following human driver in a mixed-traffic condition

Procedia computer science 2023年 216卷 221-229页

作者： Dedy Ariansyah Bens Pardamean Giandomenico Caruso Bioinformatics and Data Science Research Centre Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia Department of Mechanical Engineering Politecnico di Milano Milan 20156 Italy

Rapid development in vehicular technology has caused more automated vehicle control to increase on the roads. Studies showed that driving in mixed traffic with an autonomous vehicle (AV) had a negative impact on the time headway (THW) of conventional vehicles (CVs) (i.e., driven by humans). To address this issue, there is a need to equip CV with visual advanced driver assistance systems (ADASs) that helps the driver maintain safe headway when driving near AVs. This study examines the perception of drivers using visual ADAS and their associated risk while driving behind the AV at constant and varying speeds. The preliminary results showed that while visual ADAS could help drivers keep the safe THW, it could affect drivers’ ability to react to emergencies. This implies that visual modality alone might not be sufficient and therefore requires some other feedback or intelligent transport systems to help drivers maintain safe driving in a mixed-traffic condition.

关键词： sustainable transport management platooning car-following visual advanced driver assistance system virtual realtiy

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Multivariate Time-Series Deep Learning for Joint Prediction of Temperature and Relative Humidity in a Closed Space

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Procedia computer science 2023年 227卷 1046-1053页

作者： Fergianto E. Gunawan Arief S. Budiman Bens Pardamean Endang Juana Sugiarto Romeli Tjeng W. Cenggoro Kartika Purwandari Alam A. Hidayat Anak. A.N.P. Redi Muhammad Asrol Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta 11440 Indonesia PT Impack Pratama Industri Jakarta 14350 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

An accurate predictive model of temperature and humidity plays a vital role in many industrial processes that utilize a closed space such as in agriculture and building management. With the exceptional performance of deep learning on time-series data, developing a predictive temperature and humidity model with deep learning is propitious. In this study, we demonstrated that deep learning models with multivariate time-series data produce remarkable performance for temperature and relative humidity prediction in a closed space. In detail, all deep learning models that we developed in this study achieve almost perfect performance with an R value over 0.99.

关键词： closed space deep learning humity prediction indoor temperature prediction

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An Object Driven Fuzzy Decision Model for Sugarcane Yield Prediction in Indonesia’s Sugar Industry

An Object Driven Fuzzy Decision Model for Sugarcane Yield Pr...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Ditdit Nugeraha Utama Muhammad Asrol Suharjito Christian Harito Napthaleni Muhammad Syafrudin Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia Industrial Engineering Department BINUS Graduate Program Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia Department of Artificial Intelligence and Data Science Sejong University Seoul South Korea

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

A sugarcane yield of one plantation area depends on several independent variables. Practically it is challenging to predict accurately by using conventional methods. This study aims to develop a decision model based on a combination of fuzzy logic and object-oriented methods to predict sugarcane yield. The research is conducted in four main stages, employing object-oriented methods for model design and fuzzy logic for model construction. Object and activity diagrams are used for the object-oriented model design. The fuzzy membership functions employed are a combination of trapezoidal and triangular shapes. The resulting decision model can simulate 2,225 data from plantation areas in Indonesia. Based on the 10 examples of plantation area data in Indonesia, plantation number one obtained the largest sugarcane yield, which was 4.79%, with a similarity value of 0.90 (when compared to manual calculations as its ground truth). This similarity value is a higher value when compared to the average similarity value, which is 0.89.

关键词： Fuzzy logic Seminars Shape Object oriented modeling Plantations Manuals Predictive models Sugar industry Intelligent systems Information technology

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Investigating the Impact of Data Augmentation for Fine-Grained Grocery Product Classification Based on Vision Transformer

Investigating the Impact of Data Augmentation for Fine-Grain...

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Artificial Intelligence Robotics, Signal and Image Processing (AIRoSIP), International Conference on

作者： Rissa Rahmania Yaya Heryadi Lukas Lukas Wayan Suparta Ilvico Sonata Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Group (CEG) Faculty of Engineering Universitas Katolik Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9798350327472

ISBN: (纸本)9798350327489

The existence of fine-grained image classification supporting smart retail provides effectiveness in recognizing products with high similarity. However, the generic classification method performs poorly in identifying products from a subordinate category. This paper aims to identify augmentation techniques to leverage the Vision Transformer (ViT) model to classify the fine-grained grocery product, which involves embedding patches and transformer encoders to extract the main features. First, we develop a fine-grained image dataset with ColorJitter, CutOut, and combining both augmentations. Secondly, we perform experiments and analysis of ViT size, embedded patch size and image size in the patch embedding process. Lastly, the ViT model are evaluated according to the image sizes 224, 384, and 512 in accuracy, loss, and confusion matrix. The highest accuracy was obtained at 0.9922. The ColorJitter and CutOut improved the confusion matrix in ViT-B/16 and ViT-L/16 with an image size of 384 and 512. The results show that both augmentations in the ViT model are able to distinguish fine-grained grocery products.

关键词： Surveys computer vision Accuracy Image recognition Transformers Feature extraction Data augmentation Artificial intelligence Image classification

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Email Spam Detection Using Logistic Regression and Explainable AI

Email Spam Detection Using Logistic Regression and Explainab...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Sumit Kanti Sarker Ratul Bhattacharjee Md Abu Sufian Md Solaiman Ahamed Md Abu Talha Faiza Tasnim K.M. Nazmul Islam Sharia Tasnim Adrita Department of Computer Science & Engineering (CSE) Faculty of Science & Technology (FST) American International University Bangladesh Dhaka Bangladesh Bachelor of Science in Information Technology (BSIT) Cybersecurity Program Concentrations Washington University of Science and Technology (WUST) VA USA Department of Electrical and Electronics Engineering (EEE) Faculty of Engineering (FE) American International University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering (CSE) Faculty of Science & Engineering Northern University Bangladesh-(NUB) Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Email spam detection is crucial for ensuring a positive user experience and maintaining communication security. This study presents a novel spam detection approach leveraging Logistic Regression, optimized through hyperparameter tuning and enhanced with Explainable Artificial Intelligence. The proposed method is evaluated on a large-scale dataset of emails collected from a real-world spam detection system. Term Frequency-Inverse Document Frequency is utilized for feature extraction, converting email text into numerical representations. XAI introduces interpretability by highlighting critical features such as "pill," "PHP," and "Businessweek," which indicate spam classification patterns. Metrics such as precision, recall, and the confusion matrix are employed to assess the model’s performance, ensuring a balanced evaluation. Hyperparameter optimization using Grid Search CV achieves an optimal regularization parameter (C = 100) and maximum iterations (maxiter=100), resulting in an impressive F1 score of 98.76%, accuracy of 98.82%, precision of 98.62%, and recall of 98.90%.

关键词： Logistic regression Explainable AI Filtering Unsolicited e-mail Computational modeling Predictive models Transformers Frequency conversion Feature extraction Tuning

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Multi-scale pulmonary nodule classification with deep feature fusion via residual network

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Journal of Ambient Intelligence and Humanized Computing 2023年第11期14卷 14829-14840页

作者： Zhang, Guokai Zhu, Dandan Liu, Xiao Chen, Mingle Itti, Laurent Luo, Ye Lu, Jianwei School of Software Engineering Tongji University Shanghai China High School Affiliated to Fudan University Shanghai China Department of Computer Science and Neuroscience Program University of Southern California Los Angeles United States Institute of Translational Medicine Tongji University Shanghai China

The early stage detection of benign and malignant pulmonary nodules plays an important role in clinical diagnosis. The malignancy risk assessment is usually used to guide the doctor in identifying the cancer stage and making follow-up prognosis plan. However, due to the variance of nodules on size, shape, and location, it has been a big challenge to classify the nodules in computer aided diagnosis system. In this paper, we design a novel model based on convolution neural network to achieve automatic pulmonary nodule malignancy classification. By using our model, the multi-scale features are extracted through the multi-convolution process, and the structure of residual blocks allows the network to capture more high-level and semantic information. Moreover, a strategy is proposed to fuse the features from the last avg-pooling layer and the ones from the last residual block to further enhance the performance of our model. Experimental results on the public Lung Image Database Consortium dataset demonstrate that our model can achieve a lung nodule classification accuracy of 87.5 % which outperforms state-of-the-art methods. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Classification (of information)

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AI-Powered Precision: Revolutionizing Skin Cancer Detection with Advanced Machine Learning

AI-Powered Precision: Revolutionizing Skin Cancer Detection ...

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Novel Intelligent and Leading Emerging sciences Conference (NILES)

作者： Mariam M. Abd-elaziz Basma M. Elshafie Hagar Abdullah Karen Y. Gerges Ingy A. Ghanem Khaled M. Fouad Amir El-Ghamry Ibrahim F. Moawad Sara El-Metwally Faculty of computer science and engineering New Mansoura university Dakahlia Egypt Faculty of Engineering Computer and Communication program Faculty of Computer Science and Engineering Mansoura University New Mansoura University Information Systems Faculty of Computers and Artificial Intelligence Benha University Egypt Faculty of Computer and Information Sciences Faculty of Computer Science and Engineering Mansoura University New Mansoura University Egypt School of Engineering and Computer Science University of Hertfordshire GAF Egypt Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Department of Artificial Intelligence College of Computer Science and Engineering Taibah University Medina Saudi Arabia

ISBN: (数字)9798350378511

ISBN: (纸本)9798350378528

Skin cancer's increasing incidence rates necessitate advanced diagnostic tools. This research uses MobileNet architecture to develop an enhanced system for skin cancer detection. MobileNet's efficient CNN architecture, combined with Keras for model lifecycle management and TensorFlow for real-time inference, forms the foundation of this study. Using 10,015 dermatoscopic pictures from seven different classifications of skin cancer in the HAM10000 dataset, the methodology includes several key stages: image preprocessing to correct illumination and adjust resolution, data augmentation to balance the dataset, and model training using Transfer Learning. The MobileNet model was trained over 50 epochs with a comprehensive architecture incorporating multiple specialized layers. By combining large-scale data analysis and adaptive learning, this methodology demonstrates The revolutionary possibilities of AI and ML in improving skin cancer diagnostics and public health outcomes. Keywords Skin Cancer Detection, Dermatoscopic Images, Machine Learning, Deep Learning

关键词： Training Deep learning Adaptation models Image resolution Transfer learning Lighting Real-time systems Image preprocessing Public healthcare Skin cancer

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Analysis of Parallel Graph Applications

Analysis of Parallel Graph Applications

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Funda Atik Serif Yesil Hamza Ouarnoughi Smail Niar Ozcan Ozturk Computer Science and Engineering Department Washington University in St. Louis Doimukh MI USA NVidia Urbana IL USA Department of Computer Science Université Polytechnique Hauts-de-France Valenciennes France LAMIH/CNRS Université Polytechnique Hauts-de-France Valenciennes France Computer Science and Engineering Program Electronics Engineering Program Sabanci University Istanbul Turkey

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Despite the increasing computing power of shared memory systems with high core counts, parallel graph processing frameworks cannot exploit it effectively. The reason behind this is the inherent challenges in parallel graph algorithms, which are efficient management of dynamically created tasks and irregular data access patterns. In this paper, we categorize several popular design choices into three design dimensions: (i) execution mode, (ii) data access pattern, and (iii) work activation. We provide their high-level parallel implementations and analyze various implementations of three representative iterative graph algorithms by considering these design dimensions. To gain a better understanding of design choices, we examine their impacts on performance, communication, scalability, and work efficiency. We also investigate the communication characteristics of the design choices on two state-of-the-art shared-memory platforms by performing micro-architectural analysis. Our microarchitectural analysis reveals that a topology-driven, pull-based model gives up to $20 x$ better performance.

关键词： Analytical models Microarchitecture Scalability Heuristic algorithms Benchmark testing Throughput Iterative algorithms System-on-chip

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An Innovative New Open computer Vision Framework Via Artificial Intelligence with Python

An Innovative New Open Computer Vision Framework Via Artific...

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International Conference on Data science and Network engineering, ICDSNE 2023

作者： Bonkra, Anupam Dhiman, Pummy Goyal, Shanky Islam, Sardar M. N. Rana, Arun Kumar Sharma, Naman Information Technology Department Chandigarh Engineering College-CGC Punjab India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India ISILC & Decision Sciences and Modelling Program Victoria University Footscra Australia Department of Computer Science Galgotias College of Engineering of Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819967544

computer vision has emerged as an important subject of study, with several practical applications in a wide range of domains. OpenCV, a widely used framework, has played an important role in allowing computer vision tasks. This study presents an AI-driven Python implementation of the OpenCV framework to expand its capabilities. Data collecting and pre-processing, feature extraction, model selection and training, and Python-based system implementation are all part of the proposed system. A thorough examination of system performance indicates its advantage over competing approaches. The system’s flexibility enables it to handle a variety of computer vision tasks, such as gesture recognition, face detection, and object recognition. This paper advances AI-powered computer vision systems by providing significant insights into the implementation of OpenCV with Python. The focus of future work will be on improving system accuracy and broadening its functional range. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Python

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Advances in Classifying the Stages of Diabetic Retinopathy Using Convolutional Neural Networks in Low Memory Edge Devices

Advances in Classifying the Stages of Diabetic Retinopathy U...

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2021 IEEE Madras Section International Conference, MASCON 2021

作者： Paul, Aditya Jyoti Cognitive Applications Research Lab India Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9781665404051

Diabetic Retinopathy (DR) is a severe complication that may lead to retinal vascular damage and is one of the leading causes of vision impairment and blindness. DR broadly is classified into two stages – non-proliferative (NPDR), where there are almost no symptoms, except a few microaneurysms, and proliferative (PDR) involving a huge number of microaneurysms and hemorrhages, soft and hard exudates, neo-vascularization, macular ischemia or a combination of these, making it easier to detect. More specifically, DR is usually classified into five levels, labeled 0-4, from 0 indicating no DR to 4 which is most severe. This paper firstly presents a discussion on the risk factors of the disease, then surveys the recent literature on the topic followed by examining certain techniques which were found to be highly effective in improving the prognosis accuracy. Finally, a convolutional neural network model is proposed to detect all the stages of DR on a low-memory edge microcontroller. The model has a size of just 5.9 MB, accuracy and F1 score both of 94% and an inference speed of about 20 frames per second. © 2021 IEEE.

关键词： Neural network models

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