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Power, Electrical, and Electronics and Industrial Applications (PEEIACON), IEEE International Conference on

作者： Rezaul Haque Md Miraz Miah Shayma Sultana Hasib Fardin Abdullah Al Noman Abdullah Al-Sakib Md Kamrul Hasan Al Rafy Rahman Md Shihabur Shafiur Rahman Dept. of CSE East West University Dhaka Bangladesh Dept. of CSE BRAC University Dhaka Bangladesh Dept. of Management Information System International American University CA USA Dept. of Engineering Management Westcliff University CA USA Dept. of Information Technology Westcliff University CA USA Dept. of Cyber security Touro University CA USA Dept. of Computer Engineering Kyungdong University Gangwon-do Republic of Korea Dept. of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798331517984

ISBN: (纸本)9798331517991

Detecting diseases in jute leaves is difficult due to the variability in how the diseases appear. Manually identifying these diseases is challenging because it requires expert knowledge and visual inspections take a lot of time. Machine Learning (ML) offers a promising solution to these challenges by automating the detection process. However, research in this area is limited due to the lack of specific datasets. This study aims to address this by creating a comprehensive dataset of 10,800 high-quality images of jute leaf diseases. The main goal is to develop a robust system for classifying jute leaves into three categories: Yellow Mosaic, Powdery Mildew, and Healthy. Our methodology involved extensive image preprocessing, including resizing and various augmentation techniques, to enhance the dataset's diversity and ensure model robustness. We trained various ML and Deep Learning (DL) models and conducted a comparative analysis of their performance. Additionally, we compared our approach with the state-of-the-art methods. The results showed that DL models, particularly Inception V3, achieved an outstanding accuracy of 99.98\%, compared to 89.75\% for Random Forest (RF). This highlights the potential of DL techniques in improving the accuracy of jute leaf disease detection. Our findings contribute to better disease management strategies and increased productivity in jute cultivation.

关键词： Deep learning Radio frequency Analytical models Visualization Accuracy Transfer learning Robustness Real-time systems Random forests Diseases

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An Image Analysis-Based Automated Method using Deep Learning for Grain Counting

An Image Analysis-Based Automated Method using Deep Learning...

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International Conference on Industrial and Information Systems, ICIIS

作者： Ramesh Ajikaran Ashen Iranga Hewarathna Vigneshwaran Palanisamy Charles Joseph Selvarajah Thuseethan Dept. of Computing and Infromation Systems Sabaragamuwa University of Sri Lanka Dept. of Software Engineering Sabaragamuwa University of Sri Lanka Dept. of Computer Science FAU Germany School of Information Technology Deakin University Australia

In the process of quality analysis of grains, number counting is one of the key steps for agricultural production. Traditional manual grain counting methods are time-consuming and subject to human error, and automated methods have the potential to improve accuracy and save time. In this study, we aimed to develop an image analysis-based method to automatically quantify the number of grains in a quicker manner. The 576 grain images were collected manually, and labelImg tagging tool used to annotate to generate a text file with their respective positions by drawing bounding boxes manually. The datasets consist were separated into three groups: training, validation, and test. For object detection, the YOLOv5, YOLOv4, and YOLOv3 algorithms represent cutting-edge deep learning frameworks. They replace the tedious and error-prone manual counting process by precisely identifying and counting grains in images obtained from agricultural fields. This technique helps to increase grain counting's precision and effectiveness. We believe this method will be extremely beneficial in guiding the development of high throughput systems for counting the number of grains in other crops as it performs well with a wide range of backgrounds, picture sizes, grain sizes, as well as various quantities of grain crowding. When compared to the other two approaches, YOLOv4 performed well in terms of accuracy, speed, and robustness (97.65%), demonstrating that the suggested strategy is competitive with other cutting-edge deep networkst.

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Deep Neural Networks in Social Media Forensics: Unveiling Suspicious Patterns and Advancing Investigations on Twitter

Deep Neural Networks in Social Media Forensics: Unveiling Su...

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Intelligent Cybersecurity Conference (ICSC)

作者： Yousef Sharrab Dimah Al-Fraihat Mohammad Alsmirat Dept. of Data Science and Artificial Intelligence Isra University Amman Jordan Dept.of Software Engineering Isra University Amman Jordan Dept. of Computer Science University of Sharjah Sharjah UAE

Text data forensics, a rapidly developing field that focuses on analyzing textual content to identify criminal or suspicious activities, is becoming increasingly important due to the popularity and the huge number of text posts on social media platforms. This study aims to improve the detection of suspicious text on social media using deep neural networks. Suspicious text is defined as any text that is likely to be associated with criminal activity or is unusual or out of the ordinary. This study could make a significant contribution to the field of text data forensics by helping to improve the detection of such text. We leveraged the “CIC Truth Seeker Dataset 2023” [1], which is widely recognized as a comprehensive and representative dataset for text data forensics research. The dataset contains over 180,000 tweets related to 700 real and 700 fake pieces of news, labeled by experts. In this study, we enhance text data forensics in social media by leveraging the powerful analytical capabilities of deep neural networks. More specifically, we investigate the effectiveness of Long Short-Term Memory (LSTM) in the detection of suspicious text. The results are very promising as we achieved an accuracy of 96% during preliminary evaluations. We plan to explore future work on the model's potential applications, including criminal activity identification, misinformation detection, and online harassment prevention.

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ILP-FORMER: solving integer linear programming with sequence to multi-label learning 24

ILP-FORMER: solving integer linear programming with sequence...

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Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence

作者： Shufeng Kong Caihua Liu Carla Gomes School of Software Engineering Sun Yat-sen University Zhuhai Guangdong China and Computer Science Dept. Cornell University Ithaca NY School of Artificial Intelligence Guilin University of Electronic Technology Guilin Guangxi China Computer Science Dept. Cornell University Ithaca NY

Integer Linear Programming (ILP) is an essential class of combinatorial optimization problems (COPs). Its inherent NP-hardness has fostered considerable efforts towards the development of heuristic strategies. An emerging approach involves leveraging data-driven methods to automatically learn these heuristics. For example, using deep (reinforcement) learning to recurrently reoptimize an initial solution with Large Neighborhood Search (LNS) has demonstrated exceptional performance across numerous applications. A pivotal challenge within LNS lies in identifying an optimal subset of variables for reoptimization at each stage. Existing methods typically learn a policy to select a subset, either by maintaining a fixed cardinality or by decomposing the subset into independent binary decisions for each variable. However, such strategies overlook the modeling of LNS's sequential processes and fail to explore the correlations inherent in variable selection. To overcome these shortcomings, we introduce ILP-FORMER, an innovative model that reimagines policy learning as a sequence-to-multi-label classification (MLC) problem. Our approach uniquely integrates a causal transformer encoder to capture the sequential nature of LNS. Additionally, we employ an MLC decoder with contrastive learning to exploit the correlations in variable selection. Our extensive experiments confirm that ILP-FORMER delivers state-of-the-art anytime performance on several ILP benchmarks. Furthermore, ILP-FORMER exhibits impressive generalization capabilities when dealing with larger problem instances.

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Performance Analysis of Breast Cancer: A Machine Learning Approach 3

Performance Analysis of Breast Cancer: A Machine Learning Ap...

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3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021

作者： Khatun, Tania Utsho, Md. Mahfuzur Rahman Islam, Md. Ashiqul Zohura, Mst. Fatematuz Hossen, Md. Sagar Rimi, Robaiya Akter Anni, Sabiha Jannat Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh Daffodil International University Dept. of Software Engineering Dhaka Bangladesh Shahabuddin Medical College Gulshan-2 Dhaka Bangladesh

ISBN: (纸本)9780738146270

Nowadays, breast cancer is the most emerging diseaseamong women both in developed as well as developing countries. Due to increased life prospects, increased urbanization, and therelinquishment of western societies, the rareness of breast cancer is supersizing in the developing world. Even it became a second popular cause of cancer that has already been announced. It's very hard to identify the early symptom of this type of cancer for reducing numerous death. Different methods of machine learning and data mining techniques are using for medical diagnosis. In this study, four machine-learning algorithms are applying to analyze breast cancer in the inflammation stage and dig up the most cabbalistic and non-cabbalistic risk factors. To analyze breast cancer data from the Coimbra dataset from the UCI machine learning repository to create accurate prediction models for breast cancer. For getting better performance and to get higher accuracy Naïve Bayes (NB), Random Forest (RF), Multilayer Perceptron (MLP), Simple Logistic Regression (SLR) are using to find out some higher accuracy sequentially 70%, 68%, 85%, and 75%. Among all the above algorithms a better accuracy was achieved using Multi-layer Perceptron. Linear Regression (LiR) models are applying to dig up the most cabbalistic and non-cabbalistic risk factors of breast cancer. These results will help the doctor to detect breast cancer easily in the early stage and take the necessary steps. © 2021 IEEE.

关键词： Developing countries

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SemParser: A Semantic Parser for Log Analytics

SemParser: A Semantic Parser for Log Analytics

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International Conference on software engineering (ICSE)

作者： Yintong Huo Yuxin Su Cheryl Lee Michael R. Lyu Computer Science & Engineering Dept. The Chinese University of Hong Kong Hong Kong China School of Software Engineering Sun Yat-sen University Zhuhai China

Logs, being run-time information automatically generated by software, record system events and activities with their timestamps. Before obtaining more insights into the run-time status of the software, a fundamental step of log analysis, called log parsing, is employed to extract structured templates and parameters from the semi-structured raw log messages. However, current log parsers are all syntax-based and regard each message as a character string, ignoring the semantic information included in parameters and templates. Thus, we propose the first semantic-based parser SemParser to unlock the critical bottleneck of mining semantics from log messages. It contains two steps, an end-to-end semantics miner and a joint parser. Specifically, the first step aims to identify explicit semantics inside a single log, and the second step is responsible for jointly inferring implicit semantics and computing structural outputs according to the contextual knowledge base of the logs. To analyze the effectiveness of our semantic parser, we first demonstrate that it can derive rich semantics from log messages collected from six widely-applied systems with an average F1 score of 0.985. Then, we conduct two representative downstream tasks, showing that current downstream models improve their performance with appropriately extracted semantics by 1.2%-11.7% and 8.65% on two anomaly detection datasets and a failure identification dataset, respectively. We believe these findings provide insights into semantically understanding log messages for the log analysis community.

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An Efficient GPU-based Implementation for Noise Robust Sound Source Localization

arXiv

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arXiv 2025年

作者： Lin, Zirui Takigahira, Masayuki Terakado, Naoya Gulzar, Haris Busto, Monikka Roslianna Eda, Takeharu Itoyama, Katsutoshi Nakadai, Kazuhiro Amano, Hideharu Dept. of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Tokyo Japan Honda Research Institute Japan Co. Ltd. Saitama Japan NTT Software Innovation Center Tokyo Japan Dept. of Information and Computer Science Keio University Kanagawa Japan

Robot audition, encompassing Sound Source Localization (SSL), Sound Source Separation (SSS), and Automatic Speech Recognition (ASR), enables robots and smart devices to acquire auditory capabilities similar to human hearing. Despite their wide applicability, processing multi-channel audio signals from microphone arrays in SSL involves computationally intensive matrix operations, which can hinder efficient deployment on Central Processing Units (CPUs), particularly in embedded systems with limited CPU resources. This paper introduces a GPU-based implementation of SSL for robot audition, utilizing the Generalized Singular Value Decomposition-based Multiple Signal Classification (GSVD-MUSIC), a noise-robust algorithm, within the HARK platform, an open-source software suite. For a 60-channel microphone array, the proposed implementation achieves significant performance improvements. On the Jetson AGX Orin, an embedded device powered by an NVIDIA GPU and ARM®Cortex™-A78AE v8.2 64-bit CPUs, we observe speedups of 4645.1× for GSVD calculations and 8.8× for the SSL module, while speedups of 2223.4× for GSVD calculation and 8.95× for the entire SSL module on a server configured with an NVIDIA A100 GPU and AMD EPYC™ 7352 CPUs, making real-time processing feasible for large-scale microphone arrays and providing ample capacity for real-time processing of potential subsequent machine learning or deep learning tasks. © 2025, CC BY-NC-ND.

关键词： Acoustic generators

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SS-SFR: Synthetic Scenes Spatial Frequency Response on Virtual KITTI and Degraded Automotive Simulations for Object Detection 26

SS-SFR: Synthetic Scenes Spatial Frequency Response on Virtu...

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26th Irish Machine Vision and Image Processing Conference, IMVIP 2024

作者： Jakab, Daniel Braun, Alexander Agnew, Cathaoir Mohandas, Reenu Deegan, Brian Michael Molloy, Dara Ward, Enda Scanlan, Anthony Eising, Ciarán Dept. of Electronic and Computer Engineering University of Limerick Castletroy LimerickV94 T9PX Ireland Faculty of Electrical Engineering & Information Technology University of Applied Sciences Düsseldorf40476 Germany Dept. of Electrical and Electronic Engineering University of Galway GalwayH91 TK33 Ireland Valeo Vision Systems GalwayDY1 22DJ Ireland Lero the Science Foundation Ireland Research Centre for Software University of Limerick LimerickV94 T9PX Ireland

ISBN: (纸本)9781837242672

Automotive simulation can potentially compensate for a lack of training data in computer vision applications. However, there has been little to no image quality evaluation of automotive simulation and the impact of optical degradations on simulation is little explored. In this work, we investigate Virtual KITTI and the impact of applying variations of Gaussian blur on image sharpness. Furthermore, we consider object detection, a common computer vision application on three different state-of-the-art models, thus allowing us to characterize the relationship between object detection and sharpness. It was found that while image sharpness (MTF50) degrades from an average of 0.245cy/px to approximately 0.119cy/px;object detection performance stays largely robust within 0.58%(Faster RCNN), 1.45%(YOLOF) and 1.93%(DETR) across all respective held-out test sets. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Automobile simulators

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Towards the Utilization of Parallel Programming to Speed Up RAM Forensics

Towards the Utilization of Parallel Programming to Speed Up ...

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International Conference on software Defined Systems (SDS)

作者： Ziad A. Al-Sharif Rahaf Alhamouri Yaser Jararweh Dept. of Engineering Computing and Mathematical Sciences Lewis University Romeoville USA Dept. of Software Engineering Jordan University of Sci. and Tech. Irbid Jordan Dept. of Computer Science Jordan University of Sci. and Tech. Irbid Jordan

Memory forensics uses volatile digital artifacts as evidence about criminal activities. Analyzing captured memory dumps for volatile data requires time and effort. This paper studies the utilization of parallel programming to speed up RAM forensics. It presents a performance-based evaluation of parallel programming in the domain of memory forensics and compares sequential and parallel approaches to speed up the memory analysis process. First, it evaluates the sequential approach and uses it as a base case for further parallel approaches. Second, it evaluates two of the parallel approaches that can be performed on a typical user machine. Our experiments evaluate the use of two parallel programming paradigms: the in-process parallelization approach using OpenMP, and the inter-process parallelization approach using MPI. Our results compare the performance of the sequential approach, OpenMP thread-based approach, and MPI process-based approach. Experimentations compare the performance of three scenarios using six files of different sizes and various numbers of threads and/or processes. The results show that the use of MPI is slightly better than OpenMP approaches on the use of 2 and 4 processes/threads. However, when the number of processors/threads is increased to 8 and 16, OpenMP slightly outperforms the MPI approach. Additionally, the parallelization approach using OpenMP and MPI provides $3X$ to $5X$ speed up over the traditional sequential approach. Moreover, it is worth mentioning that this speed-up is achieved on traditional user machines without the use of HPC computers.

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XAI for Understanding the Success in Curbing COVID-19

XAI for Understanding the Success in Curbing COVID-19

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Intelligent Systems and computer Vision (ISCV)

作者： Salah Bouktif Nimmi Kunnath Ali Ouni Dept. of Computer Science and Software Engineering CIT United Arab Emirates University Al Ain UAE École de Technologie Supérieure University of Quebec Montreal Canada

ISBN: (数字)9798350350180

ISBN: (纸本)9798350350197

The Covid-19 pandemic has prompted governments worldwide to implement various non-pharmaceutical interventions (NPIs) in an effort to curb the pandemic to attenuate the harmness of the pandemic. However, there is a debate about how to assess the effectiveness of these interventions. A set of indicators has been used to monitor the outbreak such as case counts, infection rates, death rate, hospitalization. In this study, we contribute to the debate by firstly proposing a definition of the success in managing the pandemic in terms of mortality rate, socio-demographic and epidemiological factors. Secondly, we propose a modeling framework based on (1) analysing the time series pandemic data and (2) employing eXplainable Artificial Intelligence (XAI) to provide interpretability of the successfulness of countries in responding to the pandemic. By using a rich dataset collected by Oxford COVID-19 Government Response Tracker (OxCGRT), we built a success model based on Random Forest Regressor for time series analysis and we identified the major factors/practices/measures/protocols. Our findings indicate a significant relationship between policy compliance levels and death counts, highlighting the importance of considering mortality outcomes in evaluating the efficacy of interventions. Additionally, we discovered that socio-demographic and epidemiological factors such as elderly populations aged (65+), prevalent cardiovascular disease, high diabetes prevalence, increased smoking rates, and reduced life expectancy based on the person correlation coefficient are key determinants of success in managing the pandemic.

关键词： COVID-19 Analytical models Pandemics Explainable AI Time series analysis Government Sociology

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