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24th International Arab Conference on Information Technology, ACIT 2023

作者： Hamdan, Khaled Kazar, Okba College of It Uaeu Dept. of Computer Science and Software Engineering United Arab Emirates College of It Uaeu Dept. of Information Systems & Security United Arab Emirates

ISBN: (纸本)9798350384307

The industrial sector has entered a phase of profound change which sees digital technologies being integrated into the heart of industrial processes. This fourth industrial era gives birth to a new generation of factory called 'Digital Factory', 'Industry 4.0'. This industrial change is the result of the integration of new digital technologies into manufacturing processes and the optimization of energy consumption. The commitment to the environment is also a key and success factor of Industry 4.0 and green in particular the reduction of carbon emissions and pollution. This is how smart green industry try to take in charge the problem of energy consumption and to reduce the Co2 pollution. The goal of this paper is to present the new model of smart green industry using distributed artificial intelligence based on multi-agent system (MAS). The motivation to use the intelligent approach based on MAS is based on the similarity between the fundamental concepts of the cyber-physical system and those contained in the architecture and operation of MAS such as communication, coordination, decision and cooperation. © 2023 IEEE.

关键词： environmental impact Industry 4.0 smart and green Industry 4.0

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Application to Enhance the Programming Knowledge of Children 6

Application to Enhance the Programming Knowledge of Children

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Dissanayake, Anuga Lakpriya, Vishwa Sanjeevan, Mahendran Fahmi, Ahamed Siriwardana, Samanthi E.R. Samaraweera, Mihiri Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798331517878

This proposal outlines the development of a comprehensive educational platform aimed at bridging the Information Technology knowledge gap among Tamil-speaking students in Sri Lanka. The platform is designed to enhance programming skills through personalized quizzes, hands-on projects, and adaptive learning models. The application focuses on two primary components: the first component emphasizes project-based learning and assessments, leveraging advanced Artificial Intelligence models for generating educational content. This model is combined with Retrieval-Augmented Generation, which retrieves relevant external information to refine and tailor the educational content to each student's needs. This component keeps students engaged and challenged at appropriate levels, promoting deeper understanding and practical Information Technology skills. The second component involves a Level-based IT Fundamental Knowledge Evaluation system, which incorporates machine learning algorithms to predict student performance, recommend customized learning paths, and assess overall technical knowledge. The platform also integrates the OpenAI Application Programming Interface, which provides real-time Tamil translation, making the content more accessible to the target demographic. The platform empowers educators to better address individual student needs with real-time feedback and tailored experiences. Its innovative approach enhances Information Technology education, equipping Sri Lankan students with essential programming skills to succeed globally. © 2024 IEEE.

关键词： Adversarial machine learning

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Deep Learning-Based Stock Price Prediction: A Comprehensive Approach Using Bi-LSTM 4

Deep Learning-Based Stock Price Prediction: A Comprehensive ...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Chy, Md. Kalim Amzad Masum, Abdul Kadar Muhammad Alam, Mohammed Shamsul Azad, Md. Abul Kalam Hassan, Md. Maruf Bhuiyan, Touhid International Islamic University Dept. of Computer Science and Engineering Chittagong Bangladesh Daffodil International University Dept. of Software Engineering Dhaka Bangladesh Islamic University of Technology Dept. of Business and Technology Management Gazipur Bangladesh School of It Washington University of Science and Technology VA United States

ISBN: (纸本)9798331529635

This study delves into the prediction of stock prices in the Bangladesh stock market using advanced deep learning frameworks, with a particular focus on the Bidirectional Long Short-Term Memory (Bi-LSTM) model. Traditional statistical models often struggle to effectively capture the complex, non-linear characteristics of financial data, leading to inconsistent results. The Bi-LSTM model, however, addresses this challenge by processing data in both forward and backward sequences, making it particularly suitable for analyzing the intricate dynamics of the Bangladesh stock market. A rigorous feature engineering process is undertaken, incorporating 64 distinct features that include market trends, stock-specific indicators, technical and fundamental variables, as well as crucial macroeconomic factors like gold prices and foreign exchange reserves. The dataset spans historical stock market data from 2012 to 2022, with comprehensive preprocessing steps taken to ensure data quality and consistency. Hyperparameter tuning is executed through a grid search to optimize model performance. The evaluation of the Bi-LSTM model is carried out using metrics such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and the R2 score, which demonstrate its enhanced predictive power compared to traditional models. This research contributes to the growing field of deep learning in financial forecasting, offering new perspectives for stock price prediction in emerging markets, while also addressing ethical issues to minimize risks like market manipulation. © 2024 IEEE.

关键词： Mean square error

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Accounting for systemic risk in analysis of crisis scenarios 21

Accounting for systemic risk in analysis of crisis scenarios

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21st International ISCRAM Conference, ISCRAM 2024

作者： Gonzalez, Jose J. Abbas, Reem Paulsen, Sigurd University of Agder Kristiansand Norway Department of Computer Science and Software Engineering Auckland University of Technology Auckland New Zealand Dept. of Organization Crisis Management and Information Security Kristiansand Norway

Systemic risk is ubiquitous in our increasingly globalised world owing to the interconnections within and across sectors. However, a review of National Risk Assessments performed in OECD member countries reveals that systemic risk is rarely mentioned, and it is never accounted for in those assessments. A powerful method accounts for systemic risk and identifies mitigating strategies using participatory modelling with stakeholders: strategy mapping with Strategyfinder. The risk model is represented as a directed graph consisting of risk nodes and directed edges (arrows) expressing causal influences. The method originated in the management of large, complex projects, and it has been applied to systemic risks in critical infrastructures and pandemics. We demonstrate the Strategyfinder method using a scenario describing a cyberattack on electronic communication infrastructure leading to serious consequences for health and lives. The scenario is borrowed from the report Analysis of Risk Scenarios by the Norwegian Directorate for Civil Protection. © 2024 Information Systems for Crisis Response and Management, ISCRAM. All rights reserved.

关键词： Cascading effects Disaster risk reduction National Risk Assessments Risk scenarios Vicious cycles

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An In-depth Study on Handwritten Signature Authentication Using Unique Writer Traits 1

An In-Depth Study on Handwritten Signature Authentication Us...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Vamsikrishna, M. Murari, Haritha Manikandan, M. Mishra, Nilamadhab Raghavendra Gupta, T. Patel, Rohit Aditya University Dept. of Computer Applications Kakinada India Spark Infotech Inc Sr Software Engineer PhoenixAZ United States VeL Tech. R Dr.S Dept. of Computer Science and Engineering Tamilnadu India VIT Bhopal University Dept. of Computer Science and Engineering Sehore India HITM Telenagana Hyderabad India Calorx Teachers' University Dept of CSE Gujrat India

ISBN: (纸本)9798350356816

Handwriting verification, a subset of signature verification, involves using handwritten text to verify or authorize someone's identification. Although simple handwriting can serve as a signature, it is often modified to suit the signer's preferences. The uniqueness of each person's handwritten signature makes it essential for accurate distinction from others. This study presents a handwritten signature verification system using a writer-dependent approach. While signature verification is widely used for identity verification and security, it is susceptible to forgeries. The writer-dependent approach considers the unique characteristics of an individual's signature, training the system to recognize them and making forgery replication more challenging. An extensive review of the literature on signature verification systems, focusing on the writer-dependent approach, was conducted. A system was then developed and implemented based on this approach, utilizing a dataset of signatures from 12 writers. Results indicate that the writer-dependent approach outperforms other methods in terms of accuracy and robustness. The system demonstrated high precision and recall rates, underscoring its effectiveness in detecting both genuine and forged signatures. This study highlights the writer-dependent approach's efficacy in enhancing the accuracy and security of signature verification systems, with implications for developing more reliable and efficient identity verification systems in today's digital age. © 2024 IEEE.

关键词： Authentication

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Synthetic Data: Revisiting the Privacy-Utility Trade-off

arXiv

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arXiv 2024年

作者： Sarmin, Fatima Jahan Sarkar, Atiquer Rahman Wang, Yang Mohammed, Noman Dept. of Computer Science University of Manitoba Canada Dept. of Computer Science and Software Engineering Concordia University Canada

Synthetic data has been considered a better privacy-preserving alternative to traditionally sanitized data across various applications. However, a recent article challenges this notion, stating that synthetic data does not provide a better trade-off between privacy and utility than traditional anonymization techniques, and that it leads to unpredictable utility loss and highly unpredictable privacy gain. The article also claims to have identified a breach in the differential privacy guarantees provided by PATE-GAN and PrivBayes. When a study claims to refute or invalidate prior findings, it is crucial to verify and validate the study. In our work, we analyzed the implementation of the privacy game described in the article and found that it operated in a highly specialized and constrained environment, which limits the applicability of its findings to general cases. Our exploration also revealed that the game did not satisfy a crucial precondition concerning data distributions, which contributed to the perceived violation of the differential privacy guarantees offered by PATE-GAN and PrivBayes. We also conducted a privacy-utility tradeoff analysis in a more general and unconstrained environment. Our experimentation demonstrated that synthetic data indeed achieves a more favorable privacy-utility trade-off compared to the provided implementation of k-anonymization, thereby reaffirming earlier conclusions. © 2024, CC BY.

关键词： Anonymity

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Custom Hidden Markov Models for Effective Part-of-Speech Tagging 18

Custom Hidden Markov Models for Effective Part-of-Speech Tag...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Khalid, Hassan Bin Siddique, Abu Bakar Ali, Raja Hashim Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

Hidden Markov Models have proved to be a very significant tool for various time-series related problems, especially where context is important. One such problem is Part-of-speech tagging. The work uses a customized HMM to propose an effective and advanced solution to POS tagging. With a precision rate of 0.9657, recall of 0.9656, and F1-score of 0.9655, this proposed HMM-based model achieves an exceptional level of accuracy, exhibiting its accurate identification of the POS of words in a sentence. The statistical model employed by the HMM-based method predicts the most likely POS tags while taking into account the probabilities of transition between various POS tags. The model's dependability and resilience were demonstrated when it was tested on a different dataset after being trained on a extensive collection of text data. The study's findings demonstrate that the HMM-based strategy outperforms current POS tagging techniques, making it a significant contribution to the field of natural language processing. In addition, this research has significant implications for a number of NLP applications, including sentiment analysis, machine translation, and text categorization, paving the way for additional innovation and exploration in this domain. © 2023 IEEE.

关键词： Hidden Markov models

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Revolutionizing Campus Exploration with GikiLenS: A Deep Learning-Powered Object Detection App 18

Revolutionizing Campus Exploration with GikiLenS: A Deep Lea...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Mueed, Iqra Arshad, Usama Ali, Raja Hashim Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

For newcomers and tourists, navigating university campuses can be difficult, resulting in aggravation and lost time. We respond by introducing 'GikiLenS', an object identification application driven by deep learning that revolutionizes campus exploration by accurately identifying buildings and landmarks while enhancing user experience. GikiLenS is a comprehensive and user-friendly smartphone application that precisely caters to the demands of newcomers and visitors, unlike previous studies in campus navigation. Our study intends to close this gap and develop a reliable method for identifying campus buildings, optimizing navigation, and enhancing user experience. Through rigorous testing, GikiLenS has proven to have remarkable real-time building detection accuracy, highlighting its potential as an important tool for campus exploration. The app's outcomes and conclusions demonstrate how well it works to give users specific building information, promoting a more knowledgeable and enjoyable campus experience. The importance of our findings lies in the development of a unique, user-centered app that offers a cutting-edge method of campus mobility. Our contribution consists of a game-changing method for building detection that improves campus exploration and user pleasure. © 2023 IEEE.

关键词： Object detection

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Diving into Brain Complexity: Exploring Functional and Effective Connectivity Networks 18

Diving into Brain Complexity: Exploring Functional and Effec...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Siddique, Abu Bakar Khalid, Hassan Bin Ali, Raja Hashim University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan

ISBN: (纸本)9798350328172

The human brain's intricate functions are under-pinned by a vast network of synapses that enable chemical impulses between neurons. Neuroscientists employ two key approaches, functional and effective connectivity, to understand the brain's complexity, focusing on its operations, cognition, and behavior. While both methods utilize graph theory for network analysis, functional connectivity, which investigates associated brain activity, has seen more substantial research efforts than effective connectivity, which examines information processing's inter-regional impacts. In this research paper, we aim to present an extensive examination of the emerging discipline that combines graph theory with the analysis of brain characteristics, shedding light on how these characteristics arise from the interactions among different groups of neurons. Our primary focus lies in exploring the diverse cognitive and neurological applications that leverage functional Magnetic Resonance Imaging (fMRI) as a tool. Additionally, we offer a comprehensive overview of the methods employed to construct brain networks based on functional and efficient connections. Throughout the discussion, we emphasize the advantages and limitations associated with these approaches. © 2023 IEEE.

关键词： Graph theory

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Automating the Diagnosis of Cucumber Plant Diseases Using Machine Learning 25

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

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25th International Arab Conference on Information Technology, ACIT 2024

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

ISBN: (纸本)9798331540012

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. © 2024 IEEE.

关键词： Convolutional neural networks

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