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16th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2024

作者： Aljammal, Ashraf H. Qawasmeh, Ahmad Taamneh, Salah Wedyan, Fadi Obiedat, Mamoon Salameh, Hani Bani The Hashemite University Prince Al Hussein Bin Abdullah Ii Faculty of Information Technology Department of Computer Science and Applications Zarqa Jordan Lewis University Department of Engineering Computing and Mathematical Sciences RomeovilleIL United States The Hashemite University Prince Al Hussein Bin Abdullah Ii Faculty of Information Technology Department of Information Technology Zarqa Jordan The Hashemite University Prince Al Hussein Bin Abdullah Ii Faculty of Information Technology Department of Software Engineering Zarqa Jordan

ISBN: (纸本)9798350371154

Internet usage among information technology users has expanded substantially, as have cybercrimes with potentially disastrous repercussions. Therefore, internet users have to employ the available security measures and policies during internet usage. Nowadays, University students make up a sizable portion of internet users. This study assesses the cybersecurity awareness among The Hashemite University students In terms of computer Usage. A questionnaire survey is used to assess students' cybersecurity knowledge while using their PCs and connecting to the internet, concentrating on their behaviors and use of security measures. © 2024 IEEE.

关键词： Cybersecurity

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Cement Strength Prediction using Regression Techniques

Cement Strength Prediction using Regression Techniques

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Ritu Dewan Rohith Polishetty Bhagwati Sharan Mayank Kumar Goyal Amadou Mouctar Balde Ameen Mohamad Ahmad Greynoon Department of Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida India Sr. Software Engineer Migo India Limited Hyderabad India Department of Computer Science and Engineering School of Engineering and Sciences SRM University-AP Amaravati India Department of Computer Science & Engineering School of Engineering & Technology Sharda University Greater Noida India Department of Computer Science & Applications School of Engineering & Technology Sharda University Greater Noida India

One of the primary structural materials in the development business is concrete. Compressive strength is the most frequently utilized metric when assessing the strength and nature of cement. This paper clarifies how to make use of technical regression (RT) methodology to decide the legitimate dose of the concrete mixture. Water, Cement, Fly Ash, Blast Furnace, Superplasticizer, Coarse, and Fine Aggregate, were used as input parameters, on the other hand, compressive strength was used as an output parameter. The produced models were evaluated using statistical parameters: Linear Regression, Decision Tree Regression, and Random Forest Regression. In contrast with other applicable models, it was found that the Gaussian process regression model based on the function of the nucleus of radial bases delivered relatively better results. The recommended models should get a good deal on saving money on materials, labor, and time while also improving accuracy. The proposed concrete should be more durable and accordingly more cost-effective.

关键词： Computational modeling Roads Stacking Prediction algorithms Concrete Real-time systems Task analysis

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Reversible Steganography for VQ Indices Using the Modified State Codebook

Reversible Steganography for VQ Indices Using the Modified S...

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International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP)

作者： Chin-Chen Chang Chi-Shiang Chan Zhi-Hui Wang Ming-Chu Li Department of Information Engineering and Computer Science Feng Chia University FCU Taichung Taiwan Department of Information Science and Applications Asia University Wufeng Taiwan School of Software Dalian University of Technology Dalian China

In this paper, we propose an information hiding scheme to embed secret information into the compression code of an index table. First, the codewords neighboring the current processed index along with their closest codewords form a state codebook. Two indices of the state codebook are reserved as indicators. Once the current processed index satisfies some pre-defined conditions, the current processed index can be encoded using the state codebook, and the secret bit can be embedded in the compression code. On the other hand, the reserved indices are taken as indicators to denote that the current processed index is encoded by the VQ mechanism. On the basis of our experimental results, the bit rates of the proposed method are lower than those of related works. The improvements in bit rates increase when the quantity of the embedded bits increases.

关键词： Steganography Image coding Bit rate Data encapsulation Image storage Vector quantization Signal processing computer science Information science Application software

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A Hybrid LSTM-GAN Model for Predictive Cyber Threat Intelligence and Anomaly Detection

A Hybrid LSTM-GAN Model for Predictive Cyber Threat Intellig...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Khadija Danladi Sankara G. Saritha S. Anitha Elavarasi S. Saradha D. Arul Kumar Masters of Software Engineering Skyline University Nigeria Kano State Nigeria Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Computer Science and Engineering Sona College of Technology Salem India Department of Computer Applications Nandha Engineering College (Autonomous) Erode India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

Cybersecurity is increasingly challenged by complex and evolving threats that require predictive intelligence and advanced anomaly detection. This paper presents a novel Hybrid LSTM-GAN (Long Short-Term Memory - Generative Adversarial Network) model designed for predictive cyber threat intelligence and real-time anomaly detection. By leveraging LSTM's ability to capture temporal dependencies in sequential data and GAN's strength in generating synthetic samples, this hybrid model enhances the detection of subtle and emerging cyber threats. The model first uses LSTM layers to analyze patterns within time-series data, identifying potential anomalies. A GAN is then employed to generate realistic threat scenarios, enhancing the model's ability to recognize unusual activity and improve generalization. This two-step approach is crucial for capturing complex patterns and reducing false positives, which are common in conventional detection methods. Experimental results on benchmark cybersecurity datasets demonstrate that the proposed model achieves a 92.5% detection accuracy, a 15% reduction in false positives compared to standalone LSTM models, and a 25% improvement in threat prediction capabilities over traditional methods. These values indicate that the Hybrid LSTM-GAN model is a powerful tool for predictive threat intelligence, providing proactive and reliable anomaly detection across various cybersecurity applications.

关键词： Adaptation models Accuracy Predictive models Generative adversarial networks Data models Hybrid power systems Cyber threat intelligence computer security Long short term memory Anomaly detection

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InBiodiv-O: An ontology for indian biodiversity knowledge management

arXiv

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arXiv 2021年

作者： Patel, Archana Jain, Sarika Debnath, Narayan C. Lama, Vishal Department of Software Engineering School of Computing and Information Technology Eastern International University Viet Nam Department of Computer Applications National Institute of Technology Kurukshetra Haryana India Amdocs Development Centre LLP India

To present the biodiversity information, a semantic model is required that connects all kinds of data about living creatures and their habitats. The model must be able to encode human knowledge for machines to be understood. Ontology offers the richest machine-interpretable (rather than just machine-processable) and explicit semantics that are being extensively used in the biodiversity domain. Various ontologies are developed for the biodiversity domain however a review of the current landscape shows that these ontologies are not capable to define the Indian biodiversity information though India is one of the megadiverse countries. To semantically analyze the Indian biodiversity information, it is crucial to build an ontology that describes all the essential terms of this domain from the unstructured format of the data available on the web. Since, the curation of the ontologies heavily depends on the domain where these are implemented hence there is no ideal methodology is defined yet to be ready for universal use. The aim of this article is to develop an ontology that semantically encodes all the terms of Indian biodiversity information in all its dimensions based on the proposed methodology. The comprehensive evaluation of the proposed ontology depicts that ontology is well built in the specified domain. © 2021, CC BY.

关键词： Biodiversity

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Dts-Adapstnet: An Adaptive Spatiotemporal Neural Networks for Traffic Prediction with Mulit-Graph Fusion

SSRN

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SSRN 2023年

作者： Zhang, Jing Shi, Wenlong Ye, Xiucai Tsai, Peiwei School of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Department of Computer Science University of Tsukuba Tsukuba Japan Department of Computer Science and Software Engineering Swinburne University of Technology Hawthorn Australia

Traffic prediction is of vital importance in intelligent transportation systems. It can realize efficient route planning, avoid traffic congestion and reduce travel time, etc. However, it is difficult to make accurate road traffic prediction, due to the complex spatiotemporal dependencies of the traffic network. The construction and learning of spatial dependencies play a pivotal role in road traffic prediction, laying the foundation for accurate traffic prediction. Regrettably, most of the existing methods for capturing spatial dependencies only consider a single spatial relationship, ignoring other potential temporal and spatial correlations in the traffic network. Moreover, the end-to-end training methods are difficult to control the training direction during graph learning. Additionally, the result of traffic forecasting lacks of fusing multiple traffic data information. All of these have adverse effects on road traffic prediction. In other to capture the spatiotemporal dependencies of the traffic network accurately, a novel traffic prediction framework, Adaptive Spatio-Temporal Graph Neural Network based on Multi-graph Fusion (DTS-adapSTNet), is proposed. First of all, in order to better extract the hidden spatial dependencies, a method of fusing multiple factors is designed, which includs the distance relationship, transfer relationship and same-road segment relationship of traffic data, etc. Secondly, an adaptive learning method is proposed, which can control the learning direction of parameters better by the adaptive matrix generation module and traffic prediction module. Thirdly, an improved loss function is designed for training processes and a multi-matrix fusion module is designed to perform weighted fusion of the learned matrices, updating the spatial adjacency matrix continuously, which fuses as much traffic information as possible for more accurate traffic prediction. Finally, based on the experiments by two large real-world datasets, the DTS-adapSTNe

关键词： Travel time

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End-to-End AI-Based Point-of-Care Diagnosis System for Classifying Respiratory Illnesses and Early Detection of COVID-19

arXiv

引用

arXiv 2020年

作者： Belkacem, Abdelkader Nasreddine Ouhbi, Sofia Lakas, Abderrahmane Benkhelifa, Elhadj Chen, Chao Department of Computer and Network Engineering CIT UAEU UAE Department of Computer Science and Software Engineering CIT UAEU UAE Cloud Computing and Applications Reseach Lab Staffordshire University United Kingdom Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology China

Respiratory symptoms can be a caused by different underlying conditions, and are often caused by viral infections, such as Influenza-like illnesses or other emerging viruses like the Coronavirus. These respiratory viruses, often, have common symptoms, including coughing, high temperature, congested nose, and difficulty breathing. However, early diagnosis of the type of the virus, can be crucial, especially in cases such as the recent COVID-19 pandemic. One of the factors that contributed to the spread of the pandemic, was the late diagnosis or confusing it with regular flu-like symptoms. Science has proved that one of the possible differentiators of the underlying causes of these different respiratory diseases is coughing, which comes in different types and forms. Therefore, a reliable lab-free tool for early and more accurate diagnosis that can differentiate between different respiratory diseases is very much needed. This paper proposes an end-to-end portable system that can record data from patients with symptom, including coughs (voluntary or involuntary) and translate them into health data for diagnosis, and with the aid of machine learning, classify them into different respiratory illnesses, including COVID-19. With the ongoing efforts to stop the spread of the COVID-19 disease everywhere today, and against similar diseases in the future, our proposed low cost and user-friendly solution can play an important part in the early diagnosis. Copyright © 2020, The Authors. All rights reserved.

关键词： Diagnosis

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Cybersecurity Threats in the Era of AI: Detection of Phishing Domains Through Classification Rules 2

Cybersecurity Threats in the Era of AI: Detection of Phishin...

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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Aljaidi, Mohammad Alsarhan, Ayoub Al-Fraihat, Dimah Al-Arjan, Ahmed Igried, Bashar El-Salhi, Subhieh M. Khalid, Muhammad Al-Na'amneh, Qais Zarqa University Faculty of Information Technology Department of Computer Science Zarqa13110 Jordan The Hashemite University Department of Information Technology Jordan Isra University Faculty of Information Technology Department of Software Engineering Amman11622 Jordan The Hashemite University Faculty of Prince AL-Hussein Bin Abdullah Ii of Information Technology Department of Information Technology Jordan The Hashemite University Department of Computer Science and Applications Zarqa Jordan The Hashemite University P. O Box 330127 Zarqa13133 Jordan University of Hull Department of Computer Sciences and Technology Kingston upon Hull United Kingdom Applied Science Private University Dep. of Cyber Security Amman Jordan

ISBN: (纸本)9798350373363

Phishing is a cyber threat where attackers create deceptive websites or emails to exploit individuals for fraudulent purposes such as providing sensitive information like usernames, passwords, or financial details. Hence, detecting phishing emails is of paramount importance to protect sensitive information, preventing financial losses, protecting against identity theft, and maintaining trust. This study employs machine learning techniques to classify and categorize websites that are used for phishing attacks. To achieve this, we proposed a classification model for the detection of phishing domains based on classification rules. These rules were formulated based on analyzing and distributing them into three classifiers: domain name features, search engine opinion (SEO) information of the domain, and security scans by security vendors. A total of eleven classification rules were formulated which were distributed as follows: six rules for the first classifier, four rules for the second classifier, and one rule for the third classifier. Each classifier feeds its results to the main classifier, which then evaluates the received results based on its rules. Domain names from 222 confirmed phishing URLs were used to test the suggested model. The model demonstrated exceptional performance, achieving an impressive accuracy rate of 99.1% during testing. © 2023 IEEE.

关键词： Websites

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applications of fuzzy logic and artificial neural network for solving real world problem

Applications of fuzzy logic and artificial neural network fo...

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International Conference on Advances in engineering, Science and Management (ICAESM)

作者： R. Ramya R. Anandanatarajan R. Priya G. Arul Selvan Department of Computer Applications Research Scholor Dr. M.G. R. Educational & Research Institute Karaika Chennai Department of Electronics and Instrumentation Engineering Pondicherry Engineering College Puducherry Department of Software Engineering and Information Technology A.V.C. College of Engineering Nagapattinam District Tamil Nadu Department of Computer Science and Engineering EGS Pillay Engineering College Nagapattinam Tamil Nadu

Most of the neural networks in initial stage of enthusiasm, the field survived a period of frustration and disrepute. The reason for this is previous neuron doesn't do anything that conventional computers don't do already. Moreover they are observed to be too complex when noticed by humans as well as computers. Moreover we are furiously rising in fourth generation to increase the neural network standard in neurology and psychology. They are regularly used to model parts of living organisms and to investigate the internal mechanisms of the brain. At present the most exsisting aspect of neural networks is the possibility that some day `conscious' networks might be produced. Fuzzy logic and Neural network provide new method for desining control system and fuzzy logic and Neural networks can start with an approximate control knowledge base and refine it through inforcement learning.

关键词： Fuzzy logic Neural networks Biological system modeling Adaptation models Feeds Business

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Neural Networks and Deep Learning: Innovations and applications in Soft Computing

Neural Networks and Deep Learning: Innovations and Applicati...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Jahagirdar Imran Abdulkadar Chinnem Rama Mohan P. Vijaya Bharati Shamik Palit Gunda Srinivasa Rao Pankaj Mudholkar Computer Application Savitribai Phule Pune University Department of Computer Science and Engineering Narayana Engineering College Nellore Andhra Pradesh India Department of CSE Vignans Institute of Engineering for Women Visakhapatnam Andhra Pradesh India Department of Computing Science and Software Engineering University of Stirling RAK Campus UAE School of Management and Commerce Mallareddy University Kompally Faculty of Computer Applications Marwadi University Rajkot Gujarat India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The Paper explores different aspects of deep learning techniques and neural networks in the fields of healthcare, time-series forecasting, agriculture, and other relevant sectors through soft computing. The objective of the report is to examine the significant potential and applications of neural networks. While machine learning techniques called neural networks (NN) are utilized for audio, picture, and the processing of natural languages, it has huge implications in robotics where several machine learning algorithms are significantly required. Improvements in deep machine learning are also investigated in the article specifically in the fields of drug construction, genetics, recognizing faces, farming, biological medicine, biological informatics, medical treatment, natural language, multimedia analysis of data, and mobility prediction. However black box challenges and privacy concerns with data are often ignored in this research. The paper also outlines deep learning-driven data mining techniques for precisely predicting, displaying, evaluating, and classifying data. The report has also emphasized on addressing the potential privacy concerns and challenges associated to information representation. By reviewing recent literature sources about neural network techniques, the study proposes innovation strategies and potential improvement areas for the development of advanced technologies like deep learning and neural networks while addressing the key privacy concerns in data processing.

关键词： Deep learning Technological innovation Data privacy Privacy Natural languages Process control Real-time systems Informatics Biological neural networks Robots

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