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A Neuro-Fuzzy Approach to Road Traffic Congestion Prediction

computers, Materials & Continua 2022年第10期73卷 295-310页

作者： Mohammed Gollapalli Atta-ur-Rahman Dhiaa Musleh Nehad Ibrahim Muhammad Adnan Khan Sagheer Abbas Ayesha Atta Muhammad Aftab Khan Mehwash Farooqui Tahir Iqbal Mohammed Salih Ahmed Mohammed Imran BAhmed Dakheel Almoqbil Majd Nabeel Abdullah Omer Department of Computer Information System(CIS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Science(CS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Pattern Recognition and Machine Learning Lab Department of Software EngineeringGachon UniversityInchonKorea Department of Computer Science National College of Business Administration and EconomicsLahorePunjab54000Pakistan Department of Computer Science Government College University(GCU)LahorePunjab54000Pakistan Department of Computer Engineering(CE) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia College of Business Administration Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Networks and Communications(NC) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia

The fast-paced growth of artificial intelligence applications provides unparalleled opportunities to improve the efficiency of various *** as the transportation sector faces many obstacles following the implementation and integration of different vehicular and environmental aspects *** congestion is among the major issues in this regard which demands serious attention due to the rapid growth in the number of vehicles on the *** address this overwhelming problem,in this article,a cloudbased intelligent road traffic congestion prediction model is proposed that is empowered with a hybrid Neuro-Fuzzy *** aim of the study is to reduce the delay in the queues,the vehicles experience at different road junctions across the *** proposed model also intended to help the automated traffic control systems by minimizing the congestion particularly in a smart city environment where observational data is obtained from various implanted Internet of Things(IoT)sensors across the *** due preprocessing over the cloud server,the proposed approach makes use of this data by incorporating the neuro-fuzzy ***,it possesses a high level of accuracy by means of intelligent decision making with minimum error *** results reveal the accuracy of the proposed model as 98.72%during the validation phase in contrast to the highest accuracies achieved by state-of-the-art techniques in the literature such as 90.6%,95.84%,97.56%and 98.03%,*** far as the training phase analysis is concerned,the proposed scheme exhibits 99.214% accuracy. The proposed prediction modelis a potential contribution towards smart cities environment.

关键词： Neuro-fuzzy machine learning congestion prediction AI cloud computing smart cities

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Deep Learning-Based Age and Gender Prediction through Feature Extraction from Facial Images using Convolutional Neural Networks 5

Deep Learning-Based Age and Gender Prediction through Featur...

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5th IEEE International Conference on Communication, Computing and Industry 6.0, C2I6 2024

作者： Manasa, S.M. Ramamoorthy, Raghu Velu, Anitha Angel Donny, F. Chaitrashree, S. Ramaiah Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India The Oxford College of Engineering Department of Computer Science and Engineering Bengaluru India Sri Sairam College of Engineering Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350379884

In recent years, age estimation and gender classification have been focal points in the field of pattern recognition and computer vision. This paper introduces an automated approach for predicting age and gender by extracting features from human facial images. In contrast to conventional methods that operate on unfiltered facial images, our study demonstrates significant improvements in these tasks through the use of deep convolutional neural networks (CNNs). The employed feedforward neural network method enhances robustness for highly variable unconstrained recognition tasks, specifically targeting gender identification and age group estimation. The research methodology involves the analysis and validation of gender prediction and age estimation on two benchmark datasets: the Essex face dataset and the Adience benchmark dataset. Results indicate a substantial performance enhancement, showcasing the proposed approach's efficiency and state-of-the-art performance in both age and gender detection. The model achieves remarkable accuracy, positioning it as a notable advancement in the realm of age and gender prediction using deep learning techniques. © 2024 IEEE.

关键词： Convolutional neural networks

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A Systematic Investigation of Cyber Harassment Intention Behaviors and Its Impacts on Social Media Platforms

A Systematic Investigation of Cyber Harassment Intention Beh...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Abarna, S. Sheeba, J.I. Devaneyan, S. Pradeep Puducherry Technological University Department of Computer Science and Engineering India Sri Venkateshwaraa College of Engineering and Technology Department of Mechanical Engineering Puducherry India

ISBN: (纸本)9798350348217

Several technologies have been able to fundamentally affect our lives in just a few decades by integrating themselves into our society. News and opinion are now mostly shared on social media. There are both positive and negative aspects to this. Nowadays, practically everyone has a smartphone or other internet-accessing gadget. Modern technology becomes available to kids at progressively younger ages. The majority of them use social media, which contributes to the rise of cyber harassment and the negative aspects of the internet. Due to the ease of access to these platforms, cyberbullying is prevalent on social media. Moreover, when dealing with others, the perpetrators have the ability to remain anonymous. Cyberbullying statistics for 2022 highlight the alarming incidence of harassment in the virtual world, and they are disturbing. According to these data, Cyberbullying sufferers are more than twice as likely to self-harm or entertain suicidal thoughts as a coping mechanism. Anxiety and sadness might develop in certain victims of cyberbullying. Psychologists believe that social media has contributed to an increase in stress and depression over the last decade. On the other side, the most typical targets for online bullies are young children, women, and individuals with non-traditional sexual orientations. Students are often unable to concentrate on their studies because they are afraid of the trolls. As a result, this Research Essential focuses on Cyber harassment statistics and facts in order to raise awareness among parents, students, and authorities worldwide and reveal alarming facts about virtual harassment, the intention of the harasser, its impact, the relationship between victim and harasser, and its many different shapes and forms. © 2023 IEEE.

关键词： Social networking (online)

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Phishing URL Detection and Reporting System Using Machine Learning Approach 3

Phishing URL Detection and Reporting System Using Machine Le...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Aman, D. Reddy, J. Kartheek Sai, B. Manoj Dhanalakshmi, D. Hariharan, Shanmugasundaram Kukreja, Vinay Vardhaman College of Engineering Department of Cse Hyderabad India Chitkara University Institute of Engineering and Technology Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350342338

Phishing attacks continue to pose a threat to the internet users due to the consequences they bring, including the compromise of sensitive information, financial harm and reputational damage. In this research paper, we propose to create a free access, ad-free and non-profit website that uses machine learning algorithms to identify phishing URLs. This website shall be designed to provide users with a reliable and user-friendly tool to evaluate the legitimacy of the URLs they come from. Using different data-sets containing phishing and legitimate URLs, extract important features to train the classification model. The model will be embedded in website, which will evaluate user-submitted URLs and provides true phishing detection. There exists report, which claims on the website where users can contribute to the collective fight against phishing attacks by reporting phishing URLs they encountered. In this way users actively contribute to the development of the and improved efficiency of the system. © 2023 IEEE.

关键词： Ad-free Platform Cyber Security Machine Learning Non-Profit Organization Phishing detection Reporting System URL Analysis

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Time-Division Online Speculative Inference for Cost-Effective Intelligent Internet of Things 10

Time-Division Online Speculative Inference for Cost-Effectiv...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Du, Yao Wang, Zehua Leung, Cyril Leung, Victor C.M. The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

ISBN: (纸本)9798350373011

Intelligent Internet of Things (IIoT), a network paradigm, is an interconnection of intelligent edge devices, empowered by machine learning models. The recent emergence of large language models (LLMs) opens a new path towards IIoT. Although device models are generally lightweight and suitable for edge devices with limited processing power, their performance is less impressive compared to LLMs running on large servers. Server models offer superior performance with the cost of significant computational resources for model inference. To realize cost-effective IIoT, we introduce a novel collaborative inference framework: the online speculative inference enabled by device collaboration and time divisions. We aim to find the minimum number of servers required for the immediate processing of arrived inference requests. To solve the problem, we propose an evolving directed acyclic graph along with a Proof-of-Deletion mechanism for cost reduction and privacy protection. Based on computer simulations using heterogeneous LLMs, it is found that time-division online speculative inference is a promising approach towards cost-effective IIoT. © 2024 IEEE.

关键词： Adversarial machine learning

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Experimental investigation of mechanical properties and multi-objective optimization of electronic, glass, and ceramic waste–mixed concrete

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Environmental science and Pollution Research 2024年第46期31卷 57158-57176页

作者： Mageswari, Deivasigamani Uma Kareemullah, Hakim Jithesh, Korothan Boopathi, Sampath Rachel, Paul Manoharan Premkumar Priya Ramkumar, Mathiyalagan Siva Department of Science and Humanities RMK Engineering College Thiruvallur district Nadu Tamil601206 India Department of Electronics and Instrumentation Engineering B.S.A Crescent Institute of Science and Technology Tamil Nadu Chennai600048 India Department of Computer Science Mahatma Gandhi College Kerala Iritty Kannur670703 India Department of Mechanical Engineering Muthayammal Engineering College Tamilnadu Namakkal637 408 India Department of Civil Engineering Saveetha School of Engineering Thandalam Tamil Nadu Chennai602107 India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Tamil Nadu Coimbatore641021 India

The utilization of waste from various sources plays an important role in minimizing environmental pollution and civil construction costs. In this research, the mechanical properties of concrete were studied by mixing electronic waste (EW), glass powder (GW), and ceramic tile waste (CW). The effects of weight percentages of EW, GW, and CW are considered to investigate improvements in mechanical properties such as compressive strength (CS), split tensile strength (STS), and flexural strength (FS) of concrete. Taguchi analysis has been applied to predict the optimum composition of waste mixing percentages. The Multi-Objective Optimization Ratio Analysis (MOORA) techniques are applied to estimate the optimum composition of mixing wastes for maximizing the CS, STS, and FS of concrete. It was observed that 10 wt.% of EW, 15 wt.% of GW, and 30 wt.% of CW are predicted as the optimal mixing combinations to obtain a maximum compressive strength of 48.763 MPa, a split tensile strength of 4.178 MPa, and a flexural strength of 7.737 MPa, respectively. Finally, the predicted optimum waste-mixed weight percentages were used to examine the microstructure and various elements in the concrete using SEM and XRD analysis. When compared to conventional concrete, the optimum waste-mixed concrete has improved its compressive strength (38.453%), split tensile strength (41.149%), and flexural strength (36.215%). © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Compressive strength

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Predicting Cyber Identity Theft: A Cutting-Edge Approach using Cascaded ResNet-LSTM Deep Learning Models 4

Predicting Cyber Identity Theft: A Cutting-Edge Approach usi...

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

作者： Chamundeswari, G. Vavilapalli, Sivajee. Poonkuzhali, P. Sahu, Ajay Kumar Yadav, Ajay Singh Gopinath, D. Ramachandra College of Engineering Department of Computer Science and Engineering A.P Eluru India Department of Management Studies AP India R.M.D. Engineering College Department of Ece Kavaraipettai India Bajaj Institute of Technology Department of Computer Engineering Arvi Road Pipri Maharashtra Wardha442001 India Srm Institute of Science and Technology Department of Mathematics Delhi-NCR Campus U.P Ghaziabad India Department of Computer Science Karnataka Bengaluru India

ISBN: (纸本)9798331529635

Cyber identity theft has become a growing concern in the digital age, as personal and sensitive data becomes increasingly vulnerable to cyberattacks. Detecting and preventing identity theft is a critical challenge for organizations and individuals alike, requiring advanced machine learning and deep learning techniques to stay ahead of malicious attacks. In this paper, we propose a novel hybrid model, Cascaded ResNet-LSTM, for the prediction of cyber identity theft. The model integrates Residual Networks (ResNet) for spatial feature extraction and Long Short-Term Memory (LSTM) for temporal sequence learning. The combination of these two architectures allows the model to efficiently detect complex identity theft patterns by capturing both spatial and temporal dependencies in the data. The dataset utilized for this study, sourced from Kaggle, consists of 25 varied metrics and 40,000 records. Comprehensive preprocessing steps, including missing value handling, outlier detection, and dimensionality reduction using an AutoEncoder, were applied. The proposed model achieves a significantly higher accuracy of 92.65%, compared to existing models such as XGBoost (88.10%), Random Forest (86.35%), and Support Vector Machine (85.20%). Additional performance metrics, including precision (93.45%), recall (91.30%), and F1-score (92.35%), further emphasize the effectiveness of the proposed model in identifying cyber identity theft with high reliability. The hybrid architecture of ResNet and LSTM enables the model to outperform traditional approaches by reducing false positives and enhancing detection accuracy, making it a robust solution for cybersecurity applications. © 2024 IEEE.

关键词： Dimensionality reduction

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Leveraging AI for Enhanced Botnet Detection - A Review of Machine Learning Approach for Cybersecurity 3

Leveraging AI for Enhanced Botnet Detection - A Review of Ma...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Sonune, Harsha Kulkarni, Nilima Mksss's Cummins College of Engineering for Women Department of Information Technology Pune India Mitadt University Mitsoe Department of Computer Science and Engineering Maharashtra Pune India

ISBN: (纸本)9798350354348

Botnets represent a substantial threat to cybersecurity, enabling malicious actors to execute large-scale attacks that compromise network integrity and data confidentiality. Current detection methods often fall short in addressing the rapidly evolving strategies employed by botnets. This study has an aim of designing and developing a sophisticated botnet detection system leveraging artificial intelligence (AI) techniques. By integrating machine learning algorithms and advanced data analysis methods, the proposed system seeks to accurately identify and mitigate botnet activities in real-time. The study will evaluate the effectiveness of various AI models in detecting botnet traffic, provide a comparative analysis of their performance, and propose an optimal framework for implementation in cybersecurity infrastructures. © 2024 IEEE.

关键词： Bot (Internet)

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Prevention of Road Accidents Through Smart Assistive technology with Artificial Intelligence Approach

Prevention of Road Accidents Through Smart Assistive Technol...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Sai, Thota Jaya Chandra Rehaman, Syed Abdul Sriya, Sembeti Dhanalakshmi, D. Hariharan, Shanmugasundaram Kukreja, Vinay Vardhaman College of Engineering Department of CSE Hyderabad India Chitkara University Institute of Engineering and Technology Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350315127

The creation of a sophisticated two-wheeler helmet in India seeks to drastically lower the amount of traffic accidents brought on by careless and inebriated driving. It is critical to solve this issue since, according to data, two-wheelers are involved in around 25% of traffic incidents in India. Enormous number of sensors have been incorporated into the construction of the smart helmet. First of all, alcohol sensors are built in to determine whether a cyclist has drunk before riding. This function can serve as a preventative measure by discouraging people from driving when intoxicated, hence lowering the possibility of accidents brought on by poor judgment. Additionally, a rider's helmet is continually monitored by an infrared sensor. Additionally, the helmet has a vibration detector attached to detect harsh impacts during accidents. This sensor will send an alarm to emergency services or predetermined contacts in the case of a collision, enabling prompt aid and potentially life-saving treatments. The two-wheeler's whereabouts may be tracked in real-time thanks to the GPS capability. The GPS on the helmet can determine the rider's location in the event of an accident, assisting first responders in quickly reaching the scene even in outlying or unfamiliar locations. The ingenious helmet attempts to make two-wheeler riders in India safer by integrating these cutting-edge sensors. The entire system can provide fast aid in times of need in addition to preventing accidents brought on by intoxicated, careless, or fatigued driving. This program might potentially save many lives and encourage safe driving habits, which would ultimately lead to a decline in traffic accidents and their severe effects. © 2023 IEEE.

关键词： Global positioning system

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Understanding Time Series Analysis for Improved Forecasting Techniques

Understanding Time Series Analysis for Improved Forecasting ...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Singh, Umesh Kumar Kannagi, A. Shukla, Meenu College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

Time collection evaluation is a vital device used in forecasting strategies. It involves looking at the temporal behavior of records set in which observations are recorded in sequence over time. Using diverse statistical and mathematical techniques, traits, discrepancies, and styles inside the records may be identified and used to make predictions or forecasts approximately what may manifest in the future. For example, time collection evaluation may be used to forecast sales, customer spending, inventory costs, or every other variable being monitored. It is changing through the years. Stepped-forward forecasting techniques can be acquired by as it should be figuring out and considering seasonality, fashion, and different temporal functions within the information set. Better predictions may be made for destiny by applying state-of-the-art fashions and algorithms, including the ARIMA (autoregressive integrated shifting average) version. ultimately, the goal of time series analysis is to broaden predictions that accurately replicate the natural order of device conduct. © 2024 IEEE.

关键词： accurately collection evaluation considering manifest mathematical

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