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4th IEEE International Conference on Advance computing and Innovative Technologies in engineering, ICACITE 2024

作者： Banu, E.Afreen Chidambaranathan, Senthilnathan Jose, Naduvathezhath Nessariose Kadiri, Padmaja Abed, Riyad E. Al-Hilali, Aqeel Saveetha School of Engineering Chennai India Architecture and Design Virtusa NJ United States Regent Strategic Solutions Rochester United States Mohan Babu University School of Computing Department of Aiml Andhra Pradesh Tirupati India National University of Science and Technology College of Health and Medical Technology Dhi Qar64001 Iraq Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350360165

The agricultural business has seen a rise in the use of mobile robots because of their ease of navigation in crop fields. Numerous significant issues pertaining to agricultural robot navigation are covered in this paper, such as obstacle detection, route planning, mapping, localization, and controlled guiding. The proposed hybrid recurrent neural network (RNN) model seeks to optimise mobile autonomous robot tracking performance using installed laser-based weeding equipment. The system can handle a range of trajectories, including curves and straight lines, that are observed in agricultural settings by integrating spiral, lateral, and linear speed controls. A comprehensive field test is carried out on a tracked mobile platform to validate the controllers' functionality in a variety of scenarios, including loose dirt, stones, and humidity. © 2024 IEEE.

关键词： Recurrent neural networks

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Gender-Specific Speech Enhancement architecture for Improving Deep Neural Networks Learning

Gender-Specific Speech Enhancement Architecture for Improvin...

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International Conference on Innovation and Intelligence for Informatics, computing, and Technologies (3ICT)

作者： Soha A. Nossier Mhd Saeed Sharif Biomedical Engineering Department Medical Research Institute Alexandria University Alexandria Egypt Intelligent Technologies Research Group School of Architecture Computing and Engineering UEL London United Kingdom

ISBN: (数字)9798331533137

ISBN: (纸本)9798331533144

Deep learning techniques for speech enhancement rely on training a deep neural network to process noisy speech, regardless the gender of the speaker. However, research shows that the speech of male and female stimulates different parts in human brain, and that female speech requires more complex analysis. This implies that different processing is applied on the speech, based on the speaker gender. In this work, we argue that male and female speeches have different features that can help in the learning process of speech enhancement deep neural networks if the training is performed on male and female speech data, independently, and using two different deep neural networks, specifically implemented for enhancing the noisy speech signal of the target gender. This work presents a gender-specific speech enhancement architecture, which consists of a front-end binary classifier to detect the speaker gender. Based on the classifier decision, the noisy speech is enhanced using either a male or female speech enhancement model. One-stage and two-stage speech enhancement approaches are used to process male and female speeches, respectively. The results reveal that gender-specific speech enhancement has positive impact on the enhanced speech by deep neural networks. Additionally, the developed architecture achieved classifier accuracy 96.9% and 0.11 increase in Covl speech quality metric for the test data, in comparison to other best-performing networks.

关键词： Training Deep learning Technological innovation Computational modeling Noise reduction Computer architecture Speech recognition Speech enhancement Noise measurement Informatics

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Introduction to the Special Issue on Perspectives on Soundscape and Challenges of Noise Pollution: A Multidisciplinary Approach to Sustainable Environmental Solutions

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Sound & Vibration 2023年第1期57卷 69-71页

作者： Tallal Abdel Karim Bouzir Djihed Berkouk Institute of Architecture and Urban Planning Blida UniversityBlida09000Algeria Department of Architecture Hekma School of EngineeringComputing and DesignDar Al-Hekma UniversityJeddah22246Saudi Arabia

Over the past two decades,research on the subject of noise pollution and urban soundscapes has seen significant growth[1,2].The goal of these studies was to gain a better understanding of the urban acoustic environment by employing various methodologies and techniques to delve into the complexity of this *** research efforts have primarily revolved around two fundamental axes[3].On one hand,the first axis focused on combating noise pollution[4–6],emphasizing the reduction of unwanted sounds and compliance with sound levels set by environmental and health protection organizations[7,8].

关键词： primarily Sound pollution

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Application of Clustering Algorithms to Enhance Personalized Learning Through Recommendation Model

Application of Clustering Algorithms to Enhance Personalized...

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International Conference on Innovation and Intelligence for Informatics, computing, and Technologies (3ICT)

作者： Mhd Saeed Sharif Madhav Theeng Tamang Dyuthi Mani Wael Elmedany Intelligent Technologies Research Group School of Architecture Computing and Engineering UEL London United Kingdom College of Information Technology University of Bahrain Kingdom of Bahrain

ISBN: (数字)9798331533137

ISBN: (纸本)9798331533144

Technology and education have recently changed student involvement with learning resources and educational experiences. It is very important to have a platform which can provide personalized learning resources and group students by learning patterns. The research develops recommendation systems and grouping algorithms to help students with their learning patterns and performance measures. To record multidimensional student learning experiences, demographics, learning activities, problem-solving behaviours, and performance measures are extracted and processed. This system makes personalised student suggestions via collaborative filtering, especially Alternating Least Squares (ALS). The model predicts learning materials and problem sets based on student preferences and ability levels by analysing historical student interactions with learning resources. The research also uses clustering methods like K-means clustering to group students with similar learning and performance patterns. Clustering analysis lets instructors discover student group traits and tailor interventions and support techniques. This research also examines temporal relationships in student learning sequences using sequential models. Sequential learning activities and problem-solving behaviour of students help recurrent neural networks (RNNs), or sequential pattern mining algorithms predict their next activities. This research used a huge dataset of over sixteen million exercise logs and applied collaborative filtering (ALS) and K-means clustering to find learning patterns. The ALS-based recommendation system achieved a Mean Squared Error (MSE) of 0.23, showed excellent predictive accuracy. Clustering grouped students by learning behaviours, enabling targeted interventions. This research improves personalised learning using machine learning and data analytics in education. The recommendation system can help instructors tailor training, give individualised support, and boost academic success for various

关键词： Training Technological innovation Recurrent neural networks Collaborative filtering Clustering algorithms Predictive models Prediction algorithms Problem-solving Pupils Recommender systems

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AI-Enhanced Emotion Recognition through Galvanic Skin Response and ECG Analysis for Improved Accuracy and Reliability

AI-Enhanced Emotion Recognition through Galvanic Skin Respon...

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2024 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Mounagurusamy, Mathan Kumar Kaur, Parvinder Bdair, Mohammad Pavithra, G. Thenappan, S. Kalpanadevi, G. Srm University Department of Computing Technologies Kattankulathur Campus Tamil Nadu Chennai India Chandigarh College of Engineering and Technology Department of Electronics and Communication Engineering Chandigarh India School of Architecture Computing & Engineering University of East London London United Kingdom Bannari Amman Institute of Technology Department of Aiml Tamil Nadu Sathyamangalam India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Tamil Nadu Chennai India K. Ramakrishnan College of Engineering Department of Ece Tamil Nadu Turuchirapalli India

ISBN: (纸本)9798331543617

Traditional methods of emotion detection, including the problem of recognizing seven basic emotions, have issues such as occlusion, noise, and self-reporting. Besides text mining there are voices, facial and gesture recognition algorithms as well. The indicated acoustics-based difficulties are resolved in the proposed system by employing the previously mentioned signals - less forgery and interferences for ECG and GSR. When noise from signals is removed, characteristic properties such as pace fluctuation and skin conductance are obtained. All of these features are organized using modern machine learning models such as Support Vector Machines (SVM) and Convolutional Neural Networks (CNN). Measurements of labelled datasets are utilized to verify the system's accuracy levels, and better performance is reported in the dimension of emotion identification resilience. The proposed methodology in this research is more accurate than other existing systems, with an accuracy rate of 90%, recall rate of 88 percent, and F1-Score of 87.5%. Similar to feature metrics, the level of accuracy for particular parameters has improved. HCl technology will improve learning and mental health features such as recognition, emotion, and social interaction. The established goal of physiological ones is to serve as a solid and steady foundation for identifying emotions. © 2024 IEEE.

关键词： Support vector machines

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A Comparative Study of Sales Prediction Using Machine Learning Models: Integration of PySpark and Power BI

A Comparative Study of Sales Prediction Using Machine Learni...

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International Conference on Innovation and Intelligence for Informatics, computing, and Technologies (3ICT)

作者： MhD Saeed Sharif Madhav Theeng Tamang Anup Nepal Wael Elmedany Intelligent Technologies Research Group School of Architecture Computing and Engineering UEL London United Kingdom College of Information Technology University of Bahrain Kingdom of Bahrain

ISBN: (数字)9798331533137

ISBN: (纸本)9798331533144

Retail management requires accurate sales forecasting for strategic planning, inventory control, and revenue maximisation. In this research, we are predicting sales using PySpark-built ML models and the Big Mart dataset. We evaluate and demonstrate the prediction skills of numerous machine learning algorithms, concentrating on XGBoost. Big Mart offers item weight, visibility, type, and outlet data. We use these properties as prediction model features. PySpark, a strong distributed computing platform, manages massive datasets for analysis and model training. In massive trials, we test decision trees (DT), XGBoost, linear regressions (LR), and random forests (RF). Training and testing enhance model accuracy. RMSE and R-squared measure our model quality. Our metrics evaluate the model's data fit and prediction accuracy. XGBoost performed best with an RMSE of 1081 and an R-squared of 0.59. The XGBoost algorithm accurately predicts Big Mart sales. The model performs well because of its ensemble learning and understanding of intricate dataset links. We also used Power BI to present analytical insights, helping decision-makers design sales-estimated business plans. This study employed several machine learning algorithms, XGBoost gave the best performance. This research provides insights into how organisations might use these technologies to improve resource allocation and inventory management decision-making.

关键词： Training Accuracy Machine learning algorithms Biological system modeling Computational modeling Data visualization Predictive models Data models Forecasting Random forests

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Risk Management for 5G-Enabled Internet of Things by Using Machine Learning: A Survey

Risk Management for 5G-Enabled Internet of Things by Using M...

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ICT Conference (AICTC), Arab

作者： Aysha Alfaw Wael Elmedany Mhd Saeed Sharif College of Information Technology University of Bahrain Sakir Bahrain Department Computer Science and Digital Technologies School of Architecture Computing and Engineering London UEL UK

ISBN: (数字)9798350342086

ISBN: (纸本)9798350342093

Nowadays, there is a noticeable increase in the development and use of the Internet of Things(loT). With this rapid increase, IoT devices may face several challenges when used in the real world through applications. Using 5G with large numbers of Internet-connected devices of IoT puts many devices at risk and requires risk management. The growing of IoT leads to the growth of cyber-attackers and allow them to expose the vulnerabilities. This paper will present the risk management of 5G-enabled IoT technology and the use of machine learning to mitigate the risk and reduce the attacks on these technologies, and finding solutions through previous researches. This research aims to identify and eliminate potential risks in the IoT based 5G by machine learning. The paper also aims to present a solution to the security problems and risks faced when integrating the fifth-generation network and the Internet of Things. With 5G-enabled IoT, the risk management helps organizations use emerging technologies effectively while mitigating potential underlying risks such as security breaches, and data loss. Authentication,encryption, access control, and communication security are essential for making security. Machine learning algorithms have the potential to remove many obstacles to implementing the security of the Internet of Things, paving the door for the use of sophisticated technology like 5G. With new 5G networks, it is expected that the current IoT will be significantly expanded, which will improve cellular operations and the security of IoT, as well as push the future of the Internet to its edges. Machine learning (ML) creates a secure and intelligent system and provides a robust security mechanism and dynamic for 5G networks. Therefore, this time will also present previous solutions with machine learning against the risks to IoT and 5G. This paper will present a set of previous studies related to insurance of risk management for the 5G-enabled IoT, Which aims to find previ

关键词： Surveys Machine learning algorithms 5G mobile communication Insurance Machine learning Organizations Internet of Things Security Risk management Intelligent systems

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rECOnnect: Smart Waste Bin for Internet Access

rECOnnect: Smart Waste Bin for Internet Access

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International Conference on Computer engineering, Network and Intelligent Multimedia (CENIM)

作者： Gladwin Acorda Ariel M. Lorenzo Marianne Avila Kathlene Realin Jonathan Solmayor Godwin Sam Mabborang Izella Mae Calalo School of Architecture Computing and Engineering University of Saint Louis Tuguegarao City Philippines University Research and Innovation University of Saint Louis Tuguegarao City Philippines

ISBN: (数字)9798350368802

ISBN: (纸本)9798350368819

This study introduces an innovative waste disposal method by incorporating technology into a plastic bottle trash bin that offers Wi-Fi access as a reward for proper waste management. The “smart bin” is designed to collect plastic bottles and grant internet time to users. It operates using a microcontroller with sensors that detect deposited bottles, displaying the number collected and the corresponding internet access time. Users can press a button to transfer this time to a TP-Link module, which provides a voucher code for internet access. Testing revealed that the bin's infrared (IR) sensor effectively counted bottles when inserted one at a time but struggled with multiple bottles, often registering just one. After five bottles, the system successfully generated a voucher code and reset the count. The study concluded that the smart bin accurately tracks bottles and provides corresponding internet access, ensuring a reliable user experience.

关键词： Presses Codes User experience Internet Plastic products Reliability Waste disposal Intelligent sensors Wireless fidelity Testing

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Analyzing the Pattern of the Jordanian Electricity Peak Load Amid Renewable Energy Transition Considering the GDP and Population Growth

Analyzing the Pattern of the Jordanian Electricity Peak Load...

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International Renewable Energy Congress

作者： Rafat Aljarrah Tayma Afaneh Mohammed Ghazal Electrical Engineering Department Princess Sumaya University for Technology Amman Jordan School of Architecture Computing and Engineering University of East London London UK

This This paper aims to analyze the pattern of the electricity peak load of the Jordanian power network amid the renewable energy transition and to predict the peak load for the next ten years. It investigates the correlation between the population growth and the Gross Domestic Product (GDP), as an economic indicator, of the country and the peak load change through the years. Then, the yearly peak load is predicted using the linear regression model and an Autoregressive Integrated Moving Average (ARIMA) model to have a better insight on the power generation requirements for the next ten years. The results have shown that both the population growth and the GDP have a strong correlation with the yearly peak load of electricity. The results indicate that, if the same rate of growth of both the population and GDP is kept, the electricity peak load would increase from 3770 MW in 2021 up to around 5300 MW in 2034. This represents an increment of 41%, with a yearly rate of 3.4% approximately. In addition, the electricity peak load would be doubled by 25 years.

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Enhancing Phishing Universal Resource Locator Detection Systems Using Hybrid Machine Learning Classifiers and Data Balancing Techniques 12

Enhancing Phishing Universal Resource Locator Detection Syst...

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12th IEEE Conference on Systems, Process and Control, ICSPC 2024

作者： Ibrahim, Mohammed Hadi, Bello Muhammed Jasser, Muhammed Basheer Ajibade, Samuel-Soma M. Ramly, Athirah Mohd Yong, Yoke Leng Issa, Bayan Nigerian Defence Academy Dept. of Cyber Security Kaduna Nigeria Sunway University School of Engineering and Technology Selangor Darul Ehsan Bandar Sunway47500 Malaysia University of East London University Way Docklands Campus School of Architecture Computing and Engineering LondonE16 2RD United Kingdom HELP University Persiaran Cakerawala School of Information and Communication Technology Subang Bestari Seksyen U4 Shah Alam40150 Malaysia University of Aleppo Faculty of Informatics Engineering Syria

ISBN: (纸本)9798350391398

Phishing attacks have become a significant threat to online security, with cybercriminals using sophisticated tactics to evade detection. Traditional phishing detection systems often relied on rule-based approaches, which can be limited in their effectiveness. This research uses machine learning (ML) to detect phishing universal resource locator (URL) by evaluating the performance of advanced classifier for URL phishing detection using both original and balanced datasets. The classifier assessed is Random Forest. In conjunction with data balancing techniques like SMOTE and Resample, on the original dataset, it achieved an accuracy of 98.10% with a Kappa statistic of 96.20%, requiring 1.93 seconds for training and 0.05 seconds for testing. For the balanced dataset, the performance slightly improved, achieving an accuracy of 98.20% and a Kappa statistic of 96.40%, with reduced training time of 1.30 seconds and testing time of 0.03 seconds. Hence, balancing original dataset can significantly improve URL phishing detections using ML. © 2024 IEEE.

关键词： Phishing

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