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smart Gunshot Detection and Reporting Framework 4

Smart Gunshot Detection and Reporting Framework

引用

4th IEEE-Nigeria-Section international conference on Disruptive Technologies for Sustainable development (IEEE NIGERCON)

作者： Hamidu, Ibrahim Suleiman, Muhammad Aliyu Abdullahi, Ibrahim Nile Univ Nigeria Software Engn & IT Dept Abuja Nigeria

ISBN: (纸本)9781665479783

We propose a smart IoT framework for gunshot detection. the smart gunshot system detects gunshot sounds and transmits sound signals to a data centre where a trained machine learning model classifies the sound signal data and sends alerts to the base station where the response monitoring system deployed will be notified for further action. this study focuses on the design of the conceptual smart gunshot detection framework. When implemented, the study will help in mitigating crimes and insurgency in societies. Our use case is against armed banditry in Northwestern Nigeria.

关键词： IoT Device Data Science Gunshot Crime detection Banditry

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10th international conference on smart Education and e-learning, SEEL 2023

10th International Conference on Smart Education and e-Learn...

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10th international conference on smart Education and e-learning, SEEL 2023

ISBN: (纸本)9789819929924

the proceedings contain 27 papers. the special focus in this conference is on smart Education and e-learning. the topics include: ecosystems for Higher Education and Society;mathematical Simulation for Quality Management in State System of Higher Education;gaining Insight into Adoption of Immersive Technologies in Higher Education;quasi-Fractal Logic Usage in Risk Management Models of smart Systems;research and Simulation of Cryptocurrency Market as an Innovative Use Case for Emerging smart Education;literary Tourism: A Case Study;a Model of Quality Management System for Digital Economy;a Model for Business System Infrastructure’s Vulnerability Assessment;marketing Research for regional development;digital Technologies for Quality Management in Integrated Production;innovative Marketing Strategy for Industry;innovative Methods for smart Education: Hybrid Approach;impact of Offline and Online Lecture Formats on Student Satisfaction with the University;possibilities of Using Multimedia Technologies in the learning Process for Children with Special Needs;online Ed.D. Program development: A Program Level Perspective;Using CAI to Provide Early Literacy Instruction for All Learners;online Doctoral Faculty Engagement: Building Connections through Authenticity;Reliability Issues with At-Home Assessment During the COVID-19 Pandemic;preface;smart Education: Students’ Perception of Hybrid learning in Graduate Computing Curriculum;student Support in an Online Environment: Doctoral Student Feedback;organizing the University 4.0: New Goals and Insights to Promote the Digital Transformation of Higher Education Institutions to Succeed Next E-learning Era;systematic Approach to Project Management at smart University;the Concept of New-Generation Lecturers for smart Universities: Case Study and Trends.

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the integration of IoT in learning environments: Assessing the impact of educational empowerment and addressing its challenges 4

The integration of IoT in learning environments: Assessing t...

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4th international conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Rajora, Ricky Rajora, Aarju Sharma, Bhanu Aggarwal, Priyanshi thapliyal, Siddhant Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Chandigarh Engineering College Department of Computer Science & Engineering Mohali India Computer Science & Engineering Graphic Era Hill University Uttarakhand Dehradun India Computer Science & Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun India

ISBN: (纸本)9798350307757

this article explores the diverse impact of the Internet of things (IoT) on education. this research investigation examines the possible uses of the IoT in several educational domains, with a particular focus on the significant impact of IoT on student engagement, learning methodologies, and administrative efficacy. While explaining the considerable advantages, this discourse also acknowledges the significant obstacles pertaining to integration, security, and privacy. the study finishes by presenting a forward-looking model for smart campuses, providing a holistic viewpoint on the IoT impact on transforming education. the article examines the impact of the IoT on educational practices and emphasizes the significance of institutional adaptation to effectively incorporate this technological paradigm shift. the article presents an elaborate framework for the prospective development of intelligent campuses and higher education institutions. this article offers a comprehensive guide for effectively using the whole capabilities of the IoT within the realm of education. © 2024 IEEE.

关键词： Internet of things

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Machine learning Applications for Online Partial Discharge Detection, Classification, and Localization in Power Transformers: A Review 4

Machine Learning Applications for Online Partial Discharge D...

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4th international conference on smart Grid and Renewable Energy, SGRE 2024

作者： Tag, Ameera Refaat, Shady S. Kameli, Sayed Mohammad Saleh, Mohammad Alshaikh Texas A&M University at Qatar Electrical and Computer Engineering Department Doha Qatar University of Hertfordshire School of Physics Engineering and Computer Science Department of Engineering United Kingdom Texas A&M University Department of Electrical and Computer Engineering College Station United States

ISBN: (纸本)9798350306262

the power transformer is a crucial asset and a fundamental component of the power grid. Assets undergo aging due to the stresses present in insulation materials. Partial discharges (PDs) are the most common fault source in power transformers and an excellent indicator of aging. the detection, classification, and localization of PD activities in power transformers are persisting challenges, while techniques utilizing machine learning (ML) are widely sought to deal with those challenges. Existing ML techniques show promising results with an elevated level of accuracy and precision. However, there is a lack of conventional ML-based real-time monitoring capability. therefore, this paper presents a comprehensive review of the application of ML techniques for online PD activity detection, classification, and localization in power transformers, focusing on supervised, unsupervised, semi-supervised, and reinforcement learning techniques. In addition, this paper explores the challenges, future trends, perspectives, and outlook of machine learning for online transformer fault analysis. © 2024 IEEE.

关键词： Partial discharges

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the Best of Way of using AI Technology in Designing Technical Education Curriculum in Meeting Future Industry Demands: smart Way 4

The Best of Way of using AI Technology in Designing Technica...

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4th IEEE international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Saddam, H. M. Issa Hasan, Ghassan college of medical sciences at hajjah Al-Razi University Yemen college of Education and human sciences Yemen Hajjah University Yemen King Faisal University Al Hofuf Saudi Arabia

ISBN: (纸本)9798350360165

Artificial Intelligence in curriculum design would come out as the most important strategy for the academic rigor to be bridged with industry in this rapidly changing scenario of higher education. the paper examines the potential transformational impact of AI on personalizing and enhancing educational frameworks in order to ensure these meet dynamic workforce requirements. We elaborate in this paper how AI-powered tools assist in the development of academic curricula while considering real-time industry trends and the requirements of industries in regard to skills;the analysis of how AI-powered data analytics, predictive modeling, and adaptive learning technologies will aid educators in creating a more responsive and flexible learning environment. In a nutshell, we find AI in curriculum design;thus, it will result in a momentous contribution to graduating academically competent and work-ready graduates, making a big difference in the educational ecosystem of the world. Our results present AI in curriculum design as a potential powerful tool for making a large contribution to the production of work-ready, academically competent graduates and for making a big difference in the world's educational ecosystem. © 2024 IEEE.

关键词： Federated learning

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Predictive Power of Machine learning in Chronic Disease: A Comprehensive Review and Meta-Analysis 4

Predictive Power of Machine Learning in Chronic Disease: A C...

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4th international conference on smart Electronics and Communication, ICOSEC 2023

作者： Rani, Akanksha Garg, Shivangi Sharma, Astha Kumar, Ashwni Indira Gandhi Delhi Technical University for Women Department of Electronics and Communication Engineering New Delhi India

ISBN: (纸本)9798350300888

Chronic diseases pose a significant global health challenge, affecting over a quarter of adults and placing a big burden on healthcare systems and individuals. the advent of the 'smart Healthcare' era and edge cut technologies opened up new possibilities for managing chronic diseases. this article focuses on chronic diseases with a high risk of development, including lung carcinoma, breast cancer, colorectal cancer, gastrointestinal cancer, liver cancer, cataracts, and cardiovascular-cerebrovascular diseases. the rates of these diseases were found to be 57.85%, 74.63%, 22.13%, 41.65%, 53.39%, 20.24%, and 83.94%, respectively. the study discusses the implications of these findings and underscores the importance of leveraging advanced technologies for effective management and prevention of chronic diseases. © 2023 IEEE.

关键词： Diseases

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Privacy-Preserving Authenticated Federated learning Scheme for smart Healthcare System 1

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4th international conference on Emerging Information Security and Applications (EISA)

作者： Tu, Jun Shen, Gang Hubei Univ Technol Sch Comp Sci Wuhan 430068 Peoples R China

ISBN: (数字)9789819996148

ISBN: (纸本)9789819996131;9789819996148

With the rapid advancement of artificial intelligence and network technology, smart healthcare system provides patients with satisfactory medical experience and clinical diagnosis, thus alleviating the imbalance between limited medical resources and a large patient population. However, the patient privacy security in smart healthcare system is still facing severe challenges. Additionally, due to the signal interruption in the federated learning mechanism, the model parameters can not be transmitted normally between local user and central server. In response to these issues, we propose a privacy-preserving authenticated federated learning scheme for smart healthcare system. Specifically, there is a hybrid federated learning framework composed of peer-to-peer and server-client architecture in the proposed scheme. In the proposed scheme, data owners can interact directly with each other for federated training to overcome the data silos issue. In addition, we leverage a homomorphic cryptosystem and the Schnorr signature algorithm to ensure the security and integrity of local model parameters. Security analysis and experimental results show that the proposed scheme can not only protect the sensitive information of data owners, but also has high efficiency.

关键词： Privacy preservation Federated learning smart healthcare system Authentication Homomorphic encryption

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Predicting the Severity of Parkinson's Disease Based on Voice Analysis Using Deep learning 4

Predicting the Severity of Parkinson's Disease Based on Voic...

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4th international conference of Science and Information Technology in smart Administration, ICSINTESA 2024

作者： Jasim, Huda Alshakarchy, Noor D. University of Kerbala College of Computer Science and Information Technology Kerbala Iraq

ISBN: (纸本)9798350376111

Parkinson's disease (PD) is a progressive neurological disorder characterized by motor and non-motor symptoms. Prompt and precise identification of the severity of Parkinson's disease is crucial for customized treatment strategies and enhanced patient care. this study utilized voice data and deployed several convolutional neural network (CNN) architectures, such as Long Short-Term Memory (LSTM), autoencoder, and 1D CNN, to accurately predict the severity of Parkinson's disease (PD). this analysis utilizes a dataset of speech recordings from patients with Parkinson's disease at different levels. these severity levels are utilized to reflect the degree of Parkinson's disease severity in the study. the voices have undergone preprocessing, and a feature extraction method has been performed to enhance the voice features, specifically focusing on phonation, articulation, and prosody. Correlation-based feature selection is used to decrease the number of dimensions. the CNN models are trained to extract pertinent elements from the audio data, allowing for the categorization of PD severity. the experimental results indicate that the LSTM, autoencoder, and 1D CNN models achieved accuracy rates of 98%,95%, and 96% respectively, which are highly promising. © 2024 IEEE.

关键词： Convolutional neural networks

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A review of deep learning for traffic flow prediction 4

A review of deep learning for traffic flow prediction

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2024 4th international conference on Intelligent Traffic Systems and smart City, ITSSC 2024

作者： Zhang, Hao Ren, Bin He, Chunhong Dongguan University of Technology Dongguan523000 China Dongguan City University Dongguan523000 China

ISBN: (纸本)9781510686304

As urban landscapes and transportation infrastructures evolve, the precision of traffic flow prediction within Intelligent Transportation Systems (ITS) becomes paramount. It is vital for mitigating traffic congestion and for refining autonomous driving and route planning. Yet, traditional methods struggle with the intricacies and vastness of modern data. this paper surveys traditional approaches before exploring deep learning techniques that adeptly model the nonlinear, spatio-temporal complexities of traffic flows. It examines how deep learning harnesses spatial and temporal data correlations, offering a robust framework for ITS. the discussion concludes with the challenges in traffic prediction and opportunities for future research, charting a course for innovative solutions. © 2025 SPIE.

关键词： Traffic congestion

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Optimizing Rice Variety Recognition through Transfer learning with Pretrained ResNet Models 4

Optimizing Rice Variety Recognition Through Transfer Learnin...

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4th international conference of Science and Information Technology in smart Administration, ICSINTESA 2024

作者： Prasetyo, Simeon Yuda Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350376111

the identification of rice varieties holds paramount importance in addressing global food challenges, given that rice serves as a staple food source for more than half of the world's population. Accurate identification is crucial for ensuring food security and meeting the dietary needs of diverse populations. However, accurately distinguishing between rice varieties remains a complex task due to genetic mixing and hybridization, which can lead to variations in morphological and genetic traits among different varieties. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), offer promising solutions for improving rice variety classification by leveraging large datasets and sophisticated image processing techniques. this study conducts a comprehensive comparative analysis of ResNet50 and ResNet101 models in identifying rice varieties using transfer learning and fine-tuning approaches. Leveraging a dataset containing 75,000 high-resolution rice images representing various varieties, the models undergo thorough evaluation based on various performance metrics, including accuracy, precision, recall, and F1-score. Results highlight the effectiveness of pretrained ResNet models in accurately classifying rice varieties, with ResNet50 achieving the highest accuracy of 98.813% post-fine-tuning. these findings underscore the significance of fine-tuning pretrained models to enhance classification accuracy, contributing to the advancement of precision agriculture and sustainable food production practices. the insights gained from this study have implications for improving crop management practices, optimizing resource allocation, and ultimately, enhancing global food security. © 2024 IEEE.

关键词： Convolutional neural networks

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