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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Aarti Chaudhari Saniya Saratkar Trupti Thute Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Alzheimer's disease (AD) is a fatal neuronal disorder which occurs mainly in the elderly and causes a gradual dementia with or without memory loss, functional disability and death. AD is the most prevalent form of dementia, and the condition is positively affiliated with amyloid-beta plaques, tau protein tangles, neuronal ischemic losses, and brain atrophy. Even after studying for a long, the cause(s) of AD has not been well established, though it is well understood that age, genetics (specifically APOE ε4 gene), and lifestyle are some of the leading causes. At the moment there is no cure and management involves the alleviation of symptoms and the prevention of disease progression. The key for better patient management is the early-stage triage as this would increase the chance of getting proper care, fulfilling criteria for clinical trials, and receive better therapeutic management. For a long time now, an early diagnosis has been emphasized due to the improved prognosis, but little has been done in that area instead a lot of efforts are directed at treatment. MRI, PET, and fMRI consisting of imaging approaches that enables the identification of brain changes and progression of the disease. Healthcare & medicine; artificial intelligence (AI) is changing the way Alzheimer's is diagnosed and treated. In fact, classifiers such as Convolutional Neural Networks, Recurrent Neural Networks, and Transformer models can investigate imaging data and distinguish the features of early stage, progression, and survive rate, so as to assist with medical treatment planning. The general strength of AI to analyses and manage large amounts of information provides new chances for enhancing clinical practice and investigation in Alzheimer's disease.

关键词： Proteins Recurrent neural networks Prevention and mitigation Machine learning Transformers data models Planning Alzheimer's disease Prognostics and health management Positron emission tomography

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Lighting the Smart Way: A Technological Odyssey in Urban Street Illumination 10

Lighting the Smart Way: A Technological Odyssey in Urban Str...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Vinu, M.S. Priya, P Kanaga Thilakaveni, P. Hermina, J. M. Ashforn Prashanthini, K. Misba, M. Nehru Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Psg Polytechnic College Department of Computer Engineering Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Erode India Sri Ramakrishna Engineering College Department of Robotics and Automation Coimbatore India Bethlahem Institute of Engineering Department of Computer Science and Engineering Nagercoil Karungal India

ISBN: (纸本)9798350384369

The development of intelligent street light systems has ushered in a new era of efficiency and sustainability in urban infrastructure. The proposed work studies the integration of modern sensors and Internet of Things (IoT) technologies to increase the functionality of street lights. The major components include Light Dependent Resistors (LDR) for automatic light management, an MQ135 sensor for air quality monitoring, and a fog detection system employing a laser module and LDR. These sensors are integrated with the ESP8266, providing cloud integration for real-time data visualization and monitoring. Additionally, solar panels are employed to enhance energy efficiency. The proposed methodology digs into the precise implementation, data analysis, and the potential of this system to alter urban lighting infrastructure © 2024 IEEE.

关键词： Air quality

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Enhancing resilience in complex energy systems through real-time anomaly detection: a systematic literature review

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Energy Informatics 2024年第1期7卷 96页

作者： Aghazadeh Ardebili, Ali Hasidi, Oussama Bendaouia, Ahmed Khalil, Adem Khalil, Sabri Luceri, Dalila Longo, Antonella Abdelwahed, El Hassan Qassimi, Sara Ficarella, Antonio Department of Engineering for Innovation University of Salento Lecce Italy Bologna Italy Computer Science Department Faculty of Sciences Semlalia Cadi Ayyad University Marrakesh Morocco Computer Science Department Faculty of Science and Technology Cadi Ayyad University Marrakesh Morocco Department of Research and Development HSPI SpA-Roma Rome Italy Department of Embedded Systems and Artificial Intelligence MAScIR UM6P Rabat Morocco

As real-time data sources expand, the need for detecting anomalies in streaming data becomes increasingly critical for cutting edge data-driven applications. Real-time anomaly detection faces various challenges, requiring automated systems that adapt continuously to evolving data patterns due to the impracticality of human intervention. This study focuses on energy systems (ES), critical infrastructures vulnerable to disruptions from natural disasters, cyber attacks, equipment failures, or human errors, leading to power outages, financial losses, and risks to other sectors. Early anomaly detection ensures energy supply continuity, minimizing disruption impacts, an enhancing system resilience against cyber threats. A systematic literature review (SLR) is conducted to answer 5 essential research questions in anomaly detection due to the lack of standardized knowledge and the rapid evolution of emerging technologies replacing conventional methods. A detailed review of selected literature, extracting insights and synthesizing results has been conducted in order to explore anomaly types that can be detected using Machine Learning algorithms in the scope of Energy Systems, the factors influencing this detection success, the deployment algorithms and security measurement to take in to consideration. This paper provides a comprehensive review and listing of advanced machine learning models, methods to enhance detection performance, methodologies, tools, and enabling technologies for real-time implementation. Furthermore, the study outlines future research directions to improve anomaly detection in smart energy systems. © The Author(s) 2024.

关键词： Cyber attacks

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VigilantAI: Real-time detection of anomalous activity from a video stream using deep learning

VigilantAI: Real-time detection of anomalous activity from a...

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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Javed, Danish Arshad, Usama Peerzada, Shuhrabeel Saud, Muhammad Ramiz Ali, Nisar Ali, Raja Hashim Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi23460 Pakistan Department of Business University of Europe for Applied Sciences Potsdam14469 Germany Faculty of Engineering and Applied Science University of Regina ReginaS4S0A2 Canada

ISBN: (纸本)9798331504465

In an era where artificial intelligence (AI) solutions are increasingly integrated into various sectors, we have utilized artificial intelligence for enhancing public safety through realtime detection of illegal activities such as robberies and threats at gunpoint using CCTV footage. With the proliferation of deep learning in object detection, the study focuses on deploying the YoloV5 model, trained on a custom dataset compiled from diverse CCTV sources and movies, to identify specific criminal actions. One of the major problems faced in this field is the availability of a large robust labeled dataset on which a deep learning model can be trained. For this purpose, we have created our own dataset by converting various CCTV footage and movies into images, and then labeling them with the correct class. In addition, we also augmented data by using various data augmentation techniques for the chosen images. This dataset, enriched through augmentation techniques and annotated with bounding boxes, allows for the precise detection of threats, achieving an accuracy rate of 85%. Our system stands out by not only spotting these activities but also by instantly alerting security personnel, facilitating a rapid response to potentially dangerous situations. This capability is important for law enforcement agencies worldwide, offering them an advanced tool to act swiftly and prevent crimes, thereby enhancing public security. The essence of our work demonstrates the practical application and significant impact of AI in bolstering security measures, providing a solid foundation for future enhancements in the field. Through this initiative, we aim to foster a safer environment in public spaces, reducing crime rates and increasing the general public’s sense of safety. © 2024 IEEE.

关键词： Crime

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A Cognitive Architecture Based Conversation Agent technology for Secure Communication 1

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1st International Conference on Intelligent Computing for Sustainable Development, ICICSD 2023

作者： Preety Singh, Jagjit Yadav, Meenakshi Christ University Delhi NCR Ghaziabad India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Galgotias College of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (数字)9783031612985

ISBN: (纸本)9783031612978

This paper outlines a multi-agent system-based approach to provider selection. Suppliers in the supply chain are different and the demand and supply levels are high. Buy agents will find the right supply agent in our approach. First, the multi-layer classification system is used to rationally arrange and overall selection on suppliers and buyers. Secondly, the purchase information is organized by the supplier agent to improve device performance. The assessment process is then used to select the suppliers initially. In addition to selecting the correct provider and maximizing the value of the purchaser, the time negotiating mechanism is implemented. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Multi agent systems

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Enhancing Prostate Cancer Prediction: A Fusion of HAAR, LBP, and SIFT Features with CNN

Enhancing Prostate Cancer Prediction: A Fusion of HAAR, LBP,...

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2023 International Conference on data science, Agents and artificial intelligence, ICDSAAI 2023

作者： Harshanya, M. Priyanka, S.A. Shanmugasundari, S. Balasaravanan, P. Chennai Institute of Technology Department of Electronics and Communication Engineering Chennai600069 India Chennai Institute of Technology Department of Biomedical Engineering Chennai600069 India Chennai Institute of Technology Department of Artificial Intelligence and Data Science Chennai600069 India

ISBN: (纸本)9798350348910

The suggested deep learning model is specifically developed to analyze medical images, including Magnetic Resonance Imaging (MRI) scans and histopathology slides, to detect patterns and characteristics that suggest the presence of prostate cancer. The dataset utilized for training and evaluation comprises a heterogeneous collection of prostate pictures, encompassing both malignant and benign instances. The study incorporates pre-processing techniques to improve the quality of the deep learning model. Transfer learning methods can utilize pre-trained convolutional neural network (CNN) models on extensive datasets, which helps enhance the ability to predict prostate cancer with better accuracy. The model's effectiveness is assessed using performance assessment criteria such as sensitivity, specificity, and accuracy, which are measured. The proposed technique is validated by comparing the results with existing methodologies and benchmarks in the field. The research seeks to augment the existing corpus of literature on employing deep learning methodologies for cancer prognosis and aspires to furnish a dependable instrument for timely detection of prostate cancer. The suggested CNN model has the potential to be incorporated into clinical workflows, aiding healthcare workers in making well-informed decisions. © 2023 IEEE.

关键词： Magnetic resonance imaging

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CITE: Compact Interactive TransformEr for Multilingual Image Captioning 23

CITE: Compact Interactive TransformEr for Multilingual Image...

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6th International Conference on Image and Graphics Processing, ICIGP 2023

作者： Xu, Yueyuan Hu, Zhenzhen Zhou, Yuanen Hao, Shijie Hong, Richang Key Laboratory of Knowledge Engineering with Big Data School of Computer and Information Hefei University of Technology China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (纸本)9781450398572

Current state-of-the-art image captioning models generate captions in a single language, requiring a combination of multiple language specific models to build a multilingual image captioning system. However, as the number of supported languages increases, it leads to the parameters of the multilingual image captioning system grow linearly. To tackle this issue, we propose a single Compact Interactive TransformEr (CITE) model, which can describe an image in multiple languages simultaneously, making the captioning system more compact. Specifically, based on the standard Transformer model, we share the encoder and decoder backbone parameters and replace the self-attention sub-layer in the decoder with the interactive attention sub-layer. In addition, we extend the traditional monolingual reinforcement learning mechanism to a multilingual version to promote better description generation. Due to the wide use of Chinese and English, we evaluate the performance of our CITE model by simultaneously generating English and Chinese captions. We expand the image captions of the whole MSCOCO dataset and release a COCO-EN-CN dataset. Extensive experiments on the COCO-EN-CN dataset show that our single CITE model with more parameter-efficient can maintain the competitive performance or even better than the monolingual captioning models. © 2023 ACM.

关键词： Reinforcement learning

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Three-Dimensional Convolutional Neural Network for Wheat Rust Disease Classification 2

Three-Dimensional Convolutional Neural Network for Wheat Rus...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Jagadeeshan, N. Nagendar, Y. Swarnalatha, K. Al-Attabi, Kassem Ramachandra, A.C. Electronics and Communication Engineering Department Telangana Hyderabad India School of Computer Science and Engineering Sr University Telangana Warangal India Maharaja Institute of Technology Department of Artificial Intelligence and Data Science Thandavapura India The Islamic University Najaf Iraq Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India

ISBN: (纸本)9798350382952

In worldwide, the wheat is a significant crop which generates main source of food for numerous peoples. Through, the development of crop productions is vulnerable through various diseases such as bacterial, viral and fungal infections. These type of disease can cause important damage in crops which leads to diminish the yield production and grain quality. This paper proposed a Three-Dimensional Convolutional Neural Network (3D-CNN) for wheat rust disease classification that learns to recognize the pattern and structure of rust disease using convolution filter layers. The CGIAR dataset is used which contains 1486 images and it is pre-processed by gaussian filter which reduces the noise and smoothens the image. Then, the Discrete Wavelet Transform (DWT) is used for feature extraction which works in discrete timespan that outputs in low computational cost. Then, 3D-CNN is used for the classification of wheat rust disease. The performance of 3D-CNN is estimated by accuracy, recall, f1score and precision. The 3D-CNN attains accuracy 99.83%, recall 98.89%, f1score 98.81 % and precision 98.79% when compared to existing techniques like GNet+FERSPNET-50 and Few-shot learning based EfficientNet. © 2024 IEEE.

关键词： Discrete wavelet transforms

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SIGN-UNITE ASL Detection using ML for Deaf-Mute People Communication 5

SIGN-UNITE ASL Detection using ML for Deaf-Mute People Commu...

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5th IEEE International Conference on Advances in science and technology, ICAST 2022

作者： Kale, Pranali Nemade, Milind Karia, Aditya Majalkar, Vedshri K.J. Somaiya Institute of Engineering and Information Technology Department of Artificial Intelligence and Data Science Sion Mumbai India

ISBN: (纸本)9781665492638

There have been several advancements in technology and a significant amount of research has been done to help the people who are deaf and dumb. Aiding the cause, Deep learning, and computer vision has been used for detection of ASL to make an impact on this cause. We used linear SVM Algorithm and MediaPipe Libraries in our system that translates gestures like alphabets which is available for total 16 datasets A-K and U, V, W, Y with 99.6 to 99 percent accuracy then numbers from 1-10 with 99.83 percent accuracy and then 5 sentences with 99.6 percent accuracy. We attained overall 99.6 percent accuracy for detection of alphabets, numbers and words. © 2022 IEEE.

关键词： Libraries

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Deep Learning Approaches in Geomagnetic Storm Forecasting: A Comprehensive Survey and Future Prospects 2

Deep Learning Approaches in Geomagnetic Storm Forecasting: A...

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2nd International engineering Conference on Electrical, Energy, and artificial intelligence, EICEEAI 2023

作者： Khaled, Omar Salah, Shaimaa Attar, Hani Hafez, Mohamed Mohanad, A. Deif Elgohary, Rania Elsayed, Asmaa Misr University for Science and Technology Department of Artificial Intelligence Cairo Egypt Zarqa University Faculty of Engineering Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Feqs INTI-IU-University Faculty of Engineering Nilai Malaysia

ISBN: (纸本)9798350373363

Geomagnetic storms significantly threaten modern technological infrastructure and communication systems, highlighting the critical need for accurate and timely forecasting. This survey research paper delves into the dynamic intersection of geomagnetic storm prediction and deep learning methodologies. This study provides a comprehensive overview of the current landscape in this field through a meticulous analysis of a diverse array of works, ranging from convolutional neural networks (CNNs) to recurrent neural networks (RNNs). The research scrutinizes the strengths and limitations of various deep learning approaches, shedding light on their efficacy in capturing the complex temporal and spatial patterns inherent in geomagnetic storm data. Notably, CNNs demonstrate promise in extracting spatial features, while RNNs excel at capturing temporal dependencies. Additionally, hybrid architectures combining these techniques exhibit a potential synergy in bolstering prediction accuracy. Furthermore, this paper underscores the pivotal role of data quality and diversity in training robust models. The availability of large-scale, multi-modal datasets is critical in enhancing predictive capabilities. Model interpretability and the ability to handle extreme events are pressing challenges warranting further investigation. The findings of this study hold far-reaching implications for space weather preparedness. Through the leverage of advanced machine learning techniques, our capacity to forecast geomagnetic storms is poised to be revolutionized, providing a crucial line of defense against their potentially devastating impacts on terrestrial technologies. The groundwork for future research endeavors is laid by this work, advocating for interdisciplinary collaboration between space scientists and machine learning experts. By addressing the identified challenges, a path toward more reliable and timely geomagnetic storm forecasts is endeavored to be forged, ultimately safeguarding our

关键词： Recurrent neural networks

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