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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Chauhan, Shweta Kumar, Rahul Kumar, Bibek Madhav Institute of Science and Technology Department of Artificial Intelligence and Machine Learning Madhya Pradesh Gwalior474005 India GLA University ECE Department Uttar Pradesh Mathura281406 India Symbiosis University of Applied Sciences School of Data Science Madhya Pradesh Indore453112 India

ISBN: (纸本)9798350385199

Automatic age and gender classification has become important researcher since the rise of social platforms and social media. The main advancements in face recognition are the ability to determine a person's age and gender. This paper makes a significant contribution by providing a survey of different face recognition methods for determining age and gender. A deep neural network (DNN) known as a convolutional neural network is frequently used for natural language processing and image recognition and processing. The proposed method significantly enhances three elements: face alignment, face recognition, and face recognition accuracy. All weight vectors of people in the same age group within a given database are averaged together. To predict gender and age, picture classification and regression models have been discussed in this paper respectively. Although training the proposed network to make accurate predictions takes a lot of time, it significantly improves age and gender categorization precision. From the experimental results we compared and analyzed the work to the most recent UTK-Face benchmark for estimating age and gender. © 2024 IEEE.

关键词： Convolutional neural networks

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Using Generative Models to Improve Fire Detection Efficiency

Using Generative Models to Improve Fire Detection Efficiency

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Information technology and Nanotechnology (ITNT), International Conference on

作者： Nikita Andriyanov Alexandr Kim Xenin Fao Data Analysis and Machine Learning Department Financial University under the Government of the Russian Federation Moscow Russia Data Science Department Huazhong University of Science and Technology Huazhong China

ISBN: (数字)9798350372939

ISBN: (纸本)9798350372946

The paper discusses generative artificial intelligence technologies used to improve the efficiency of fire detection in satellite images. Different detector architectures are proposed and compared in terms of accuracy. It is found that the use of generative models to expand the training sample is on average 1–2% more effective than the use of standard augmentations. The combined method of expanding the training image base can improve the accuracy by 4-5%. In addition, the right approach in generative prompts can also improve the accuracy metrics of the models.

关键词： Training Measurement YOLO Accuracy Image databases Detectors Satellite images

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Comprehensive Analysis on Anomaly Detection in Cybersecurity dataset using Deep learning Algorithms

Comprehensive Analysis on Anomaly Detection in Cybersecurity...

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2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Nidhishree, G. Vidyalakshmi, K. Vidyavardhaka College of Engineering Department of Computer Science and Engineering (Artificial Intelligence & Machine Learning) Mysuru India Ssahe University Sri Siddhartha Institute of Technology Department of Computer Science and Engineering (Data Science) Tumakuru India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Cybersecurity has become very important due the rapid growth of digital technologies. It plays an important role in protecting computer systems and networks from potential attacks. Cybersecurity involves a set of technologies and algorithms that sometimes bring in threats to the security of computer networks, the end system, attacks on data, inappropriate access, changes and harm. Cyber-defense mechanisms involved at the host, application, network and data level require a constantly improved process to secure the system from any feasible attack, targeting data security and privacy. The network anomaly detection is challenging because of the complex network topologies and high dimensional data, leading to unbalanced performance, high false positive rates and difficulty of handling time-series data. Deep learning (DL) methods appear as an essential element in network Intrusion Detection Systems (NIDS) to recognize malicious behaviors or anomalies. Anomaly detection and network security are extensively utilized for intrusion detection performed by utilizing Cyber security to reduce malicious behavior in the networks. DL based-based detection utilizes many algorithms, which are capable of capturing complex patterns, effective for high-dimensional data. In DL, the anomaly is solved by two ways: flow-based detection that analyzes the network flows and packet-level data to identify anomalies. © 2024 IEEE.

关键词： anomaly detection cyber security deep learning intrusion detection system privacy and security

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Smart Healthcare: Harnessing the Power of Machine learning for Predictive Analysis 6

Smart Healthcare: Harnessing the Power of Machine Learning f...

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6th Parul International Conference on Engineering and technology, PICET 2024

作者： Barhate, Aditya Kumar, Praveen Verma, Prateek Jikar, Nayan Tale, Abhay Hikre, Vaibhav Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra Wardha India Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra Wardha India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra Wardha India

ISBN: (纸本)9798350369748

Smart healthcare employs cloud computing, the Internet of Things (IoT), and supercomputers to manage the healthcare system. Medical data storage volume is increasing, necessitating the adoption of Machine learning (ML) models for predicting outcomes, particularly in the treatment of chronic illnesses. The trend of care personalization and technological integration is highlighted by the development of ML applications for clinical decision support systems, including real-time predictive analytics. Predictive analytics applications employ ML and Artificial Intelligence (AI) to forecast patient risks, outcomes, and treatment responses. Predictive analytics is more important in healthcare when it comes to detecting Alzheimer's disease, cardiovascular events, radiology, and other conditions. Therefore, the current study highlights the change in illness identification, medical imaging, and personalized medication brought about by ML and Predictive Analytics in Precision Medicine (PM). However, ethical concerns and the deployment of transparent AI models still need more research. This study anticipates a time when healthcare will be quicker, better informed, and more patient-centered by using the insights provided by intelligent healthcare data. By effectively saving patient lives, the use of ML in healthcare ushers in a new era for the industry. © 2024 IEEE.

关键词： Disease Detection Machine learning Medical Imaging Predictive Analytics Smart Healthcare

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Meta Heuristic Image Optimisation in LiDAR Sensor Based on Cloud IoT Network and Deep learning Algorithms

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Remote Sensing in Earth Systems Sciences 2025年第2期8卷 566-575页

作者： Meenakshi, K. Manoj, Gunna Diwakar, Meenakshi Prabakaran, Manjula Sravan, Padala Babu, Gaddam Ravindra Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Cyber Security Marri Laxman Reddy Institute of Technology and Management Telangana Hyderabad India Department of IT Panimalar Engineering College Chennai India Dept of CSE (Data Science) Madanapalle Institute of Technology & Science Andhra Pradesh Angallu India Computer Science and Engineering NIIT with learning partner Hainan Normal University Haikou China Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Telangana Hyderabad India

LiDAR technology has become a vital tool for researching these atmospheric components because of its capacity to measure backscattered signals and investigate the atmosphere at extremely high spatial and temporal resolution. However, machine learning has become a potent technique for sophisticated data processing and analysis because of the complexity and diversity of lidar technology. The aim of this research is to propose a novel technique in cloud IoT network-based LiDAR sensor imaging with image optimization using a metaheuristic deep learning model. Input has been collected LiDAR image using a cloud IoT module and processed for noise removal and normalization. Then this processed image is classified and optimized utilizing a quantile transfer graph convolutional model with binary firefly whale colony optimization. The experimental analysis is carried out in terms of detection accuracy, random precision, recall, RMSE, and AUC for various LiDAR sensor-based datasets. Comparing the results of the various approaches reveals that the suggested approach outperforms them all with a recall of 94%, a random precision of 95%, and a detection accuracy of 98%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Cloud IoT network Convolutional model Image optimization LiDAR sensor Metaheuristic deep learning

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Deep Reinforcement learning In Autonomous Cars To Preventing Road Accident 2

Deep Reinforcement Learning In Autonomous Cars To Preventing...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Thote, Rushikesh Barhate, Aditya Tale, Abhay Verma, Prateek Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India

ISBN: (纸本)9798331528713

The integration of autonomous vehicles (AVs) into modern transportation systems presents both unprecedented opportunities and challenges, particularly in ensuring road safety and reducing accidents. This paper explores the application of Deep Reinforcement learning (DRL) techniques to enhance decision-making processes in AVs, aiming to proactively prevent road accidents. By simulating complex driving environments, DRL algorithms enable AVs to learn optimal driving strategies through continuous interaction and feedback. We introduce a DRL-based framework that incorporates sensor data fusion, real-time environment analysis, and adaptive control mechanisms. Experimental results from simulated urban and highway scenarios demonstrate a significant reduction in collision rates and improved response to unforeseen obstacles compared to traditional rule-based systems. The findings underscore the potential of DRL in fortifying the reliability and safety of autonomous driving technologies. © 2024 IEEE.

关键词： Deep reinforcement learning

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A Review on Web data Mining to Detect Online Spread of Terrorism Activity

A Review on Web Data Mining to Detect Online Spread of Terro...

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

作者： Saurav Kumar Jha Utkarsha Pacharaney Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Wardha Maharashtra India Department of Artificial Intelligence and Machine learning Faculty of Engineering and Technology Wardha Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

This research study outlines the significant contribution that web data mining can make in discovering and combating the dissemination of online terrorism. With terrorist groups using the internet more and more to deliver propaganda, recruit, and organize, there is a pressing need for efficient monitoring and analysis of web content. This article discusses different data mining methods that can be used to identify and study patterns suggestive of terrorist activity throughout networked digital environments. Specifically, the embedding of machine learning algorithms, i.e., Support Vector Machines (SVM), Random Forest and Gradient Boosting, is investigated with regard to its suitability to differentiate between benign and harmful online content. These algorithms can process large volumes of data, where features are extracted, including URL structures, domain ages, and keyword frequencies, to detect anticipate threats. According to the article, feature extraction plays a crucial role in improving detection system accuracy, enabling real-time surveillance of anomalous online activities. In addition, the review discusses the problems that researchers and law enforcement agencies have been encountering in using web data mining for counter-terrorism purposes. Ethical concerns around privacy and civil liberties are addressed and technical challenges, including the quality of data and the dynamic character of online content, are elaborated. Scenarios in which false positives arising in detection systems may occur are also considered, with emphasis on the need for ongoing algorithm and technique refinement.

关键词： Support vector machines Uniform resource locators Machine learning algorithms Reviews Law enforcement Terrorism Feature extraction Real-time systems data mining Internet of Things

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A Review on Web data Mining to Detect Online Spread of Terrorism Activity 2

A Review on Web Data Mining to Detect Online Spread of Terro...

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2nd International Conference on Machine learning and Autonomous Systems, ICMLAS 2025

作者： Jha, Saurav Kumar Pacharaney, Utkarsha Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine learning Maharashtra Wardha India

ISBN: (纸本)9798331505745

关键词： Crop protection

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Smart Finance: An overview of Artificial Intelligence Integration in Fintech 2

Smart Finance: An overview of Artificial Intelligence Integr...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Akre, Parth Dhananjay Pacharaney, Utkarsha Siraskar, Warsha Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Yeshwantrao Chavan College of Engineering Maharashtra Nagpur441110 India

ISBN: (纸本)9798331528713

The role of FinTech in advancing contemporary economies, societies, and technologies is growing. Smart FinTech is the next wave of FinTech that leverages artificial intelligence and data science methodologies to drive major advancements in fields including blockchain, cryptocurrencies, InsurTech, PayTech, BankingTech, TradeTech, LendTech, InsurTech, WealthTech, and RiskTech. An overview of these domains, as well as the artificial intelligence and data science techniques applied, is given in this paper. These techniques include augmentation, optimization, privacy-preserving processing, deep learning, federated learning, and intelligent interactions, complex system methods, mathematical approaches, knowledgeable communication, recognition and responses, and data analytics. This study offers a thorough analysis of smart financial companies, the obstacles they face, the smart FinTech environment, the artificial intelligence and data science methods that make smart FinTech possible, and potential future research areas for the artificial intelligence and data science community. To improve comprehension and analysis, the work makes use of visual aids such as figures and tables. The relevance of data quality, including data augmentation and manipulation, as well as enhancing fairness and decision-support, is emphasized in the study for financial firms. It also covers ethical and explicable FinTech, with a focus on privacy preservation, accountability, openness, and objectivity in FinTech and business designs. The potential for more research and innovation at the heart of analytics, data science, and FinTech is highlighted in the paper's conclusion. © 2024 IEEE.

关键词： Federated learning

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Unsupervised Anomaly Detection on Node Attributed Networks: A Deep learning Approach 21

Unsupervised Anomaly Detection on Node Attributed Networks: ...

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4th International Conference on Information Science and Systems, ICISS 2021

作者： Kavehzadeh, Parsa Samadi, Mohammadreza Amir Haeri, Maryam Computer Engineering Department Amirkabir University of Technology Iran Learning Data-Analytics and Technology Department University of Twente Netherlands

ISBN: (纸本)9781450389136

Anomaly detection has been one of the important issues in social network analysis in recent years due to the crucial role it plays in different applications such as fraud and spammer detection. Using both graph and node characteristics leads to more accurate results in detecting anomalous nodes of node attributed networks. Most of the research works in this field are concentrated on supervised methods for anomaly detection. However, in real-world problems, there is not enough labeled data to use supervised methods for anomaly detection. This paper proposes an unsupervised method for detecting anomalous nodes in node attributed networks. The methods used a two-step deep learning approach. In the first step, structural features of the network are extracted using node2vec;in the next step, Variational AutoEncoder (VAE) is used to detect the anomalies considering both structural and node attributes. The anomalous nodes are recognized by their higher reconstruction loss. Our experimental results on two datasets, BlogCatalog and Flickr, show that the suggested method can compete with the state-of-the-art approaches of anomaly detection in attributed networks. Our method (Deep2NAD) outperforms the state-of-the-art result on the Flickr dataset based on AUC. Moreover, it receives an acceptable AUC over the BlogCatalog dataset in comparison to the state-of-the-art methods. © 2021 ACM.

关键词： Anomaly detection

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