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science, Technology, engineering and Management (ICSTEM), International Conference on

作者： Nagendar Yamsani K. Muthukumaran B Santhosh Kumar Asha V Navdeep Singh JoshuvaArockia Dhanraj School of Computer Science and Artificial Intelligence SR UNIVERSITY Warangal Department of Biomedical Engineering DSCET-Anna University Chennai Institute of Aeronautical Engineering Hyderabad Department of Computer Applications New Horizon College of Engineering Bangalore Lovely Professional University Phagwara Department of Computer Science and Engineering (AI&ML) School of Engineering Dayananda Sagar University Bengaluru Karnataka India

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

This article provides a description of an Internet of Things (IoT) system that employs machine learning algorithms to provide real-time and precise insights into the health and behaviour of animals. A network of Internet of Things (IoT) sensors strategically placed throughout the cattle area of the proposed system is capable of monitoring a variety of attributes, including temperature, humidity, grazing patterns, and movements. The information gathered by these sensors is wirelessly transmitted to a central centre, where it is analysed by algorithms that acquire expertise through practice. To extract meaningful results from acquired data, supervised and unsupervised machine learning algorithms are implemented. The system maintains real-time surveillance of farmers or administrators and promptly alerts them to any anomalous events that may occur in regards to the animals' welfare or behaviour. This empowers them to expeditiously implement preventative measures against infections or optimise feeding regimens. Investigating historical data can yield significant insights that facilitate well-informed decision-making concerning enduring concerns like resource allocation or breeding initiatives. The incorporation of machine learning technology and the internet of things (IoT) into livestock management practices increases overall agricultural efficiency, productivity, and animal welfare.

关键词： Temperature sensors Temperature measurement Machine learning algorithms Animals Surveillance Sensor phenomena and characterization Real-time systems

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A Hybrid Deep Learning Framework for Efficient Network Intrusion Detection Systems using Data Augmentation

A Hybrid Deep Learning Framework for Efficient Network Intru...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Priya Shirley Muller K Madhavi Naga Durga Saile K B Prasanthi B Jayaram Goski Sathish Department of Mathematics Sathyabama Institute of Science and Technology Chennai India Department of Mathematics Mallareddy Engineering College (Autonomous) Hyderabad India Department of Computer Science and Engineering (AIML& IoT) VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India Departmentt of Computer Science (AI&ML) CVR College of Engineering Hyderabad India Department of Computer Science and Engineering RMK Engineering College Gummidipoondi India Department of Information Technology St.Martins Engineering College (Autonomous) Hyderabad India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Because of their critical position as the initial barrier against harmful actions, intrusion detection systems (IDSs) rely substantially on the efficacy of cybersecurity in protecting corporate communication. There are several limits to machine learning approaches that be solved by utilizing other deep learning architectures, even though they are widely used for intrusion detection. In addition, unbalanced datasets frequently make it difficult to evaluate the suggested models, which prevents a thorough evaluation of the models' effectiveness. This study seeks to tackle these difficulties by improving the performance of numerous deep-learning(DL) structures for IDS using data augmentation approaches. The experimental results shown that even a basic CNN-based architecture may produce very efficient network-attack-detection, but more complicated designs only managed to show slight performance increases. The results show that cybersecurity frameworks can easily incorporate the suggested deep learning-based intrusion detection approaches, which improves the capacity to identify and counteract complex network threats. The outcomes of this research exhibit that the accuracy of intrusion detection models is highly dependent on the dataset's quality and quantity, with augmented CIC-IDS-2017 showing an accuracy of up to 92%.

关键词： Deep learning Accuracy Communication systems Network intrusion detection Complex networks Data augmentation Integrated circuit modeling computer crime

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Artificial Intelligence-Driven Power Management System for Enhanced Efficiency in Smart Grids

Artificial Intelligence-Driven Power Management System for E...

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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Deepak, B.L. Sabin, T.T. Darshana, A. Vindya, L. Suma Manjunath, N. Mallikarjunaswamy, S. Shilpa, M. Rekha, V. Sjc Institute of Technology Dept of Information Science and Engineering Chickballapur India Sjc Institute of Technology Dept of Ai & Ds Chickballapur India Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India Bengaluru Institute of Technology Dept of Information Science and Engineering Bengaluru India Christ University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350388602

Smart grids are crucial in modern energy systems, integrating sensors, communication networks, and control mechanisms to improve energy distribution. However, conventional methods like Time-Based Scheduling (TBS), Demand Response Mechanism (DRM), and Static Load Distribution (SLD) struggle with inefficiencies such as poor real-time load balancing, limited adaptability, and inaccurate power usage predictions. These drawbacks hinder the overall effectiveness of smart grid *** address these challenges, the proposed ai-Driven Power Management System (ai-PMS) leverages machine learning to optimize energy distribution and predict demand more accurately. By using historical and real-time data, ai-PMS dynamically adjusts power flow, ensuring better load distribution and efficiency. This method surpasses the limitations of TBS, DRM, and SLD. The implementation of ai-PMS has resulted in a 0.25% improvement in energy efficiency and a 0.15% boost in grid reliability, significantly reducing power losses. The system also enhances the integration of renewable energy sources, making it a more sustainable and adaptive solution for modern smart grids. These advancements promise better power management and lower operational costs for grid operators. © 2024 IEEE.

关键词： Smart power grids

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The Main Threat to computer Network Security in Smart Cities 3

The Main Threat to Computer Network Security in Smart Cities

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Salama, Ramiz Al-Turjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Vikash, Om Yadav, Satya Prakash Vats, Satvik Ai and Robotics Institute Research Center for Ai and IoT Near East University Department of Computer Engineering Near East Boulevard TRNC Mersin 10 Nicosia99138 Turkey Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept Mersin10 Nicosia Turkey Waterloo University Department of Chemical Engineering ONN2L 3G1 Canada Friedrich- Alexander University Clean Energy Processing Department Chemical and Biological Engineerin Erlangen Germany Department of Computer Science and Engineering Greater Noida201306 India Graphic Era Hill University Dehradun Department of Computer Science and Engineering India

ISBN: (纸本)9798350330724

As the concept of smart cities continues to gain momentum, the integration of various technologies has become essential for creating sustainable and efficient urban environments. However, with the proliferation of interconnected devices and systems, the risk of network security threats has increased significantly. This research article aims to identify and analyze the main threats to network security in smart cities. By exploring the potential vulnerabilities and challenges posed by the interconnected infrastructure, this study provides insights into the key areas that require attention to ensure the resilience and protection of smart cities. The findings of this research emphasize the need for robust security measures and proactive strategies to mitigate the identified threats. © 2023 IEEE.

关键词： Smart city

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Endoscopic Image Processing: Evaluating the Robustness of Deep Neural Networks 5

Endoscopic Image Processing: Evaluating the Robustness of De...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Gireesh Babu, C.N. Babu, Kaparapu Satish Suneel, Sajja Rao, K.Srinivasa Muralitharan, Divya Aarthi, R. Bms Institute of Technology and Management Dept.of CSE/ISE Karnataka Bengaluru India Geetanjali College of Engineering and Technology Department of Electronics and Communication Engineering Telangana Hyderabad India Hyderabad Telangana Dundigal500043 India K.S.R.M. College of Engineering Department of Ai&ml Andhra Pradesh Kadapa516003 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India R.M.D Engineering College Department of Ece R. S. M Nagar Gummidipoondi601206 India

ISBN: (纸本)9798331504403

The Hybrid CNN-Transformer Model (HCNNT) is a novel model which exploits Convolutional Neural Networks (CNN) for spatial feature extraction and Transformers for capturing contextual and long range dependencies in order to carry complex image classification. In order to evaluate the algorithm proposed, a new model has been introduced and trained on a data set of gastrointestinal (GI) image classification, which is derived from a balanced data set with 226 images available on Kaggle. Although the cascaded CNN part is to learn elaborate visual characteristics, the improved understanding of complex patterns and relationships is achieved by the Transformer component to process these features. When both are integrated, we gain a powerful system that has high accuracy in distinguishing GI bleeding condition from normal condition. Extensive training and optimization of the hybrid model using binary cross-entropy loss and Adam optimizer has achieved state-of-the-art performance. Through the experiments evaluated on the HCNNT model, it is evident that an accuracy of 97.34% proves to be significantly beneficial, its practicality in terms of reliability for medical image classification that essentially improves the accuracy of diagnosis in gastrointestinal healthcare. © 2024 IEEE.

关键词： Convolutional neural networks

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COMPASS: SRAM-Based Computing-in-Memory SNN Accelerator with Adaptive Spike Speculation 57

COMPASS: SRAM-Based Computing-in-Memory SNN Accelerator with...

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57th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2024

作者： Wang, Zongwu Liu, Fangxin Yang, Ning Huang, Shiyuan Li, Haomin Jiang, Li Shanghai Jiao Tong University Department of Computer Science and Engineering China Shanghai Qi Zhi Institute China AI Institute Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence China

ISBN: (纸本)9798350350579

Brain-inspired spiking neural networks (SNNs) are considered energy-efficient alternatives to conventional deep neural networks (DNNs). By adopting event-driven information processing, SNNs can significantly reduce the computational demands associated with DNNs, while still achieving comparable performance. However, current SNNs primarily prioritize high accuracy by constructing complex neuron models that generate sparse spikes. Unfortunately, this approach results in low energy efficiency and high latency, posing a significant challenge for deploying SNNs at the edge. Furthermore, the dominant computation in SNNs, which involves spike-wise Accumulate-Compare operations, is well-suited for Computing-in-Memory (CIM) architectures. However, exploiting high parallel processing and spike sparsity in CIM-based SNN accelerators is challenging due to the irregularity and time dependency of spikes. To address these limitations, the paper proposes COMPASS, a SRAM-based CIM architecture for efficient SNNs. We first introduce an efficient method to exploit irregular sparsity for both input spikes (explicit) and output spikes (implicit). This is achieved through a speculation mechanism that exploit dynamic spike patterns, enabling lean hardware for sparsity utilization. Additionally, the CIM architecture is carefully modified to facilitate dynamic spike pattern generation and exploitation with minimal overhead. Moreover, we design an adaptive dataflow with temporal spike representation tailored for input/output spikes, reducing memory footprint and enabling parallel execution. Comprehensive evaluation results demonstrate that COMPASS can achieve 26.7x end-to-end speedup over recent SNN accelerators hardware implementation with up to 386.7x less energy per inference. © 2024 IEEE.

关键词： Compasses (magnetic)

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Robotic Perception of Transparent Objects: A Review

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2547-2567页

作者： Jiang, Jiaqi Cao, Guanqun Deng, Jiankang Do, Thanh-Toan Luo, Shan King's College London Department of Engineering LondonWC2R 2LS United Kingdom University of Liverpool SmARTLab Department of Computer Science LiverpoolL69 3BX United Kingdom Imperial College London LondonSW7 2AZ United Kingdom Monash University Department of Data Science and AI ClaytonVIC3800 Australia

Transparent object perception is a rapidly developing research problem in artificial intelligence. The ability to perceive transparent objects enables robots to achieve higher levels of autonomy, unlocking new applications in various industries, such as healthcare, services, and manufacturing. Despite numerous datasets and perception methods being proposed in recent years, there is still a lack of in-depth understanding of these methods and the challenges in this field. To address this gap, this article provides a comprehensive survey of the platforms and recent advances for robotic perception of transparent objects. We highlight the main challenges and propose future directions of various transparent object perception tasks, i.e., segmentation, reconstruction, and pose estimation. We also discuss the limitations of existing datasets in diversity and complexity, and the benefits of employing multimodal sensors, such as RGB-D cameras, thermal cameras, and polarized imaging, for transparent object perception. Furthermore, we identify perception challenges in complex and dynamic environments, as well as for objects with changeable geometries. Finally, we provide an interactive online platform to navigate each reference. © 2020 IEEE.

关键词： Cameras

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Fog enabling technologies in healthcare: A review

Fog enabling technologies in healthcare: A review

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Yadav, Aditya Pradhan, Onesimus Chandra Khanam, Ruqaiya Amrita Computer Science and Engineering Sharda School of Engineering and Technology Sharda University U.P Greater Noida India Department of EECE Center for AI in Medicine Imaging and Forensic Sharda University U.P Greater Noida India Center for Cyber Security and Cryptology Computer Science and Engineering Sharda School of Engineering and Technology Sharda University U.P Greater Noida India

ISBN: (纸本)9781032669663

This paper provides an overview of fog computing healthcare technologies that are playing a huge role in the current healthcare Industry. In this paper, we suggest the Data mining is the most effective option for disease prediction. The suggested framework intends to support health care based on disease symptoms, and health-related data. Gathering and analyzing activity-specific data that may be utilized to gauge the health benefits of exercise and its intensity as well as provide suggestions. Health Informatics makes extensive use of wireless sensor networks (WSNs). Patients at risk for chronic illnesses are increasingly being monitored via wireless and wearable sensors. This ensures that patients follow their treatment schedules and protects them from unexpected assaults. Along with its abilities, the fog computing framework also brings over from the cloud computing platform a number of security flaws that must be solved for the benefit of the user. Fog computing is crucial because it aids in the system’s elimination of latency problems, which are crucial in the healthcare sector. © 2024 The Author(s).

关键词： Fog computing

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Slotted ALOHA with Code Combining for IoT Networks 3

Slotted ALOHA with Code Combining for IoT Networks

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3rd IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2023

作者： Tyrovolas, Dimitrios Chrysologou, Athanasios Chondrogiannis, Georgios Tegos, Sotiris Mekikis, Prodromos-Vasileios Diamantoulakis, Panagiotis Ioannidis, Sotiris Liaskos, Christos Chatzidiamantis, Nestor Karagiannidis, George Aristotle University of Thessaloniki Dept. of Electrical and Computer Engineering Thessaloniki Greece Technical University of Chania Dept. of Electrical and Computer Engineering Chania Greece University of Ioannina Computer Science Engineering Department Ioannina Greece Cyber Security Systems and Applied Ai Research Center Lebanon

ISBN: (纸本)9798350333732

Internet of Things (IoT) networks are expected to be a key enabler technology for smart cities, due to their ability to offer real-time monitoring. In this work, we propose a novel medium access control (MAC) protocol, inspired by the synergy of slotted ALOHA and hybrid automatic repeat request with code combining (HARQ-CC), termed as code combining slotted ALOHA (CCSA), to improve the performance of IoT networks with multiple randomly deployed sensors. Specifically, we provide analytical closed-form expressions for the network's outage probability as well as for its average throughput when CCSA is applied. Finally, we provide simulation results that reveal the authenticity of the derived expressions and reveal the capabilities of HARQ-CC to significantly improve the network's performance when the sensors' access is based on a random access scheme. © 2023 IEEE.

关键词： Wireless sensor networks

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Titanic Survivors Prediction Analysis Using Machine Learning Algorithms

Titanic Survivors Prediction Analysis Using Machine Learning...

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Computational Intelligence for Security, Communication and Sustainable Development (CISCSD), International Conference on

作者： Shridhar B. Devamane Abhijith B H Bhushan Purushotham Ravindranath R. C Lakshmikantha G. C Udayaprasad P K Department of Computer Science and Engineering Global Academy of Technology Bangalore India Department of Artificial Intelligence and Machine Learning Global Academy of Technology Bangalore India Department of AI & ML Global Academy of Technology Bangalore India

ISBN: (数字)9798350365405

ISBN: (纸本)9798350365412

Machine learning algorithms are computational models that allow computers to acquire knowledge and improve performance on a task by automatically learning patterns and rules from input data provided by the user. In this work we are predicting the Titanic disaster survivors based on a particular dataset. Based on characteristics such as age, sex, ticket class, etc., we applied the Support Vector Machine, K-Nearest Neighbors, Decision Tree, and Random Forest algorithm to identify which passenger would survive.

关键词： Support vector machines Machine learning algorithms Accuracy Disasters Nearest neighbor methods Prediction algorithms Data models Vectors Sustainable development Random forests

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