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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Goyal, Dinesh Gandhi, Yatin Dongre, Deepika Shailesh Bhagat, Gorakh P. Pawar, Rohit Department of Computer Engineering Poornima Institute of Engineering and Technology Rajasthan Jaipur India Competent Softwares Maharashtra Pune India Artificial Intelligence and Data Science VIIT Pune India Electronics and Communication Engineering Arya College of Engineering Jaipur India Department of Mechanical Engineering Dr. D. Y. Patil Institute of Technology Pimpri Maharashtra Pune India Shri Ramdeobaba College of Engineering and Management Maharashtra Nagpur India

ISBN: (纸本)9789819601462

Traditional methods of responding to cybersecurity incidents often use set playbooks or routines that don’t change, which might not work well with the constantly changing nature of modern cyber dangers. The idea behind this paper is to create and use decision systems that are specifically designed for adapting how to handle hacking incidents. Advanced artificial intelligence (AI) methods, such as machine learning and natural language processing, are used by these decision systems to analyze security events in real time and help people make quick, well-informed decisions. These systems improve the speed and efficiency of hacking reaction by constantly learning from past events and responding to new threats. Automatic danger recognition, risk assessment, and reaction planning are important parts of the suggested decision systems. Automated systems that look for threats use AI algorithms to look for signs of bad behavior in network traffic, system logs, and other relevant data sources. The risk assessment tools look at the dangers and how bad they could be, considering things like how sensitive the data is, how important the system is, and the rules that need to be followed. Decision systems actively plan responses based on danger recognition and risk assessment results. These responses may include separating affected systems, blocking suspicious IP addresses, or sending events to human experts for further study. Over time, these systems get better at making decisions by using adaptive learning methods and constant feedback loops. This makes it easier for them to spot and stop new cyber threats. The creating and using decision systems for adaptive cybersecurity incident reaction is a big step toward making organizations’ defenses more resilient and effective in the face of changing cyber dangers. By using AI and machine learning, these systems stop threats before they happen, giving cybersecurity teams the tools they need to stay ahead of attackers in the ongoing battl

关键词： Cyber attacks

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Modelling a Multi-Perceptron Q Network Layer for Analyzing Patient Condition in Healthcare Sector

Modelling a Multi-Perceptron Q Network Layer for Analyzing P...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Karthi M Manirajan S P Prince A R. Santhosh Department of Artificial Intelligence and Data Science Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Chronic diseases like chronic respiratory diseases, diabetes, heart disease (HD) and cancer are the important causes of mortality globally. The diagnosis of Heart-related diseases with a variety of symptoms or characteristics is incredibly complex. Internet of Things (loT) embedded solution has improved with the increase in smart wearable devices. However, people who undergo heart attacks unexpectedly have poor survival rates. As a result, an Internet of Things based Multi-Perceptron Q-Network (MQN)-based monitoring program is suggested for cardiac patients to assist in identifying heart disease and administering medication appropriately. The technology based on MQN is used to securely encrypt this sensor data before sending it to the cloud. The encrypted and decrypted data is eventually done and MQN classifier is used to complete the classification process. The two categories of data that make up the classified outcomes are i) regular and ii) irregular. It indicates the individual's cardiac status, and if the results are peculiar, the doctor is notified via text so that the patient can receive treatment. When compared to current algorithms, the MQN for HD diagnosis exhibits better enhancement in the estimated investigational results. Furthermore, when contrasted against the current MQN, the introduced Multi-layered Perceptron (MLP) system facilitates safe data transmission yields the maximum security level of 96%, and is accomplished in the shortest amount of time for both decryption and encryption.

关键词： Heart Accuracy Sensitivity Pulmonary diseases Mortality Medical services Encryption Internet of Things Wearable devices Diseases

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Supervised Machine Learning Method for Bone Cancer Detection and Classification 3rd

Supervised Machine Learning Method for Bone Cancer Detection...

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3rd International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Lingappa, Ediga Sriramya, P. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai600056 India Malla Reddy College of Engineering TS Dhulapally Secunderabad500100 India Department of Artificial Intelligence and Data Science Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai600056 India

ISBN: (纸本)9789819783540

Bone cancer kills most people worldwide. X-rays, MRIs, and CT scans aid bone cancer diagnosis. Manual processes take too much time and knowledge. Thus, a machine learning system must distinguish malignant bone from healthy bone. Cancer-damaged bones feel different. Cancer and healthy bone scans were similar. Classifying them is hard. Despite continuous innovation in the area of cancer research, it still remains one of the most fatal diseases in the world. It is paramount to harness all resources to bring breakthroughs in cancer treatment. Detection at the initial stage would be a significant stride in prolonging the life of the patient and decreasing the mortality rate. Hence, it is imperative to develop techniques that are innovative, efficient, and with lesser undesirable effects. This paper involves the study of all elements of bone cancer and features to evaluate/estimate its type. ACM is one of the most well-known techniques for segmenting a bone image into individual parts and the random forest method employs a clustering strategy with a maximum of five nodes. Insight into the organ's volumes and contours from an image is seen as highly challenging. It's a collection of operations including filtering out background noise, dividing up the data, mining for features, and making a final pick. It detaches the pixels from the edges of the object. They are treated further to bring about the intended effect. The achieved results are satisfactory, and this method is among the best ones for segmenting greyscale images currently available. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diseases

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EchoNet Dynamic: Deep Learning-driven Advancements in Dynamic Heart Function Analysis through Echocardiography 3

EchoNet Dynamic: Deep Learning-driven Advancements in Dynami...

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3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Vidhya, K. Nagarajan, B. Jenefa, A. Catherine Joy, R. Cherian, Minu Franz Kingstein, N. School of CST Karunya Institute of Tech. and Sci Coimbatore India Artificial Intelligence and Data Science KPR Institute of Engineering and Technology Coimbatore India Division of ECE Karunya Institute of Tech. and Sci Coimbatore India Computer science and engineering department Amal Jyothi college of engineering kanjirapally Kottayam India

ISBN: (纸本)9798331527549

Advances in echocardiography have significantly improved the diagnostic processes for heart function analysis. However, traditional imaging techniques often struggle with the dynamic complexities of cardiac activities. Existing methods fail to capture detailed variations in cardiac function over time, leading to suboptimal clinical decision-making and patient outcomes. Traditional echocardiography relies heavily on manual interpretation, which can be subjective and varies significantly between observers. This research presents EchoNet Dynamic, a novel method for improving echocardiographic data analysis and interpretation based on deep learning and the Transformer model. The EchoNet Dynamic dataset used in this study comprises 15,000 B-mode echocardiographic images from 750 patients, divided into training, validation, and testing sets for comprehensive model evaluation. The Transformer model demonstrated remarkable effectiveness, achieving an accuracy of 98.5%, an AUC of 0.992, and an F1 score of 0.972, surpassing traditional diagnostic tools by a significant margin. The EchoNet Dynamic model demonstrates a promising enhancement in detecting and diagnosing cardiac functions, offering substantial improvements over conventional echocardiographic methods and setting a new benchmark in cardiac healthcare. © 2025 IEEE.

关键词： Medical imaging

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Multi-Agent Deep Reinforcement Learning with Trajectory Prediction for Task Migration-Assisted Computation Offloading

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IEEE Transactions on Mobile Computing 2025年

作者： Zhang, Xinyi Wang, Chunyang Zhu, Yanmin Cao, Jian Liu, Tong Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China School of Data Science and Engineering East China Normal University Shanghai China School of Computer Engineering and Science Shanghai University Shanghai China

Multi-access edge computing has become an effective paradigm to provide offloading services for computation-intensive and delay-sensitive tasks on vehicles. However, high mobility of vehicles usually incurs spatio-temporal load-imbalances among edge servers. Therefore, task migration is employed to maintain dynamic workload balancing by transmitting excessive tasks from overloaded to underloaded servers. Recent studies adopt deep reinforcement learning approaches to generate offloading and migration decisions based on current observations of systems. However, we argue that the migration direction is highly dependent on vehicular movements, and task migration towards the wrong direction could lead to additional delays. Therefore, we emphasize the importance of guiding task migration via exploring prospective trajectories of vehicles. We propose a Mobility-Aware Cooperative Multi-Agent (MCMA) deep reinforcement learning approach to make vehicle-by-vehicle decisions in multi-edge computation offloading scenarios. A two-stage decision framework is designed to solve the joint optimization problem of computation offloading and resource allocation. Additionally, an Informer-based multi-step vehicular trajectory prediction module is incorporated to enhance the capability of forecasting vehicular movements. Extensive experiments and analysis are conducted on synthetic and realistic scenarios, showing that our approach consistently outperforms both heuristic and DRL-based methods. The simulation scenarios and source codes are publicly available here. © 2002-2012 IEEE All rights reserved.

关键词： Computation offloading

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Minimize the Energy Consumption to Increase the Network Lifetime for Green IoT Environment

Minimize the Energy Consumption to Increase the Network Life...

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International Conference on Machine Learning and Big data Analytics, ICMLBDA 2023

作者： Dekka, Satish Raju, K. Narasimha Sai, D. Manendra Pallavi, M. Sambana, Bosubabu JNTU-GV Andhra Pradesh Vizianagaram India Andhra University Andhra Pradesh Visakhapatnam India Department of Computer Science and Engineering Vignan’s Institute of Engineering for Women Visakhapatnam JNTU-GV Andhra Pradesh Vizianagaram India Department of Data Science School of Computing Mohan Babu University Andhra Pradesh Tirupathi India

ISBN: (纸本)9783031513374

One of the most important and complex issues in the IoT context is energy usage. Because the many nodes utilized in IoT contexts are energy-constrained and must operate for long periods of time without human interaction, their batteries quickly run out of power. As a result, nodes must manage their energy effectively. Sensing, communicating, and processing are regarded as the primary functions of IoT sensors. IoT connectivity uses more energy at the node level than at other times. The main motto of this study is to reduce energy consumption and lengthen network life. Although routing also has a major impact on energy usage, the first one deals with employing an energy model, and the second one deals with selecting an effective routing protocol. When picking a protocol, various other performance indicators such as throughput, delay, packet delivery ratio, and packet loss are also considered. The Network Simulator 3 is used as a tool for testing these strategies (NS3). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Energy Model Green IoT Green networks MANET NS3 Routing

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Adaptive Operator Selection for Meta-Heuristics: A Survey

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Pei, Jiyuan Mei, Yi Liu, Jialin Zhang, Mengjie Yao, Xin Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China Victoria University of Wellington Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington New Zealand Lingnan University School of Data Science Hong Kong Hong Kong

Appropriate selection of search operators plays a critical role in meta-heuristic algorithm design. Adaptive selection of suitable operators to the characteristics of different optimization stages is an important task that owns promising potential to improve the performance of a meta-heuristic algorithm. A variety of adaptive operator selection methods have been proposed in last decades, from the machine learning and optimization communities. However, the existing studies have not been systematically reviewed so far. To fill the gap, this paper provides a comprehensive survey of adaptive operator selection for meta-heuristics. According to the information required for selection, adaptive operator selection methods are classified into two categories: (i) stateless methods and (ii) state-based methods. Each category is further summarized into several key components. The strategies of each component belonging to the two categories are reviewed respectively. The motivation, strengths and weaknesses of the proposed strategies are also discussed. Furthermore, studied meta-heuristics and optimization problems in the literature are summarized. The effects from the difference of meta-heuristics and problems to the specific design of methods are discussed, together with the guidance of selecting the suitable method in different application scenarios. At the end, emerging challenges that could guide further research are discussed. © 2020 IEEE.

关键词： Adaptive algorithms

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Wireless Semantic Communication Based on Probability Distribution: An Initial Work

Wireless Semantic Communication Based on Probability Distrib...

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IEEE Conference on Wireless Communications and Networking

作者： Qingxiang Luo Yashuang Guo Aoran Zheng Zhitong Ni F. Richard Yu Victor C.M. Leung School of Computer Science and Technology Beijing Jiaotong University Big Data Technologies Centre University of Technology Sydney Department of Systems and Computer Engineering Carleton University Department of Electrical and Computer Engineering The University of British Columbia

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In the paper, we consider the general semantic transmission in wireless networks based on probability distribution. Firstly, we extract a multidimensional semantic probability distribution function, independent of any a specific wireless channel model, by using the variational inference technique. Secondly, we propose a new semantic similarity metric for measuring the difference between the received semantics and the expected semantics based on Kullback-Leibler divergence. Then, we formulate the semantic transmission problem as an optimization problem of transmission symbol adjustment with the aim to maximize the semantic similarity. Finally, we develop an optimal semantic transformation and transmission (STT) algorithm to obtain the optimal transmission symbol adjustment decision. This decision makes the closed-form expression of semantic transmission symbol available, which can realize lossless semantic transmission with energy constraint. Simulation results verify the effectiveness and robustness of the proposed STT algorithm.

关键词： Measurement Wireless networks Simulation Semantics Symbols Semantic communication Propagation losses Probability distribution Robustness Optimization

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EFNet: estimation of left ventricular ejection fraction from cardiac ultrasound videos using deep learning

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PeerJ computer Science 2025年 11卷 e2506-e2506页

作者： Ali, Waqas Alsabban, Wesam Shahbaz, Muhammad Al-Laith, Ali Almogadwy, Bassam Computer Science Department University of Engineering and Technology Lahore Pakistan Department of Computer and Network Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia Computer Science Department University of Copenhagen Copenhagen Denmark Department of Artificial Intelligence and Data Science Taibah University Medina Saudi Arabia

The ejection fraction (EF) is a vital metric for assessing cardiovascular function through cardiac ultrasound. Manual evaluation is time-consuming and exhibits high variability among observers. Deep-learning techniques offer precise and autonomous EF predictions, yet these methods often lack explainability. Accurate heart failure prediction using cardiac ultrasound is challenging due to operator dependency and inconsistent video quality, resulting in significant interobserver variability. To address this, we developed a method integrating convolutional neural networks (CNN) and transformer models for direct EF estimation from ultrasound video scans. This article introduces a Residual Transformer Module (RTM) that extends a 3D ResNet-based network to analyze (2D + t) spatiotemporal cardiac ultrasound video scans. The proposed method, EFNet, utilizes cardiac ultrasound video images for end-to-end EF value prediction. Performance evaluation on the EchoNet-Dynamic dataset yielded a mean absolute error (MAE) of 3.7 and an R2 score of 0.82. Experimental results demonstrate that EFNet outperforms state-of-the-art techniques, providing accurate EF predictions. Copyright 2025 Ali et al.

关键词： Convolutional neural networks

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Multi-UAV path planning for multiple emergency payloads delivery in natural disaster scenarios

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Journal of Electronic Science and Technology 2025年第2期23卷

作者： Kutpanova, Zarina Kadhim, Mustafa Zheng, Xu Zhakiyev, Nurkhat Department of Computer Engineering Astana IT University Astana010000 Kazakhstan School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China Department of Computational and Data Science Astana IT University Astana010000 Kazakhstan

Unmanned aerial vehicles (UAVs) are widely used in situations with uncertain and risky areas lacking network coverage. In natural disasters, timely delivery of first aid supplies is crucial. Current UAVs face risks such as crashing into birds or unexpected structures. Airdrop systems with parachutes risk dispersing payloads away from target locations. The objective here is to use multiple UAVs to distribute payloads cooperatively to assigned locations. The civil defense department must balance coverage, accurate landing, and flight safety while considering battery power and capability. Deep Q-network (DQN) models are commonly used in multi-UAV path planning to effectively represent the surroundings and action spaces. Earlier strategies focused on advanced DQNs for UAV path planning in different configurations, but rarely addressed non-cooperative scenarios and disaster environments. This paper introduces a new DQN framework to tackle challenges in disaster environments. It considers unforeseen structures and birds that could cause UAV crashes and assumes urgent landing zones and winch-based airdrop systems for precise delivery and return. A new DQN model is developed, which incorporates the battery life, safe flying distance between UAVs, and remaining delivery points to encode surrounding hazards into the state space and Q-networks. Additionally, a unique reward system is created to improve UAV action sequences for better delivery coverage and safe landings. The experimental results demonstrate that multi-UAV first aid delivery in disaster environments can achieve advanced performance. © 2025 The Authors

关键词： Unmanned aerial vehicles (UAV)

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