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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Dubey, Krati Esnaashariyeh, Ali Nahak, Kirti Hodade, Dipali Nagnath Shrivastava, Kapil Jorvekar, Ganesh Manipal University Jaipur Department of Information Technology Jaipur India Institute of Management and Entrepreneurship Development Pune India Kalinga University Dept of Computer Science & Engineering Chhattisgarh Raipur India Shree Ramchandra College of Engineering Department of Engineering and Applied Science Maharashtra Pune412216 India Department of Computer Engineering and Applications GLA University Mathura India Sanjivani K.B.P. Polytechnic Computer Technology Kopargaon India

ISBN: (纸本)9798350330915

To improve patient care and operational effectiveness, this study investigates the implementation of big data along with artificial intelligence (AI) into medical operations. Utilizing a deductive technique and an interpretive approach, this study collects secondary data from reliable sources using a descriptive design. While AI applications concentrate on clinical decision support, tailored treatment strategies, and predictive analytics, Big Data integration includes health records, medical imaging, genome-wide data, and patient-generated information. Private data, interconnection, biases in AI systems, and resource limitations are some of the difficulties. Transparency, justice, and patient autonomy are all important ethical considerations. Strong data security protocols, continuous attempts to mitigate prejudice, interdisciplinary training, and open governance frameworks are among the suggestions offered. Future research should focus on interoperability standards © 2023 IEEE.

关键词： Big data

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Data collection of wireless sensor network based on trajectory optimization of laser-charged UAV

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High-Confidence Computing 2024年第2期4卷 116-128页

作者： Chuanwen Luo Jian Zhang Jin Qian Yi Hong Zhibo Chen Yunan Hou Xiujuan Zhang Yuqing Zhu School of Information Science and Technology Beijing Forestry UniversityBeijing 100083China Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing 100083China College of Information Engineering Taizhou UniversityTaizhou 225300China School of Computer Science Qufu Normal UniversityRizhao 276826China Research Center of Applied Mathematics and Machine Intelligence Zhejiang LabHangzhou 311121China Department of Computer Science California State UniversityCA 90032USA

Unmanned Aerial Vehicle(UAV)can be used as wireless aerial mobile base station for collecting data from sensors in UAV-based Wireless Sensor Networks(WSNs),which is crucial for providing seamless services and improving the performance in the next generation wireless ***,since the UAV are powered by batteries with limited energy capacity,the UAV cannot complete data collection tasks of all sensors without energy replenishment when a large number of sensors are deployed over large monitoring *** overcome this problem,we study the Real-time Data Collection with Lasercharging UAV(RDCL)problem,where the UAV is utilized to collect data from a specified WSN and is recharged using Laser Beam Directors(LBDs).This problem aims to collect all sensory data from the WSN and transport it to the base station by optimizing the flight trajectory of UAV such that realtime data performance is ensured It has been proven that the RDCL problem is *** address this,we initially focus on studying two sub-problems,the Trajectory Optimization of UAV for Data Collection(TODC)problem and the Charging Trajectory Optimization of UAV(CTO)problem,whose objectives are to find the optimal flight plans of UAV in the data collection areas and charging areas,*** we propose an approximation algorithm to solve each of them with the constant ***,we present an approximation algorithm that utilizes the solutions obtained from TODC and CTO problems to address the RDCL ***,the proposed algorithm is verified by extensive simulations.

关键词： Wireless sensor networks Laser-charged UAV Trajectory optimization Data collection

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mathematical model of COVID-19 dynamics in the presence of multiple controls

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Quality and Quantity 2024年第1期59卷 261-290页

作者： Akanni, J.O. Fatmawati Ajao, S. Asamoah, J.K.K. Abimbade, S.F. Department of Mathematical and Computing Sciences Koladaisi University Oyo State Ibadan Nigeria Department of Mathematics Faculty of Science and Technology Universitas Airlangga Surabaya 60115 Indonesia Department of Mathematics and Computer Science Elizade University Ondo State Ilara-Mokin Nigeria Department of Mathematics Saveetha School of Engineering SIMATS Chennai India Department of Mathematics Kwame Nkrumah University of Science and Technology Kumasi Ghana Department of Pure and Applied Mathematics Ladoke Akintola University of Technology Oyo State Ogbomoso Nigeria

The coronavirus disease 19 (COVID-19), caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2), is an infectious disease constituting the most significant challenge to human and socio-economic development across the globe. Many studies have been conducted to gain more knowledge on the dynamic spread of SARS-COV-2 in the community of people. In this study, a new mathematical model made up of a system of non-linear ODEs is designed and meticulously analyzed to understand the dynamics of transmission of COVID-19. Specific necessary properties exhibited by the COVID-19 model are examined through the theory of non-negativity and boundedness of solutions. The threshold parameter measuring the invasion potential of COVID-19 in the population is worked out by employing the Next Generation Matrix approach. The model is shown to have two endemic equilibria whenever basic reproduction number R₀<1. The analysis of the endemic equilibrium stability is explored using a suitably constructed Lyapunov function. It is shown that the COVID-19 model is globally asymptotically stable whenever the associated basic reproduction number is beyond unity. The Latin Hypercube Sampling or Partial Rank Correlation Coefficient is utilized to examine how parameter changes affect the COVID-19 spread in the population. Furthermore, the study considers six optimal control interventions to flatten the curve of the coronavirus pandemic in society. Numerical experiments of the COVID-19 model are executed in MatLab to corroborate the qualitative analysis of the model. © The Author(s), under exclusive licence to Springer Nature B.V. 2024.

关键词： 49J15 cost-effectiveness analysis COVID-19 Dynamical system LHS/PRCC mathematical modelling Optimal control analysis Sensitivity analysis

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Deep learning and encryption algorithms based model for enhancing biometric security for artificial intelligence era

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Journal of Ambient Intelligence and Humanized Computing 2024年 1-14页

作者： Byeon, Haewon Shabaz, Mohammad Surbakti, Herison Keshta, Ismail Soni, Mukesh Bhatnagar, Vaibhav Department of Digital Anti-Aging Healthcare Inje University Gimhae50834 Korea Republic of Model Institute of Engineering and Technology J&K Jammu India Information and Communication Technology Rangsit University International College Pathumthani Lak Hok Thailand Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Department of CSE University Centre for Research & ampDevelopment Chandigarh University Punjab Mohali140413 India Department of Computer Applications Manipal University Jaipur Jaipur India

The significance of facial recognition in the era of artificial intelligence lies in its utilization of facial features as a type of biometric characteristic possessing uniqueness and irreversibility. However, exposing these features to attacks, tampering, or unauthorized disclosure poses considerable threats to user privacy and security. A privacy and security solution based on deep learning and encryption algorithms is proposed to tackle this issue. This solution employs the FaceNet deep learning algorithm to extract facial features efficiently. The combination of biometric feature blurriness and cryptographic system precision is achieved, utilizing the CKKS fully homomorphic encryption algorithm for operations in the ciphertext domain of facial recognition. The SM4 algorithm is used to enhance the resilience of facial feature ciphertext against malicious attacks. By leveraging the properties of symmetric ciphers, a balance is achieved between security and computational efficiency. The management of the symmetric key used in the SM4 algorithm is conducted through the employment of the SM9 asymmetric encryption algorithm. Experimental results and analysis demonstrate that the proposed solution enhances the security of data transmission, storage, and comparison without compromising the accuracy and efficiency of facial recognition. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Differential privacy

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Graph of Effort: Quantifying Risk of AI Usage for Vulnerability Assessment

arXiv

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arXiv 2025年

作者： Mehra, Anket Aßmuth, Andreas Prieß, Malte Department of Computer Science and Electrical Engineering Kiel University of Applied Sciences Kiel Germany

With AI-based software becoming widely available, the risk of exploiting its capabilities, such as high automation and complex pattern recognition, could significantly increase. An AI used offensively to attack non-AI assets is referred to as offensive AI. Current research explores how offensive AI can be utilized and how its usage can be classified. Additionally, methods for threat modeling are being developed for AI-based assets within organizations. However, there are gaps that need to be addressed. Firstly, there is a need to quantify the factors contributing to the AI threat. Secondly, there is a requirement to create threat models that analyze the risk of being attacked by AI for vulnerability assessment across all assets of an organization. This is particularly crucial and challenging in cloud environments, where sophisticated infrastructure and access control landscapes are prevalent. The ability to quantify and further analyze the threat posed by offensive AI enables analysts to rank vulnerabilities and prioritize the implementation of proactive countermeasures. To address these gaps, this paper introduces the Graph of Effort, an intuitive, flexible, and effective threat modeling method for analyzing the effort required to use offensive AI for vulnerability exploitation by an adversary. While the threat model is functional and provides valuable support, its design choices need further empirical validation in future work. Copyright © 2025, The Authors. All rights reserved.

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computer Vision assisted Deep Learning based Sitting Posture Suggestion System

Computer Vision assisted Deep Learning based Sitting Posture...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： Ghanashyam Vagale GK Prajwal Rohit Kumar P. Padma Priya Dharishini Department of Computer Science and Engineering Ramaiah University of Applied Sciences Bangalore India

This paper presents an innovative framework that employs camera-captured visual data to detect and suggest optimal sitting postures. The framework consists of two crucial components: a video capture object and an object detection system that incorporates Deep Learning to enhance efficiency and reliability. The camera initially captures the user’s posture image, which is then subjected to video processing to extract video metadata. Subsequently, the object is created by extracting the image from the video, and the object detection algorithm is applied to provide posture recommendations. The algorithm continually monitors posture correctness and provides suggestions to improve it as necessary, ultimately benefiting the user’s daily life and mitigating potential long-term problems. Additionally, the algorithm can be further developed to recognize posture patterns and suggest corrective exercises or techniques. Furthermore, the algorithm’s efficiency can be enhanced by optimizing landmark detection for more effective outcomes. This cutting-edge framework offers immense potential for improving posture and overall health, and its development can significantly enhance the quality of life for individuals.

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Smart Traffic Management System for Efficient Mobility and Emergency Response

Smart Traffic Management System for Efficient Mobility and E...

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Knowledge engineering and Communication Systems (ICKECS), International Conference on

作者： Farhain Sultana Ritika Bhardwaj Rohan N Kumar Ruchir Gaur Sahana.P. Shankar Aryan Bharadwaj Department of Computer Science and Engineering Ramaiah University of Applied Sciences Bengaluru India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

This research addresses the critical issue of urban trafficcongestion exacerbated by the inadequacies of traditional traffic management systems. Inefficient adaptation to real-time traffic conditions by static traffic light timers leads to wasted time, and fuel, increased pollution, and compromised safety during peak hours and emergencies. To mitigate these challenges, this study proposes a novel Smart Traffic Management (STM) system that leverages cutting-edge technologies such as the Internet of Things (IoT) and Artificial Intelligence (AI). The STM framework dynamically adjusts traffic flow in response to real-time data, optimizing efficiency and enhancing emergency response mechanisms. Through comprehensive simulations and case studies, this research demonstrates the potential of the STM system to alleviate urban congestion and improve overall traffic management in diverse urban settings.

关键词： Training Adaptation models Smart cities Transportation Emergency services Data augmentation Real-time systems

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Priority queueing system with many types of requests and restricted processor sharing

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12651-12662页

作者： D’Apice, Ciro Dudin, Alexander Dudin, Sergey Manzo, Rosanna Dipartimento di Scienze Aziendali - Management and Innovation Systems University of Salerno Via Giovanni Paolo II 132 Salerno Fisciano84084 Italy Department of Applied Mathematics and Computer Science Belarusian State University 4 Nezavisimosti Ave. Minsk220030 Belarus Department of Information Engineering Electrical Engineering and Applied Mathematics University of Salerno Via Giovanni Paolo II 132 Salerno Fisciano84084 Italy

A priority queueing model with many types of requests and restricted processor sharing is considered. A novel discipline of requests admission and service is proposed. This discipline assumes restriction of the bandwidth (capacity) of the server and the number of requests that can receive service in the system at the same time. This discipline is some kind of realistic hybrid of the traditional discipline of service in a multi-server system and the discipline of the limited processor sharing. The requests of the highest priority can push out from the service the low priority requests. Therefore, the important problem is fitting of the number of requests that can receive service at the same time to the bandwidth of the server. This problem is solved via construction and analysis of a multi-dimensional Markov chain describing operation of the system under any fixed set of the system parameters. © 2022, The Author(s).

关键词： Bandwidth

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Secured Data Storage Using OAuth and Encryption of Data Using AES with Patterns and Hidden Layer

Secured Data Storage Using OAuth and Encryption of Data Usin...

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Smart Systems for Electrical, Electronics, Communication and computer engineering (ICSSEECC), International Conference on

作者： V Vilasini K Hariharan Nethra Chezhian D Kaviyarasu Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore

ISBN: (数字)9798350378177

ISBN: (纸本)9798350378184

In today's digital era, the use of data is becoming more day by day. Data is serving as a main source for all the applications we rely on. As the volume of data rapidly increases, the need for security also increases in the same phase. This brings the need for securing the data in a more effective manner. The need to protect sensitive data is underscored by the increasing intricacy of cyberattacks and the expansion of virtual commerce. It is imperative to rely on several legal and regulatory requirements for the need for data security. The failure to do so may result in severe penalties. In order to maintain trust in digital interactions, data security during transmission and storage is becoming more important as the world gets smaller. If the data is not secured in an effective manner, the growth of the companies and several other technologies may collapse and result in higher threats to the world. Both firms and governments are required to preserve intellectual property and information related to national security. The availability, confidentiality, and integrity of data are critical for sustaining innovation, trust, and business continuity. This project focusses on the two-level authentication of the system where the users are allowed to store the sensitive data. The first level authentication is provided using OAuth where it is a single sign on mechanism which is advantageous to the users to access it easily. The second level authentication is provided using the encryption technique where the Advanced Encryption Standard is used along with the creative usage of user-generated patterns as a hidden layer in the encryption process. Here a symmetric key is used for the encryption to make it more secure. The two-level authentication brings high level security for the stored data.

关键词： Technological innovation Protocols Data security Authentication User experience Encryption Internet

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RadarMOTR: Multi-Object Tracking with Transformers on Range-Doppler Maps

RadarMOTR: Multi-Object Tracking with Transformers on Range-...

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IEEE International Conference on Radar

作者： Martin Dell Wolfgang Bradfisch Steffen Schober Clemens Klöck Department Computer Science and Engineering Esslingen University of Applied Sciences Esslingen Germany

ISBN: (数字)9798350362381

ISBN: (纸本)9798350362398

Tracking with radar sensors play a crucial role in the perception of many systems, including autonomous vehicles. They are particularly effective in various environmental conditions that require reliable performance. In this paper, we propose RadarMOTR, an adaptation of MOTRv2 [1], combining both traditional radar-based object detection and Transformer-based association on Range-Doppler data. Additionally, we introduce a new dataset, based on RADDet [2], that is suitable for Multi-Object Tracking on Range-Azimuth-Doppler and Carte-sian Bird’s-Eye-View data. To annotate the data automatically, we used the SORT [3] tracker on Cartesian data. Our model achieves 78.9 % HOTA and a 10.4 p.p. higher association accuracy than a baseline linear Kalman filter on Range-Doppler data.

关键词： Adaptation models Transfer learning Radar detection Radar Object detection Radar tracking Transformers Data models Sensors Kalman filters

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