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2025 International Conference on Ambient Intelligence in Health Care, ICAIHC 2025

作者： Routray, Sweta Padma Mahapatra, Saswati Nayak, Debasish Swapnesh Kumar Subudhi, Enketeswara Swarnkar, Tripti University Centre for Biotechnology Bhubaneswar India University Department of Computer Application Bhubaneswar India Centurion University of Technology and Management Department of Computer Science and Engineering Bhubaneswar India National Institute of Technology Department of Computer Application Raipur India

ISBN: (纸本)9798331530259

Antimicrobial resistance (AMR) in Pseudomonas aeruginosa, a critical pathogen in healthcare-associated infections, demands new insights into underlying genetic mechanisms. This study aimed to identify key regulatory genes contributing to multidrug resistance (MDR) through a comprehensive analysis of gene expression in clinical isolates. RNA-Seq data from 246 clinical isolates of P. aeruginosa was analyzed using Weighted Gene Co-Expression Network Analysis (WGCNA) to construct co-expression networks. Modules of co-expressed genes were identified, and hub genes with high connectivity were selected, focusing on those within and across modules, including the largest module enriched with resistance-related genes. Pathway enrichment was performed using STRING and ShinyGO, highlighting key resistance-associated pathways. Pathway enrichment analysis of hub genes identified polA as a central player in DNA repair and replication processes, crucial for bacterial survival under antibiotic stress. In addition to polA, other highly connected hub genes, such as dnaB, tatC, and ppnK, were enriched in pathways including protein transport and mismatch repair. Within the turquoise module, hub genes like dnaA, uvrD, and gyrA were primarily involved in ATP-binding, ligase activity, and biosynthetic processes, essential for antibiotic resistance. Network analysis further emphasized the centrality of polA and its role in bridging multiple resistance pathways, highlighting its potential as a therapeutic target. This research highlights polA and other critical hub genes as key regulatory elements within the MDR networks of P. aeruginosa, playing essential roles in pathways vital for bacterial survival. The identification of these genes provides promising biomarkers and potential therapeutic targets to counteract MDR in clinical settings. © 2025 IEEE.

关键词： Transcription

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Dynamic Approaches of Information Technology in the Betterment of Hospital 1

Dynamic Approaches of Information Technology in the Betterme...

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1st International Conference on Healthcare Innovations, Software and engineering Technologies, HISET 2024

作者： Jadhav, Sharad Kadam, Manjushri Abhishek More, Seema Raju, B. Agarwal, Purvi Krishna Institute of Computer Application and Management Wathar India Department of Management Studies New Delhi India Jaywant College of Engineering and Poltechnic Maharashtra India Institute of Aeronautical Engineering Department of Freshman Engineering Hyderabad India Atharva College of Engineering Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350360707

Health care, both individual and institutional, is shaped by the digital revolution. Politicians and legislators are beginning to view the present models of care as unsustainable, which puts the healthcare industry at a critical crossroads. Nonetheless, the simultaneous application of blockchain, advanced IoT, AI, and smart sensors in Healthcare Management System (HMS) remains fairly problematic. Some prominent and recurrent challenges are impeding the development of HMS, such as a lack of accurate and inexpensive "smart medical sensors", "heterogeneous wearable device networks", "non-standardized IoT system architectures", multidimensional data creation, and a high need for interoperability. This study gives a full overview of the deployment of these emerging technologies "Blockchain, IoT, AI, and Smart Sensors"in HMS to better understand the progress made thus far. Major enabling properties, significant use cases, as well as effective deployments are specifically investigated, as are current research and findings on the adoption of these emerging innovations in healthcare. © 2024 IEEE.

关键词： Information management

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An Effective Agricultural Produce Object Detection System: YOLOv5-Based Classification 2

An Effective Agricultural Produce Object Detection System: Y...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Upadhyay, Priyanshu Singh, Arpita Dubey, Arpit Shukla, Himanshu Agarwal, Ritesh Gaur, Kushagra CSE Department Maharana Pratap Engineering College Kanpur India UIET Department of Computer Application Chhatrapati Sahuji Maharaj University Kanpur India Department of Rajasthan India

ISBN: (纸本)9798331530389

Automated object detection (OD) in real-time agricultural applications poses unique challenges due to uneven lighting conditions, object similarities, and cluttered environments. In comparison to standard object detection tasks, agricultural OD requires models that are both efficient and lightweight, enabling deployment on resource-constrained devices. This study introduces a novel agricultural produce detection system based on the YOLOv5 architecture, specifically designed to classify "Green Chilli," "Red Chilli," and "Eggplant." The YOLOv5 model was customized to handle these specific crops and optimized for deployment on edge devices by converting the model to TensorFlow Lite (TFLite).To enhance detection accuracy, advanced techniques such as data augmentation and hyperparameter tuning were applied. The TFLite model was optimized to run efficiently on IoT devices and low-powered CPUs, same as the Jetson Nano and Raspberry Pi, ensuring smooth real-time inference without the need for cloud-based computation. The SIoU loss function was utilized for precise localization, especially in densely packed images. The proposed system demonstrates high accuracy, precision, and recall, achieving a mAP) of 96% on a proprietary dataset of 600 images. The system's lightweight nature and real-time detection capabilities make it an effective tool for automating produce sorting and quality assessment in agricultural and food processing industries. © 2025 IEEE.

关键词： Image enhancement

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Utilizing Mixture Methods for Classifier in NLP: An Essential Consideration

Utilizing Mixture Methods for Classifier in NLP: An Essentia...

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Kavita Singh, Harveer Chandigarh School of Business Department of Master of Computer Application India Sanskriti University Department of Computer Science Engineering Uttar Pradesh Mathura India

ISBN: (纸本)9798350322309

The primary purpose of natural language program is to use it for desktop programming tool. This is very effective, and it can transform common biological conversation into a device which can be easily usable form. The robot may receive inputs in the form of data, voice, or visual. Currently, the machine earning is growing and finding usage in a variety of fields. The machine learning tool is developed by leading companies are having very big effect on the mobile and communication industry. Previous generations' daily tasks will be made easier by chat bots that are educated for all kind of issues which need to be discussed and or taken guidance. This well-known platform was mostly constructed using machine learning algorithms (ML) methods. In order to obtain the best outcomes and also the accuracy rate, the study intends to offer a principle component analysis gives the answer to a text categorization issue. © 2023 IEEE.

关键词： Natural language processing systems

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Comparative study of IoT- and AI-based computing disease detection approaches

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Data Science and Management 2025年第1期8卷 94-106页

作者： Rhmann, Wasiur Khan, Jalaluddin Khan, Ghufran Ahmad Ashraf, Zubair Pandey, Babita Khan, Mohammad Ahmar Ali, Ashraf Ishrat, Amaan Alghamdi, Abdulrahman Abdullah Ahamad, Bilal Shaik, Mohammad Khaja Department of Computer Application Lovely Professional University Punjab Phagwara144411 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur522502 India School of Computing Science and Engineering VIT Bhopal University Kothrikalan MP Sehore466114 India Babasaheb Bhimrao Ambedkar University Lucknow226025 India College of Commerce and Business Administration Dhofar University Salalah211 Oman Faculty of Computer Studies Arab Open University 18211 Bahrain Department of Computer Application Shri Ramswaroop Memorial University Lucknow225003 India College of Computing and Information Technology Shaqra University Shaqra11961 Saudi Arabia

The emergence of different computing methods such as cloud-, fog-, and edge-based Internet of Things (IoT) systems has provided the opportunity to develop intelligent systems for disease detection. Compared to other machine learning models, deep learning models have gained more attention from the research community, as they have shown better results with a large volume of data compared to shallow learning. However, no comprehensive survey has been conducted on integrated IoT- and computing-based systems that deploy deep learning for disease detection. This study evaluated different machine learning and deep learning algorithms and their hybrid and optimized algorithms for IoT-based disease detection, using the most recent papers on IoT-based disease detection systems that include computing approaches, such as cloud, edge, and fog. Their analysis focused on an IoT deep learning architecture suitable for disease detection. It also recognizes the different factors that require the attention of researchers to develop better IoT disease detection systems. This study can be helpful to researchers interested in developing better IoT-based disease detection and prediction systems based on deep learning using hybrid algorithms. © 2024 Xi'an Jiaotong University

关键词： Adversarial machine learning

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Performance Evaluation of Blockchain Platforms

Performance Evaluation of Blockchain Platforms

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2023 International Conference on IoT, Communication and Automation Technology, ICICAT 2023

作者： Gupta, Manish Kumar Dwivedi, Rajendra Kumar Sharma, Annu Farooq, Mansoor Britto Raj, S. Madan Mohan Malaviya University of Technology Department of Information Technology & Computer Application Gida Gorakhpur India RajaRajeshwari College of Engineering Department of Computer Science & Application Bengaluru India University of Kashmir Department of Management Studies Jammu & Kashmir India Rrase College of Engineering Department of Computer Science & Engineering Chennai601301 India

ISBN: (纸本)9798350302820

Blockchain (BC) is a cryptographic technology that enables the decentralized storage of an immutable record of transactions. As a result, many industries are interested in integrating BC into their IT systems. Although BC-based platforms are used in enterprise solutions, concerns regarding accessibility, privacy, performance, and scalability are still legitimate. Permissioned Blockchain (PBC) frameworks provide a solution to securely store private records. The main objective of this paper is to focuses on the performance and scalability of popular private BC platforms. To evaluate each platform, we varied the workloads and assessed performance metrics. Our comparative analysis provides valuable insights into the strengths and weaknesses of each platform, enabling enterprises to make informed decisions when choosing a private BC solution. © 2023 IEEE.

关键词： Blockchain

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A Multi-Objective Decision Assistance System for Selecting Security Controls Based On Simulation 3

A Multi-Objective Decision Assistance System for Selecting S...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Shree, Ritu Sandhu, Komal Vivekananda Global University Department of Computer Science Engineering Jaipur India Chandigarh School of Business Department of Master of Computer Application Jhanjeri India

ISBN: (纸本)9798350399264

In this work, we present the results of our continuing investigations into simulation-based risk analysis for information security and multi-objective investment optimization for security controls. We provide a methodological approach that takes into account the organization's features, information architecture, assets that must be safeguarded, unique threat sources encountered, and decision-makers' risk preferences. This framework includes dynamic attack graph generation techniques, ontological modelling of security knowledge, probabilistic reenactment of assaults by objective driven danger specialists, Identification of compelling arrangement of data security controls utilizing meta-heuristics, and intuitive choice help. These components make it possible for cutting-edge techniques to generate attack graphs and infer potential attack routes based on the motivations, goals, skills, and available entry points of attackers, Then, using this inferred information, simulate assaults based on the infrastructure a model of an organisation. The strategy aids decision-makers weigh financial and non-financial factors related to risks, costs, and benefits when assessing possible security control initiatives. In order to assess the strategy through application case studies, we are now constructing a prototype implementation of the framework. © 2023 IEEE.

关键词： Decision support systems

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Genetic Algorithm-Based Data Allocation in Multi Media Using Cloud Computing 3

Genetic Algorithm-Based Data Allocation in Multi Media Using...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Yadav, Surendra Kaur, Manpreet Vivekananda Global University Department of Computer Science Engineering Jaipur India Chandigarh School of Business Department of Master of Computer Application Jhanjeri India

ISBN: (纸本)9798350399264

The recent growth of Internet-of-Things (IoT) applications using cloud computing has been amazing. One of the advancements is heterogeneous cloud computing, which has made it possible to use the cloud for a range of infrastructure solutions, including multimedia big data. The optimizations of on-premise heterogeneous memory have been the subject of several past studies. However, the performance and financial limits brought on by hardware distributions and manipulative techniques are placing restrictions on the heterogeneous cloud memory. It is an NP-hard combinatorial issue to distribute data jobs across dispersed memory with different capacities. In order to provide high performance cloud-based heterogeneous memory service offerings, this study focuses on this problem and suggests a unique solution called Cost-Aware Heterogeneous Cloud Memory Model. It allocates data to the cloud-based memory via genetic programming. In our suggested method, we take into account a number of important elements that have a significant influence on how well Communication expenses, data transfer operating costs, energy performance, and time constraints all play a role in how cloud memories operate. Finally, we put our suggested paradigm to the test via experimental assessments. The trial findings have demonstrated the viability and scalability of our technique as a cost-conscious cloud-based solution. © 2023 IEEE.

关键词： Genetic algorithms

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BIFT: A federated learning System for Connected and Autonomous Vehicles Based on Blockchain 3

BIFT: A federated learning System for Connected and Autonomo...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Yadav, Aarti Kaur, Amandeep Vivekananda Global University Department of Computer Science Engineering Jaipur India Chandigarh School of Business Department of Master of Computer Application Jhanjeri India

ISBN: (纸本)9798350399264

Algorithms for machine learning(ML) are essential for autonomous driving. The vast majority of history's automated and connected cars send a significant quantity of movement data collected from many automobiles to a central server for regular training. Nonetheless, the info process has made the safety and confidentiality of information essential. Nowadays, the development of federated learning (FL) for data security has the potential to potential to enhance distributed machine learning's data privacy. The malicious attackers might still target the training process, though. FL is extremely vulnerable because of its total dependency on the central server. We suggest BIFT as a solution to the aforementioned issue, which consists of two parts: 1) a completely decentralized 2) A blockchain-based machine learning system for CAVs that guarantees privacy. With the use of their own driving data, BIFT's scattered CAVs may train local machine learning models, which they can subsequently upload to improve the global model. In order to fend off potential attackers, BIFT offers a consensus mechanism called Proof of Federated Learning. We assess BIFT's performance and demonstrate that it is scalable, reliable, and capable of fending off harmful assaults. © 2023 IEEE.

关键词： Autonomous vehicles

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Multi-agent QoS-aware autonomic resource provisioning framework for elastic BPM in containerized multi-cloud environment

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12895-12920页

作者： Saif, Mufeed Ahmed Naji Niranjan, S.K. Murshed, Belal Abdullah Hezam Al-ariki, Hasib Daowd Esmail Abdulwahab, Hudhaifa Mohammed Department of Computer Applications Sri Jayachamarajendra College of Engineering VTU Mysore India Department of Studies in Computer Science University of Mysore Karnataka Mysore India Department of Computer Science College of Eng. &IT University of Amran Amran Yemen Department of Information Technology Faculty of Engineering and Information Technology Al-ata’a University for Science and Technology Taiz Yemen Department of Computer Networks Engineering and Technologies Sana’a Community College Sana’a Yemen Department of Computer Application Ramaiah Institute of Technology VTU Bangalore India

Cloud computing enables businesses to improve their market competitiveness, enabling instant and easy access to a pool of virtualized and distributed resources such as virtual machines (VM) and containers for executing their business operations efficiently. Though the cloud enables the deployment and management of business processes (BPs), it is challenging to deal with the enormous fluctuating resource demands and ensure smooth execution of business operations in containerized multi-cloud. Therefore, there is a need to ensure elastic provisioning of resources to tackle the over and under-provisioning problems and satisfy the objectives of cloud providers and end-users considering the quality of service (QoS) and service level agreement (SLA) constraints. In this article, an efficient multi-agent autonomic resource provisioning framework is proposed to ensure the effective execution of BPs in a containerized multi-cloud environment with guaranteed QoS. To improve the performance and ensure elastic resource provisioning, autonomic computing is utilized to monitor the resource usage and predict the future resource demands, then resources are scaled based on demand. Initially, the required resources for executing the incoming workloads are identified by clustering the workloads into CPU and I/O intensive, and the local agent achieves this with the help of an initialization algorithm and K-means clustering. Then, the analysis phase predicts the workload demand using the proposed enhanced deep stacked auto-encoder (EDSAE), further, the containers are scaled based on the prediction outcomes, finally, the multi-objective termite colony optimization (MOTCO) algorithm is used by the global agent to find suitable containers for executing the clustered workloads. The proposed framework has been implemented in the Container Cloudsim platform and evaluated using the business workload traces. The overall simulation results proved the effectiveness of the proposed approach compare

关键词： Quality of service

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