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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Rajathi, N. Divya, S. Anitha Elavarasi, S. Saritha, G. Jeya ramya, V. Department of Information Technology Kumaraguru College of Technology Coimbatore India Department of Computer Science and Business System Bannari Amman Institute of Technology Erode India Department of Computer Science and Engineering Sona College of Technology Salem India Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India

ISBN: (纸本)9798331504960

The increasing integration of Cyber-Physical Systems (CPS) in critical infrastructure presents unique challenges for ensuring robust cybersecurity. Traditional Intrusion Detection Systems (IDS) often struggle with the dynamic, complex nature of CPS environments, making adaptive solutions essential. This paper presents an Adaptive Intrusion Detection System (AIDS) that leverages Reinforcement Learning-Based Autoencoders (RL-AE) to detect and respond to cyber intrusions in CPS environments. The proposed model combines the power of autoencoders for unsupervised anomaly detection with reinforcement learning to continuously adapt and improve detection accuracy. Experimental results on benchmark CPS datasets demonstrate that the RL-AE-based IDS achieves a detection accuracy of 98.3%, a precision rate of 96.8%, and a recall rate of 97.5%, effectively minimizing false positives to 2.4%. The system also demonstrated rapid adaptability, with a convergence time of less than 50 iterations in response to new attack patterns. These results highlight the model's ability to enhance system resilience and promote secure and efficient CPS operations in industrial and critical infrastructure applications. This approach provides a scalable and flexible IDS solution, promoting secure and resilient CPS operations in real-time, high-stakes environments. © 2024 IEEE.

关键词： Network intrusion

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Multi-Scale Network for Thoracic Organs Segmentation

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computers, Materials & Continua 2022年第2期70卷 3251-3265页

作者： Muhammad Ibrahim Khalil Samabia Tehsin Mamoona Humayun N.Z Jhanjhi Mohammed A.AlZain Department of Computer Science Bahria UniversityIslamabadPakistan Department of Information systems College of Computer and Information SciencesJouf UniversityKSA School of Computer Science and Engineering(SCE) Taylor’s UniversityMalaysia Center for Smart Society 5.0(CSS5) Faculty of Innovation and Technology(FIT)Taylor’s UniversityMalaysia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Medical Imaging Segmentation is an essential technique for modern medical *** is the foundation of many aspects of clinical diagnosis,oncology,and computer-integrated surgical *** significant successes have been achieved in the segmentation of medical images,DL(deep learning)*** delineation of OARs(organs at risk)is vastly dominant but it is prone to errors given the complex irregularities in shape,low texture diversity between tissues and adjacent blood area,patientwide location of organisms,and weak soft tissue contrast across adjacent organs in CT *** now several models have been implemented onmulti organs segmentation but not caters to the problemof imbalanced classes some organs have relatively small pixels as compared to *** segment OARs in thoracic CT images,we proposed the model based on the encoder-decoder approach using transfer learning with the efficientnetB7 DL *** have built a fully connected CNN(Convolutional Neural network)having 5 layers of encoding and 5 layers of decoding with efficientnetB7 specifically to tackle imbalance class pixels in an accurate way for the segmentation of *** methodology achieves 0.93405 IOU score,0.95138 F1 score and class-wise dice score for esophagus 0.92466,trachea 0.94257,heart 0.95038,aorta 0.9351 and background *** results showed that our proposed framework can be segmented organs accurately.

关键词： Deep learning convolutional neural network computed tomography organs at risk computer-aided diagnostic

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PharmaLLM: A Medicine Prescriber Chatbot Exploiting Open-Source Large Language Models

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Human-Centric Intelligent Systems 2024年第4期4卷 527-544页

作者： Azam, Ayesha Naz, Zubaira Khan, Muhammad Usman Ghani Department of Computer Science University of Engineering and Technology Lahore Pakistan Al-Khwarizmi Institute of Computer Science University of Engineering and Technology Lahore Pakistan Department of Computer Science Information Technology University Lahore Pakistan

The increasing adoption of large language models (LLMs) in healthcare presents both opportunities and challenges. While LLM-powered applications are being utilized for various medical tasks, concerns persist regarding their accuracy and reliability, particularly when not specifically trained on medical data. Using open-source models without proper fine-tuning for medical applications can lead to inaccurate or potentially harmful advice, underscoring the need for domain-specific adaptation. Therefore, this study addresses these issues by developing PharmaLLM, a fine-tuned version of the open-source Llama 2 model, designed to provide accurate medicine prescription information. PharmaLLM incorporates a multi-modal input/output mechanism, supporting both text and speech, to enhance accessibility. The fine-tuning process utilized LoRA (Low-Rank Adaptation) with a rank of 16 for parameter-efficient fine-tuning. The learning rate was maintained at 2e-4 for stable adjustments, and a batch size of 12 was chosen to balance computational efficiency and learning effectiveness. The system demonstrated strong performance metrics, achieving 87% accuracy, 92.16% F1 score, 94% sensitivity, 66% specificity, and 90% precision. A usability study involving 33 participants was conducted to evaluate the system using the Chatbot Usability Questionnaire, focusing on error handling, response generation, navigation, and personality. Results from the questionnaire indicated that participants found the system easy to navigate and the responses useful and relevant. PharmaLLM aims to facilitate improved patient-physician interactions, particularly in areas with limited healthcare resources and low literacy rates. This research contributes to the advancement of medical informatics by offering a reliable, accessible web-based tool that benefits both patients and healthcare providers.

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Deep Learning Technique Based State-Of-The-Art in Skin Cancer Detection: A Review

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Journal of Mobile Multimedia 2023年第6期19卷 1583-1606页

作者： Srilakshmi, C.H. Lydia, E. Laxmi Ramakrishnaiah, N. Department of Information Technology Malla Reddy Engineering College for Women Telangana India Department of Computer Science and Engineering Vignan’s Institute of Information Technology Visakhapatnam India Department of CSE University College of Engineering JNTU Andhra Pradesh Kakinada India

Research of skin cancer images through visual survey and manual evaluation to investigate skin threatening development has always been abnormal. This manual evaluation of skin injuries to recognize melanoma is monotonous as well as somewhat long. With movement in advancement and fast development in computational resources, different AI techniques and significant learning methods have emerged for assessment of clinical pictures most especially the skin lesion images. In late years, AI arising as an innovation equipped for tackling issues connected with horticulture, medical services, business, and soon. To diminish the endanger to human existence we can embrace AI calculations in the medical care area and can foresee the deadliest skin illnesses like dangerous melanoma in beginning phases. The point of the research is to give bits of knowledge about various classifications of skin lesions and strategies executed to arrange and foresee skin diseases and the job of dermatologists while fostering the models, at last gives a general rundown of existing work. © 2023 River Publishers.

关键词： machine learning algorithms malignant melanoma skin cancers Skin lesion

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A Combined Approach of Principal Component Analysis and Support Vector Machine for Early Development Phase Modeling of Ohrid Trout(Salmo Letnica)

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computer Modeling in engineering & sciences 2021年第3期126卷 991-1009页

作者： Sunil Kr.Jha Ivan Uzunov Xiaorui Zhang School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Faculty of Computer Science and Engineering University of Information Science and TechnologyOhrid6000Republic of Macedonia

Ohrid trout(Salamo letnica)is an endemic species of fish found in Lake Ohrid in the Former Yugoslav Republic of Macedonia(FYROM).The growth of Ohrid trout was examined in a controlled environment for a certain period,thereafter released into the lake to grow their natural *** external features of the fish were measured regularly during the cultivation period in the laboratory to monitor their *** data mining methods-based computational model can be used for fast,accurate,reliable,automatic,and improved growth monitoring procedures and classification of Ohrid *** this motivation,a combined approach of principal component analysis(PCA)and support vectormachine(SVM)has been implemented for the visual discrimination and quantitative classification of Ohrid trout of the experimental and natural breeding and their growth *** PCA results in better discrimination of breeding categories of Ohrid trout at different development phases while the maximum classification accuracy of 98.33% was achieved using the combination of PCA and *** classification performance of the combination of PCA and SVM has been compared to combinations of PCA and other classification methods(multilayer perceptron,naive Bayes,randomcommittee,decision stump,random forest,and random tree).Besides,the classification accuracy of multilayer perceptron using the original features has been studied.

关键词： Salamo letnica growth phase modeling PCA SVM

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Exploring the Potential of AI-Driven Systems for Automating Data science

Exploring the Potential of AI-Driven Systems for Automating ...

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2023 IEEE International Conference on Paradigm Shift in information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Srivastava, Adarsh Katyal, Ashu Srisainath, R. Kshirsagar, Pranali S. Singh, Akhilendra Pratap Rajesh, A. Vivekananda Global University Department of Computer Science & Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology UP India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India

ISBN: (纸本)9798350329773

this paper discusses the capacity of artificial intelligence (AI)-pushed structures for automating records technological know-how duties including records cleaning, function engineering, and version improvement. We argue that those systems provide a promising opportunity to automate the hard work-extensive and time-ingesting venture of data technological know-how, enabling records scientists to recognition on the more creative factors of the paintings. We begin by using offering a brief evaluation of AI and its capacity packages after which study the existing AI-driven data technology gear on the market. We then describe a few revolutionary applications of AI and discuss their capability to improve existing facts technology approaches. Ultimately, we talk the demanding situations and barriers of AI-driven structures and advocate approaches to address them. We finish by using emphasizing the importance of responsibly integrating AI into the statistics science field and highlighting the capacity of collaboration between AI and facts technological know-how specialists. © 2023 IEEE.

关键词： technology transfer

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Efficient Cell Segmentation in Microscopy Images using Sub-Sampling Integrated SVM 2

Efficient Cell Segmentation in Microscopy Images using Sub-S...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Ramprasath, J. Kumar, M. Arun Krishnaraj, N. Vignesh, C. Chandru Saranya, N. Ramprakash, P. Dr. Mahalingam College of Engineering and Technology Department of Information Technology Pollachi India Sri Krishna College of Technology Department of Electronics and Communication Engineering Coimbatore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India Sri Krishna College of Technology Department of Computer Science and Engineering Coimbatore India Sri Krishna College of Technology Department of AI&DS Coimbatore India

ISBN: (纸本)9798331534950

Support Vector Machine (SVM) integrated with subsampling methods provides the latest approach to cell segmentation of microscopic images. This approach aims to increase computing efficiency and also maintain segmentation accuracy. To begin with, the processing load associated with large-scale microscopic images is reduced by employing subsampling techniques. On applying Support Vector Machine (SVM), classification of the picture patches into cell and non-cell portions based on the gathered attributes. The produced method is highly valuable for high-throughput analysis in medical and biological diagnostics as it provides competitive segmentation performance while drastically cutting down time for its process. Through a detailed analysis from rigorous testing and comparison with existing methods, efficiency and efficacy are demonstrated in accurately differentiating cells from microscopic images and extending solutions for vast biomedical applications. © 2024 IEEE.

关键词： Image segmentation

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CNN-based device-free health monitoring and prediction system using WiFi signals

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International Journal of information technology (Singapore) 2022年第7期14卷 3725-3737页

作者： Kumar, Amit Singh, Shashwat Rawal, Vibhor Garg, Shivam Agrawal, Anand Yadav, Sonal Department of Computer Science and Engineering Indian Institute of Information Technology Kota Jaipur India Department of Electronics and Communication Engineering Indian Institute of Information Technology Kota Jaipur India Department of Computer Science and Engineering National Institute of Technology Raipur India

Health monitoring has been a growing area of interest in recent times. It provides many beforehand benefits including early detection of potentially harmful illnesses. Till now, it has been achieved primarily by using devices such as wearable bands to monitor breathing rate and heart rate. Wearing and carrying devices make the person uncomfortable. In this paper, we introduce a deep learning based device-free way to monitor breathing rate and heart rate in different human positions to provide insight into whether the person is healthy. A convolutional neural network (CNN) model along with Gaussian naive bayes (GaussianNB) and support vector machine (SVM) is introduced and the device-free monitoring is implemented using WiFi signals. An individual in proximity causes a change in the channel state information (CSI) and the distortion is measured to provide corresponding vitals. The vitals give an insight into the health of the individual. The experimental results are quite encouraging with the evidence of superiority of SVM with CNN. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Channel state information (CSI) Convolution neural network (CNN) Support vector machine (SVM) WiFi signals

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Adoption of Blockchain-Fog-IoMT Framework in Healthcare 4.0 Digital Revolution 20

Adoption of Blockchain-Fog-IoMT Framework in Healthcare 4.0 ...

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20th OITS International Conference on information technology, OCIT 2022

作者： Mallick, Soubhagya Ranjan Sharma, Suraj Kumar Tripathy, Pradyumna Kumar Ray, Niranjan International Institute of Information Technology Computer Science and Engineering Bhubaneswar India Silicon Institute of Technology Computer Science and Engineering Bhubaneswar India Computer Science and Engineering Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (纸本)9781665493482

Healthcare is the fastest growing sector that implements the latest technologies for providing healthcare services. Most of the IoMT healthcare systems are using cloud-based systems for providing smart healthcare services. Fog Computing is introduced in loT-based healthcare environments to reduce the additional computing and storage required by cloud servers. Healthcare 4.0 focuses on a smart environment that guarantees security, privacy, reliability, integrity, easy access, trust, transparency, faster processing, etc. Blockchain-integrated IoT-fog systems are used for providing security, and privacy and avoiding single-point failure in healthcare systems. In this paper, we have proposed a Blockchain-Fog-IoMT framework for faster processing of healthcare data and reducing the storage and processing overhead on the cloud server. We have also minimized the latency, resource utilization and response time with the implementation of fog computing at the nearest network of the user devices. Ethereum Blockchain and smart contract has been integrated with our system to address the security, privacy, scalability, transparency, reliability, and interoperability challenges. Block validation and transaction validation are done with the use of proof-of-work. Moreover, this model helps to create a decentralized platform for providing healthcare services, facilitates remote-patient tracking and monitoring, and is cost-effective. © 2022 IEEE.

关键词： Smart contract

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Computing Confinement Loss of Open-Channels Based PCF-SPR Sensor with ANN Approach 5

Computing Confinement Loss of Open-Channels Based PCF-SPR Se...

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5th IEEE International Conference on Telecommunications and Photonics, ICTP 2023

作者： Islam, Nazrul Islam Khan, Md. Saikat Hasan, Md. Nahid Yousuf, Mohammad Abu Institute of Information Technology Jahangirnagar University Savar Dhaka1342 Bangladesh Mawlana Bhashani Science and Technology University Department of Computer Science and Engineering Tangail1902 Bangladesh Computer Science and Engineering Discipline Khulna University Khulna9208 Bangladesh

ISBN: (纸本)9798350393477

Surface plasmon resonance (SPR) is a sensitive spectroscopic technique for measuring changes in the refractive index (RI) of a medium in contact with a sensor surface. Confinement loss computation is crucial for designing an SPR-based photonic crystal fiber (PCF) sensor as it directly impacts its performance and sensitivity. The study highlights the simple and fast-training feed-forward Artificial Neural Network (ANN) as a technique for calculating the confinement loss of an open-channels-based PCF-SPR sensor. Moreover, ANN demonstrates proficiency in generating precise predictions for the aforementioned optical properties within the conventional parameter range, encompassing wavelengths between 0.60 μm and 0.90 μm, as well as an analyte RI range spanning from 1.33 to 1.40. Additionally, this work demonstrates a higher speed in predicting output values for varying sensor structural parameters than direct numerical simulation techniques. This study signifies a noteworthy progression in the field through the utilization of ANN-driven optimization methods for integrated silicon photonics devices. This advancement leads to improved speed and accuracy in nredicting sensor outputs. © 2023 IEEE.

关键词： Machine learning

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