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A Novel Capability of Object Identification and Recognition Based on Integrated mWMM

computers, Materials & Continua 2023年第4期75卷 959-976页

作者： MZeeshan Sarwar Mohammed Hamad Alatiyyah Ahmad Jalal Mohammad Shorfuzzaman Nawal Alsufyani Jeongmin Park Department of Computer Science Air UniversityIslamabadPakistan Department of Computer Science College of Sciences and Humanities in AflajPrince Sattam Bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Engineering Tech University of Korea237 Sangidaehak-roSiheung-siGyeonggi-do15073Korea

In the last decade,there has been remarkable progress in the areas of object detection and recognition due to high-quality color images along with their depth maps provided by RGB-D *** enable artificially intelligent machines to easily detect and recognize objects and make real-time decisions according to the given *** cues can improve the quality of object detection and *** main purpose of this research study to find an optimized way of object detection and identification we propose techniques of object detection using two RGB-D *** proposed methodology extracts image normally from depth maps and then performs clustering using the Modified Watson Mixture Model(mWMM).mWMM is challenging to handle when the quality of the image is not ***,the proposed RGB-D-based system uses depth cues for segmentation with the help of *** it extracts multiple features from the segmented *** selected features are fed to the Artificial Neural Network(ANN)and Convolutional Neural Network(CNN)for detecting *** achieved 92.13%of mean accuracy over NYUv1 dataset and 90.00%of mean accuracy for the Redweb_v1 ***,their results are compared and the proposed model with CNN outperforms other state-of-the-art *** proposed architecture can be used in autonomous cars,traffic monitoring,and sports scenes.

关键词： Artificial intelligence convolutional neural network depth images interactive-object detection machine learning

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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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Adaptive Intrusion Detection in Cyber-Physical Systems using Reinforcement Learning-Based Autoencoders 2

Adaptive Intrusion Detection in Cyber-Physical Systems using...

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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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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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Uncovering Android Zero-Day Threats: The Zero-Vuln Approach with Deep and Zero-Shot Learning 2

Uncovering Android Zero-Day Threats: The Zero-Vuln Approach ...

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2nd International Conference on Computing and Data Analytics, ICCDA 2024

作者： Palarimath, Suresh Maran, Pyingkodi Wilfred Blessing, N.R. Kavitha, S.J. Cibi, A. Sutherlin, Subitha G. College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman Kongu Engineering College Department of Computer Applications Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Department of Computer Science and Engineering Pallavaram Chennai India Virtusa Consulting Services Private Limited Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Kanyakumari India

ISBN: (纸本)9798331542573

The growing occurrence of cybersecurity hazards, such as Android zero-day vulnerabilities, poses substantial difficulties because they are unattended and imperceptible. With the increasing popularity of Android smartphones, hackers are taking advantage of these weaknesses to quickly spread advanced malware. Conventional detection approaches frequently prove inadequate due to the absence of shown criteria for emergent threats. In order to address this crucial problem, this article presents the Zero-Vuln method, an advanced technique specifically developed to identify and categorize previously unidentified malware on Android platforms. Zero-Vuln combines sophisticated supervised deep learning methods with zero-shot learning algorithms to accurately detect zero-day threats. Through the utilization of comprehensive datasets, the method attains an average classification metric score of 84.22%, showcasing exceptional accuracy, precision, and recall. This advanced solution significantly improves the ability to identify and handle Android zero-day threats, representing a major progress in cybersecurity research and implementation. The Zero-Vuln strategy provides a strong and adaptable solution for the constantly changing mobile security risks, representing a notable advancement in the sector. © 2024 IEEE.

关键词： Zero-shot learning

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Correction to: Automated approach to predict cerebral stroke based on fuzzy inference and convolutional neural network (Multimedia Tools and Applications, (2024), 10.1007/s11042-024-19254-0)

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Multimedia Tools and Applications 2024年 1-2页

作者： Alrowais, Fadwa Jamjoom, Arwa A. Karamti, Hanen Umer, Muhammad Alsubai, Shtwai Abate, Andrea F. Ashraf, Imran Department of Computer Sciences College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Computer Science University of Salerno Fisciano84084 Italy Department of Information and Communication Engineering Yeungnam University Gyeongsan38541 Korea Republic of

The original publication of this article contains an error in the affiliation of authors Fadwa Alrowais and Hanen Karamti. Incorrect: Department of information Systems, College of computer and information sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia Correct: Department of computer sciences, College of computer and information sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia The original article has been corrected. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

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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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Ciphertext-only fault analysis of GIFT lightweight cryptosystem

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science China(information sciences) 2022年第3期65卷 263-265页

作者： Wei LI Shan CAO Dawu GU Jiayao LI Tianpei CAI Menglin WANG Li SUN Zhiqiang LIU Ya LIU School of Computer Science and Technology Donghua University Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai Key Laboratory of Computing and Systems Shanghai Jiao Tong University Shanghai Key Laboratory of Integrate Administration Technologies for Information Security Shanghai Jiao Tong University Department of Computer Science and Engineering University of Shanghai for Science and Technology

Dear editor,The GIFT cryptosystem was proposed by Banik et al. [1]in CHES 2017. It can be widely applied to protect RFID tags and other low-resource devices. It has an SPN structure with a fixed 128-bit key size and two flexible variants of 64-bit and 128-bit block sizes. In simulations, GIFT achieves good performance and surpasses both SIMON and SKINNY [1]. In 2013, Fuhr et al. [2] proposed a ciphertextonly fault analysis(CFA) of AES for three types of faults:zero-byte fault, zero-nibble fault,

关键词： lightweight cryptosystem GIFT ciphertext-only fault analysis cryptanalysis fault analysis

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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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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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