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Applying Artificial Intelligence Techniques for Optimizing Big Data Retrieval Performance

Applying Artificial Intelligence Techniques for Optimizing B...

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

作者： Yadav, Dhananjay Vennila, C. Sonali, P. Doda, Devendra Kumar Sivakumar, Srinivas Dehankar, Pooja Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Department of Computer Science and Information Technology Karnataka Bangalore India School of Engineering Ajeenkya D. Y. Patil University Pune India

ISBN: (纸本)9798350329773

The traditional paradigm for records storage has been to depend on conventional databases and query techniques, but this paradigm is turning into an increasing number of old because the want to effectively shop and retrieve massive quantities of records maintains to grow. This paper outlines the consequences of an evaluation exploring the capability utility of artificial intelligence (AI) algorithms and techniques for optimizing records retrieval performance in big, dispensed information stores. Via the use of those algorithms, strategies including genetic algorithms and neural networks are explored for indexing, clustering, and records mining. Some of the strategies mentioned on this paper are complementary and may be used at the side of other question optimization techniques which includes those primarily based on indexes, statistical methods, and question optimizations. Using AI strategies can improve the velocity, accuracy, and scalability of huge data retrieval overall performance whilst presenting better effects and advanced efficiency. © 2023 IEEE.

关键词： Efficiency

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Analyzing the Potential of Deep Learning for Enhancing Data Science Practices 3

Analyzing the Potential of Deep Learning for Enhancing Data ...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Mercilin Raajini, X. Deshmukh, Gopal B. Prabha, Savita Singh, Jagtej Agarwal, Ankita Manu, S.E. Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India 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 Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Civil Engineering Karnataka Ramnagar562112 India

ISBN: (纸本)9798350319125

Deep learning is a progressive new field of artificial intelligence that has drastically changed the landscape of technology over the last few years. It has enabled the creation of clever packages, including self-using motors, facial recognition, natural language processing, and others. It has also affected information science, with deep getting to know presenting a new set of tools to system and examine information. This paper discusses the capacity of deep gaining knowledge for enhancing records technological know-how practices, consisting of predictive analytics, visible insights, and self-studying structures. For each subject, the paper provides a short evaluation accompanied by an evaluation and discussion. Subsequently, it provides an outline of cutting-edge developments and guidelines for how records scientists can OK use their abilities and revel to capitalize on the capacity of deep learning. © 2023 IEEE.

关键词： capitalize discussion learning recommendations scientists

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A GPU scheme for multi-secret visual sharing with varied secret dimensions and contrast enhancement using blind super-resolution

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International Journal of information technology (Singapore) 2024年第3期16卷 1801-1814页

作者： Holla, M. Raviraja Suma, D. Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education (MAHE) Karnataka Manipal 576104 India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education (MAHE) Karnataka Manipal 576104 India

Multi-secret visual sharing schemes are essential for secure and efficient sharing of sensitive visual information. However, existing schemes often overlook important considerations such as varied secret dimensions, contrast enhancement, and computational efficiency. This research addresses these challenges by proposing a comprehensive approach that addresses these limitations. Firstly, the scheme takes into account the varied dimensions of secret images encountered in real-world scenarios, allowing flexibility in sharing and reconstructing images of different sizes and aspect ratios. Secondly, the research integrates contrast enhancement techniques, such as blind super-resolution, to improve the visual quality and visibility of shared secret images affected by factors like noise, compression, or low lighting conditions. Lastly, to enhance the computational efficiency, the scheme leverages the power of Graphics Processing Units (GPUs) for parallel computing, enabling faster processing of large-scale image operations. The proposed scheme achieves outstanding results, with a high PSNR of 98.264, a strong NCC value of 0.965, an exceptionally low NAE value of 0.046, and an impressive SSIM value of 0.983. Furthermore, the GPU implementation provides a remarkable overall speedup of 112 × for 256 × 256 color images. © 2024, The Author(s).

关键词： Contrast GPU Multi-secret Real-time Security

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Image Recognition technology for Abnormal Capsule Detection 8

Image Recognition Technology for Abnormal Capsule Detection

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8th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023

作者： Wang, Chuin-Mu Chu, Shao-Wei Wu, Yen-Ching Jian, Jia-Xian Tsai, Ming-Yu Huang, Meng-Fen National Chin-Yi University of Technology Department of Computer Science and Information Engineering Taichung Taiwan National Chin-Yi University of Technology Department of Artificial Intelligence and Computer Engineering Taichung Taiwan National Cheng Kung University Department of Electrical Engineering Tainan Taiwan

ISBN: (纸本)9798350373615

Capsules are widely used in the pharmaceutical and health food industries. Traditionally, the inspection of capsule appearance relies on manual visual inspection, which is both time-consuming and prone to errors. Therefore, this paper proposes the replacement of manual inspection with deep learning image technology to improve the speed and consistency of capsule appearance defect *** this study, we focus on defect types in capsule appearance that are difficult to identify and compare the performance of two deep learning network architectures: YOLOv7 [1] and YOLOv8 [2]. The training, validation, and test sets were created using manually captured images, distributed in a 7:2:1 *** experimental results demonstrate that YOLOv8 outperforms YOLOv7 in capsule appearance defect detection. YOLOv8 achieves a recall of approximately 0.916 and an average accuracy of about 96.2%. Furthermore, YOLOv8 excels in terms of detection speed and recognition accuracy. These research findings highlight the potential of deep learning for capsule appearance inspection, offering the industry opportunities to enhance production efficiency and quality control. © 2023 IEEE.

关键词： Inspection

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A Robust Data Hiding Reversible Technique for Improving the Security in e-Health Care System

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computer Modeling in engineering & Sciences 2023年第1期134卷 201-219页

作者： Saima Kanwal Feng Tao Ahmad Almogren Ateeq Ur Rehman Rizwan Taj Ayman Radwan School of Computer and Communication Lanzhou University of TechnologyLanzhou730050China Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh11633Saudi Arabia Department of Electrical Engineering Government College UniversityLahore54000Pakistan Instituto de Telecomunicaoes and Universidade de Aveiro Aveiro3810-193Portugal

The authenticity and integrity of healthcare is the primary *** reversible watermarking schemes have been developed to improve the primary objective but increasing the quantity of embedding data leads to covering image distortion and visual quality resulting in data security detection.A trade-off between robustness,imperceptibility,and embedded capacity is difficult to achieve with current algorithms due to limitations in their *** this purpose insight,an improved reversibility watermarking methodology is proposed to maximize data embedding capacity and imperceptibility while maintaining data security as a primary concern.A key is generated by a random path with minimum bit flipping is selected in the 4 × 4 block to gain access to the data embedding *** random path's complex structure ensures data *** of various sizes(8 KB,16 KB,32 KB)are used to analyze image imperceptibility and evaluate quality *** proposed reversible watermarking methodology performance is tested under standard structures PSNR,SSIM,and *** results revealed that the MRI watermarked images are imperceptible,like the cover image when LSB is 3 bits *** proposed reversible watermarking methodology outperforms other related techniques in terms of average PSNR(49.29).Experiment results show that the suggested reversible watermarking method improves data embedding capacity and imperceptibility compared to existing state-of-the-art approaches.

关键词： Medical images security data hiding LSB matching hiding capacity imperceptibility

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ETL-LWNet: An Ensemble of Transfer Learning Based Light Weight Deep Learning Models for Identification of Lung Cancer Type in CT-Scan Images 3rd

ETL-LWNet: An Ensemble of Transfer Learning Based Light Weig...

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3rd International Conference on Advanced Computing and Applications, ICACA 2024

作者： Ray, Averi Chakraborty, Neelotpal Department of Electrical and Electronic Engineering University of Surrey Surrey GuildfordGU27XH United Kingdom Department of Computer Science and Engineering RCC Institute of Information Technology Kolkata700015 India

ISBN: (纸本)9789819747986

Lung cancer is nowadays becoming a common disease that is fatal to the life of any human being. However, modern diagnosis systems have significantly improved to detect such disease at an early stage and helping the medical specialists to effectively prescribe the appropriate treatment. However, there exists different categories of lung cancer which require specific diagnosis and treatment. Recent times have witnessed the development of different machine learning and deep learning methods for identifying any Computed Tomography Scan (CT-Scan) image as either normal or affected by any type of lung cancer. However, owing to hardware constraints there is a need to develop lightweight systems that can achieve maximum possible accuracy. Here a new system called ETL-LWNet which is an ensemble of transfer learning based light weight deep learning models, developed for effectively identifying a CT-Scan image as either normal or the type of lung cancer it displays. The proposed system achieves an accuracy of 89.21% for the Chest CT-Scan dataset which is found to be higher than that achieved by some recent methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Lung cancer

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EMT VR - A Gamified Emergency Response Training System Using Virtual Reality and Artificial Intelligence 9

EMT VR - A Gamified Emergency Response Training System Using...

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9th IEEE Smart World Congress, SWC 2023

作者： Dib, Noor Alattas, Noor Fatima, Alneyadi Obaidat, Heba Ismail, Heba Abu Dhabi University College of Engineering Department of Computer Science and Information Technology Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350319804

Traditional, real-life, and in-person training for emergency medical technicians (EMTs) poses many challenges for trainees, which can, in-turn, affect patients, leaving them victim to accidents in live emergency responses that can result in mortality. As an alternative, virtual reality (VR) training is a safer and more affordable method to prepare for disasters and emergencies that enhances response capacity of EMTs. In this study, we proposed an immersive gamified VR simulation, EMT VR, for trainees of the emergency response healthcare field. This system hosts 3D simulations of realistic emergency case scenarios in line with the United Arab Emirates (UAE) national EMT guidelines and protocols to help institutions and healthcare entities to train their paramedics, EMTs and other first responders. Using the proposed simulation, they can repeatedly access equipment in a fully immersive and safe environment. It has the scope to enhance EMT's preparedness for real-world crises and fine-tune their stress management and decision-making abilities. In turn, trainee self-confidence is developed because of the tolerance to errors during the simulated training process, unlike in real-life. It also develops a certification exam to be completed by trainees upon successfully passing all the training scenarios. © 2023 IEEE.

关键词： digital twin emergency response emergency response technician (EMT) game-based training Virtual reality (VR)

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Leveraging AI-Driven Systems to Advance Data Science Automation 1

Leveraging AI-Driven Systems to Advance Data Science Automat...

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1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Preetha, M. Wankhede, Disha S. Kumar, Ravindra Ezhilarasan, G. Khurana, Saniya Sahoo, Girija Shankar Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Civil Engineering Jaipur India Faculty of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India

ISBN: (纸本)9798350359756

Records technology automation is becoming increasingly essential for groups that want to make sense of immense and complicated datasets quickly and effectively. Leveraging AI-pushed structures gives a feasible solution for facts and technological know-how automation. AI-driven systems can automate statistics processing, exploration, analysis, and validation of records units. It allows data scientists to manage massive quantities of statistics without needing to sift through them manually. AI-driven systems identify styles, discover insights, and optimize algorithms in data technological know-how tasks. Additionally, they can automate and scale up predictive and prescriptive analytics to speedy check the effect of adjustments and pick out quick wins. With advancements in AI-driven structures, it is feasible for agencies to leverage advanced analytics and a statistics-driven approach to make better selections and force their agencies forward. © 2023 IEEE.

关键词： Automation

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Velocity control of a Stephenson III six-bar linkage-based gait rehabilitation robot using deep reinforcement learning

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Neural Computing and Applications 2025年第7期37卷 5671-5682页

作者： Kapsalyamov, Akim Brown, Nicholas A. T. Goecke, Roland Jamwal, Prashant K. Hussain, Shahid School of Information Technology and Systems University of Canberra CanberraACT2617 Australia School of Clinical Sciences Queensland University of Technology BrisbaneQLD Australia School of Systems and Computing UNSW CanberraACT Australia Department of Electrical and Computer Engineering Nazarbayev University Nur-Sultan City Kazakhstan

Lower limb rehabilitation robots can help to improve the locomotor capabilities of patients experiencing gait impairments and help medical workers by reducing strain on them. However, since commercially available exoskeletons are expensive and there is a lack of number of physiotherapists many patients are still not able to get proper rehabilitation training. The closed-loop linkage mechanisms have recently drawn much attention in the realization of gait rehabilitation robots. Such mechanisms are affordable and capable of providing suitable trajectories for gait training therapy. In this work, we have proposed a fully operational one degree-of-freedom mechanism which can generate complex naturalistic lower limb trajectories. Although in theory, it is assumed that the constant speed applied at the input crank is sufficient to control the system, in reality, the external forces exerted by human legs and the inertia of the links can greatly alter the rotational velocity at the crank, which may negatively affect the training process. Therefore, we have explored the performance of a deep reinforcement learning-based control algorithm designed to regulate the speed of the input crank to reach satisfactory performance needed for gait rehabilitation training. Experimental evaluations with healthy human subjects were conducted to demonstrate that the mechanism is capable of directing lower limbs on naturalistic gait trajectories with a required walking speed. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Exoskeleton (Robotics)

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Laptop Price Prediction Using Real Time Data 1

Laptop Price Prediction Using Real Time Data

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Reddy, Chada Lakshma Reddy, K Bhargav Anil, G.R. Mohanty, Sachi Nandan Basit, Abdul Computer Science & Engineering Vardhaman College of Engineering Hyderabad India VIT-AP University School of Computer Science & Engineering Amaravati India National Urdu University Department of Computer Science & Information Technology Hyderabad India

ISBN: (纸本)9781665472753

Online laptop sales are at an all-time high as a result of the pandemic. A laptop is a must-have for working from home, as well as e-learning and other activities. The buyer is aided in making a purchasing decision by a feature-based pricing prediction algorithm. Based on real-time data scraped from an e-commerce website, this study proposes a model for predicting laptop costs. The suggested method collects data from a real-time environment and predicts the model's pricing with high accuracy. This study employs Support Vector Regression, Decision Tree Regression and Multi-Linear Regression to forecast laptop price. © 2023 IEEE.

关键词： Costs

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