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36th International Conference on Software engineering and Knowledge engineering, SEKE 2024

作者： Zhang, Zhiping Sun, Tao Zheng, Hongyan Liu, Hao Liu, Gengchen Yang, Zhi Wang, Xiaoyu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)1891706594

The aim of cross-modal image-text retrieval is to heighten comprehension and to create robust associations between visual and textual content. This process entails a mutual querying and synchronization across various data formats. While conventional techniques have seen enhancements via attention-based approaches, they frequently miss the dynamics between corresponding and non-corresponding entity areas within image-text pairs. These techniques, often fixated on aligning image entities with textual terms, can overlook the congruence of entity characteristics, leading to less accurate localization. This research introduces PNRITR, an innovative strategy for cross-modal retrieval that leverages label learning to discern attributes absent from images, broadening the scope beyond just positive entity areas. PNRITR has delivered notable advancements on the Flickr30K and MS-COCO datasets. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Knowledge based systems

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Enabling Scalable Applications with Intelligent Distributed Data Processing 2

Enabling Scalable Applications with Intelligent Distributed ...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Mandela, Nelson Alam, Intekhab Amudha, A. Sirija Priyanka, D. Singh, Dhiraj Kumar Department of Animation and Virtual Reality Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore641021 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India

ISBN: (纸本)9798350308846

This paper discusses the enabling of scalable programs with smart disbursed information processing. Scaling programs can be challenging, mainly when dealing with massive datasets. Existing scaling answers often rely on conventional database-reliant architectures, which usually require massive manual attempts and significant investments in hardware and software. Clever dispensed information processing offers an alternative technique to scalability. With this technique, facts can be processed successfully and with accelerated degrees of automation using intelligent algorithms and distributed computing. This paper presents a top-level view of the ideas in the back of slick disbursed information processing and examines the benefits and challenges associated with this technique. Key ideas addressed in this paper include using utility-precise AI algorithms, dispensed computing, and data correlations. Sooner or later, the report highlights the critical trade-off between scalability and cost regarding the usage of wise disbursed statistics processing. © 2023 IEEE.

关键词： Computing Conventional information Scalability Technique

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Real-Time Reconstruction of 3D Space using Holographic Video 15

Real-Time Reconstruction of 3D Space using Holographic Video

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Deshpande, Vivek Anandha Silambarasan, D. Kalra, Hitesh Reena, R. Gupta, Shivangi Manjunath, C. Vishwakarma Institute of Technology Department of Computer Engineering Maharashtra Pune India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Chennai127 India Quantum University Research Center Quantum University India School of Engineering and Technology Karnataka Bangalore India

ISBN: (纸本)9798350370249

The real-time Reconstruction of 3-D space and the usage of holographic video entail the capture and processing of films that have a three-dimensional shape in them. The manner of capturing the 3-dimensional shape includes: using a camera with the functionality to seize and technique the holographic photograph in a composite format, taking multiple photos, merging them into one hologram. Then, the photographs are processed in real-time with the usage of laptop imaginative and prescient algorithms for function extraction, segmentation, and 3-D object popularity. This application of the laptop, imaginative and prescient, allows the person to navigate and engage with the three-D illustration that is reconstructed in actual time. The reconstructed 3D scene is then rendered in the shape of an interactive 360-degree holographic video. This technology can provide a couple of applications in augmented and virtual reality gaming and immersive 3-D visualization of the captured environment. © 2024 IEEE.

关键词： Holograms

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Advancing Blackboard Framework: Responsive Trigger Mechanisms and Dynamic Feasibility Conditions for Optimal Knowledge Source Selection 19

Advancing Blackboard Framework: Responsive Trigger Mechanism...

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19th International Conference on Ubiquitous information Management and Communication, IMCOM 2025

作者： Muhd Mukhtar, Hana Munira Husin, Husna Sarirah Ismail, Suriana Rahmat, Azizah Ismail, Roslan Software Engineering Section Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia School of Computer Science Taylor’s University Kuala Lumpur Malaysia Centre of Innovative Digital Education & Emerging Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia

ISBN: (纸本)9798331507817

Expert systems (ES) rely heavily on effective control mechanisms to navigate complex problem-solving tasks. These mechanisms govern how ES utilizes knowledge and data to make decisions and execute actions. Optimizing these mechanisms, or addressing the "control problem," remains a central challenge. Blackboard architectures, with their separation of knowledge and control processes, offer a promising approach. This study introduces an improved control mechanism for blackboard architectures. It focuses on a critical aspect—trigger mechanisms and feasible conditions—for optimal knowledge source selection to participate in the problem-solving process. By leveraging these features, this study aims to enhance decision-making and problem-solving capabilities within complex domains. This paper focuses on the technical details of exploring how it manages the flow of rule activation and execution throughout the problem-solving process. © 2025 IEEE.

关键词： Expert systems

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Automated Leukaemia Prediction and Classification Using Deep Learning Techniques 2

Automated Leukaemia Prediction and Classification Using Deep...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Narendran, M. Amarendra, K. Revathy, D. Divyapushpalakshmi, M. Sharma, Dilip Kumar Upreti, Kamal Kcg College of Technology Department of Information Technology Tamil Nadu Chennai India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522502 India Karpaga Vinayaga College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chengalpet India Gitam University Department of Computer Science and Engineering Bengaluru561203 India Jaypee University of Engineering and Technology Department of Mathematics Madhya Pradesh Guna473226 India Department of Computer Science Delhi -NCR Ghaziabad India

ISBN: (纸本)9798350369960

Leukemia is typically diagnosed based on an abnormal blood count, frequently an elevated White Blood Cell (WBC) count. The diagnosis is established through bone marrow, replaced by neoplastic cells. Acute Lymphoblastic Leukemia (ALL) is a type of leukaemia that affects the blood and bone marrow. Leukaemia primarily affects children and adults around the world. Early leukaemia detection is critical for appropriately treating patients, especially children. This research aims to present a diagnostic method that uses computational intelligence and image processing algorithms to identify blast cells from ALL images. The medical image is prepared initially using the preprocessing and segmentation technique for efficient classification. In this research, the type is accomplished using Bidirectional Associative Memory Neural Networks (BAMNN), where the accuracy is 96.87%, the highest classification rate and outperforms the existing technique. © 2023 IEEE.

关键词： accuracy activation function and associative memory bone marrow classification DL Leukaemia neural network neurons

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The Use of 6G Communication technology in Healthcare Applications for the Accurate and Better Transmission 4

The Use of 6G Communication Technology in Healthcare Applica...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Omkar, Jamvant Menaka, S. Praveena, J. Varshney, Neeraj Arunkumar, B. Reddy, G. Vinoda Ies College of Technology Department of Electronics & Communication Engineering M. P Bhopal India Nehru Institute of Information Technology and Management Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Hummanities TN India Gla University Department of Computer Engineering and Applications Mathura India Karpagam Academy of Higher Education Computer Science Engineering Department India Hyderabad Medchal India

ISBN: (纸本)9798350360165

The IIoT is the most important element in the 6th generation networks, or 6G, so it greatly depends on effective data processing and storage. For the cause of ensuring smooth node-to-node communication, 6G provides ultra-low delays, high bandwidth, and higher-level QoS. Intelligent diagnosis, numerous in-hospital, and distant healthcare services are prerequisites to enable healthcare institutions to provide patientcentered care. What is essential is that the system hosting an array of uses, ranging from ambient appropriate living settings with wearable technology-enabled remote patient monitoring to robotic procedures powered by an intelligent networking platform. Therefore, this work addresses the needs of the healthcare sector by providing a reference-layered contact architecture for locations and devices in real-time communication. The unique 6G technology can, therefore, be utilized with great foresight to the future in the healthcare sector, enabling effective treatment and intelligent service integration. Because several networking nodes, including terminal devices, are integrated into effective health service delivery, a backbone structure is thus required for the services that take much time to complete. Therefore, a backbone structure for managing services is required to enable smooth communication by the framework, developed to enable this kind of 6G-enabled advanced healthcare application. © 2024 IEEE.

关键词： Remote patient monitoring

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ElephantGuardNet: A Comprehensive Solution for Elephant Conservation and Human-Elephant Conflict Reduction 4

ElephantGuardNet: A Comprehensive Solution for Elephant Cons...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Naveen, S. Dhamotharan, R. Sahebzathi, S. Krishnan, R. Santhana Kumar, V. Vinoth Sundaravadivel, P. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Mechanical Engineering Chennai India Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Krishnankoil India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331523923

The ElephantGuardNet system is an advanced technological framework designed to monitor and protect elephants while minimizing human-elephant conflict. The system leverages a combination of real-time data collection, AI-driven analysis, and IoT sensors to track elephant movements across vast territories. By integrating wildlife camera traps, drones, satellite imagery, and IoT sensors such as infrared motion detectors and seismic sensors, the system provides continuous monitoring of elephant activity, enabling proactive management of potential risks. A custom dataset, ElephantMovementTracker, compiled from diverse data sources, forms the foundation for the model's training. Preprocessing techniques such as data normalization, augmentation, and noise filtering enhance the dataset's quality and ensure its suitability for deep learning applications. EfficientNet B7 and YOLO are employed for image and real-time detection tasks, respectively, while 3D CNNs are used for video analytics, enabling accurate tracking of dynamic movement patterns. GRU-based models predict future elephant movements, helping assess the likelihood of conflict. XGBoost is utilized for classification tasks, evaluating the risk of human-elephant conflict. The system is deployed on AWS and incorporates NVIDIA Jetson Nano for real-time detection and automated alerting via SMS and email. IoT sensors and automated deterrent systems, including sound emitters and drones, further mitigate conflict risk. The system's effectiveness in reducing human-elephant conflicts and providing a scalable solution for elephant conservation demonstrates the potential of AI and IoT integration in wildlife protection efforts, offering a comprehensive approach to both conservation and human-elephant coexistence. © 2025 IEEE.

关键词： Drones

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Advances and Challenges in Color and Multispectral Image Retrospective Analysis 15

Advances and Challenges in Color and Multispectral Image Ret...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Soni, Ishika Yuvaraj, S. Kaur, Ashmeet Jayaprakash, B. Kolekar, Vikas K. Yuvaraj, K. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Sciences Department of Computer Science & It Karnataka Bangalore India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India

ISBN: (纸本)9798350370249

The ever-developing subject of photograph processing and pc vision has made excellent strides inside the advancement of coloration and multispectral photo retrospective analysis. This generation allows for particularly designated and accurate evaluation of images accrued from various sources. Its miles a useful asset for various industries, together with clinical imaging, facial popularity, and remote sensing. Notwithstanding the several blessings and blessings of this generation, there are still numerous demanding situations related to it. these include difficulties of appropriately processing huge amounts of information in a well timed way, the high price of acquiring and implementing the essential hardware and software program, and they want for accuracy and reliability in identifying and extracting precise features from picas. Moreover, the undertaking of dealing with regulators and highbrow assets owners of the imagery must additionally be considered. Even though these challenges persist, the terrific strides in era have enabled greater accuracy and precision, making the challenge of retrospective photograph analysis greater green and price-powerful. © 2024 IEEE.

关键词： Additionally Amounts Enabled Greater Implementing information Precision Processing

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A Non-Linear Dimensionality Reduction Approach for Unmixing Hyper Spectral Data

A Non-Linear Dimensionality Reduction Approach for Unmixing ...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Barve, Purushottam S. Vigenesh, M. Preetha, M. Deshpande, Vivek Wanjari, Mayur Bhaskar Patil, Shrishailappa Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Maharashtra Nagpur India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Maharashtra Pune India Jawaharlal Nehru Medical College Research and Development India Vishwakarma Institute of Technology Computer Engineering Pune India

ISBN: (纸本)9798350357769

This paper offers a non-linear dimensionality discount approach to unmixing hyperspectral records. Unmixing, that is, decomposing hyperspectral records into a hard and fast of element spectra and their respective fractions, is critical in quantifying resources in far-flung sensing. Nevertheless, this has proved challenging because of problems that include high dimensionality and noise in the information. The technique provided uses non-linear manifold mastering algorithms to generate low-dimensional representations of the information. Unmixing is then achieved on those low-dimensional representations, and similarly, refinement is applied using linear strategies. Experiments were carried out on simulated datasets to assess the technique's effectiveness, with top outcomes being received. © 2023 IEEE.

关键词： challenging dimensionality information representations simulated

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Hybrid convolutional neural network (CNN) for Kennedy Space Center hyperspectral image

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Aerospace Systems 2023年第1期6卷 71-78页

作者： Anand, R. Khan, Bilal Nassa, Vinay Kumar Pandey, Digvijay Dhabliya, Dharmesh Pandey, Binay Kumar Dadheech, Pankaj Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore India Department of Computer Science University of Bradford Bradford United Kingdom CSE Department Rajarambapu Institute of Technology Rajaramnagar Maharshtra Uran Islampur India Department of Technical Education IET Dr. A.P.J. Abdul Kalam Technical University Uttar Pradesh Lucknow226021 India Department of Information Technology Vishwakarma Institute of Information Technology Maharashtra Pune India Department of Information Technology College of Technology Govind Ballabh Pant University of Agriculture and Technology Pantnagar Uttarakhand Udham Singh Nagar India Rajasthan Jaipur India

The upgradation in Deep learning has enabled us to use the advantage of classification techniques into hyperspectral image for analysis of variety of image bands. In recent works, convolutional neural network (CNN) are predominantly used for visual processing of data and its classification. Hyperspectral image classification deals with both spatial and spectral information extracted from the image datasets. 3-D CNN is sparsely used due to their complexity in computation. However, it has the capability to facilitate both spatial and spectral data from the spectrum of remotely sensed images. Usage of 2-D CNN includes only the spatial information and cannot be feasible for classification. The proposed model includes 3-D CNN stacked with 2-D CNN reducing the complexity. This hybrid model validated for the remote sensing datasets of Indian Pines (IP), Pavia University (PU), Salinas (SA) and Kennedy Space Canter (KSC). The conventional principal component analysis (PCA) is applied to remove the spectral redundancy over the HSI datasets. The model incorporates both 2-D and 3-D CNN, collectively called the Hybrid Spectral CNN. It comprises of three 3-D convolutional layers and one 2-D convolution layer with 3FC (fully connected) layers. The results of the Hybrid model are compared with the 2-D CNN and 3-D CNN models. Several statistical tests like F1-score, Recall, Precision etc., are also computed. All these experiments show that the performance of hyperspectral image classification is improved efficiently with this framework. © 2022, Shanghai Jiao Tong University.

关键词： Principal component analysis

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