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An Analytical Review of Large Language Models Leveraging KDGI Fine-Tuning, Quantum Embedding’s, and Multimodal Architectures

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computers, Materials and Continua 2025年第3期83卷 4031-4059页

作者： Uddagiri Sirisha Chanumolu Kiran Kumar Revathi Durgam Poluru Eswaraiah G Muni Nagamani Department of Computer Science and Engineering Prasad V Potluri Siddhartha Institute of Technology Vijayawada 520007 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram 522302 India Department of Computer Science and Engineering (Data Science) AVN Institute of Engineering and Technology Hyderabad 501510 India Department of Computer Science and Engineering (Data Science) Vignan’s Institute of Management and Technology for Women Hyderabad 501301 India Department of Computer Science and Engineering Andhra Loyola Institute of Engineering and Technology Vijayawada 520008 India

A complete examination of Large Language Models’ strengths, problems, and applications is needed due to their rising use across disciplines. Current studies frequently focus on single-use situations and lack a comprehensive understanding of LLM architectural performance, strengths, and weaknesses. This gap precludes finding the appropriate models for task-specific applications and limits awareness of emerging LLM optimization and deployment strategies. In this research, 50 studies on 25+ LLMs, including GPT-3, GPT-4, Claude 3.5, DeepKet, and hybrid multimodal frameworks like ContextDET and GeoRSCLIP, are thoroughly reviewed. We propose LLM application taxonomy by grouping techniques by task focus—healthcare, chemistry, sentiment analysis, agent-based simulations, and multimodal integration. Advanced methods like parameter-efficient tuning (LoRA), quantum-enhanced embeddings (DeepKet), retrieval-augmented generation (RAG), and safety-focused models (GalaxyGPT) are evaluated for dataset requirements, computational efficiency, and performance measures. Frameworks for ethical issues, data limited hallucinations, and KDGI-enhanced fine-tuning like Woodpecker’s post-remedy corrections are highlighted. The investigation’s scope, mad, and methods are described, but the primary results are not. The work reveals that domain-specialized fine-tuned LLMs employing RAG and quantum-enhanced embeddings perform better for context-heavy applications. In medical text normalization, ChatGPT-4 outperforms previous models, while two multimodal frameworks, GeoRSCLIP, increase remote sensing. Parameter-efficient tuning technologies like LoRA have minimal computing cost and similar performance, demonstrating the necessity for adaptive models in multiple domains. To discover the optimum domain-specific models, explain domain-specific fine-tuning, and present quantum and multimodal LLMs to address scalability and cross-domain issues. The framework helps academics and practitioners identify, a

关键词： Large language models quantum embeddings fine-tuning techniques multimodal architectures ethical AI scenarios

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Deep Learning based Smart American Sign Language Detection in Alphabets 6

Deep Learning based Smart American Sign Language Detection i...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： RukumaniKhandhan, C. Vijayakumar, P. Sri Sabari, C. Arun, G. Chandrasekaran, N. Rohith Bhat, C. Kongu Engineering College Department of Computer Technology - PG Erode Perundurai India Kongu Engineering College Department of Master of Computer Applications Tamil Nadu Perundurai India Kongu Engineering College Department of Computer Technology - PG Tamil Nadu Perundurai India Erode Sengunthar Engineering College Department of Artificial Intelligence and Data Science Thuduppathi India PSNA College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350304480

Disabled people are of different kinds like physically challenged people, mentally challenged etc. To help the people who cannot convey their thoughts and who cannot hear others thoughts, sign language is being used to communicate with them. Sign language is something that is used to communicate through body movements mainly through hands and *** Sign Language(ASL) is an authorized sign language that is taught and used widely. ASL is difficult to teach and learn for them because it is completely new and they have no knowledge about it. In this research paper the ASL is recognized and converted into text through data mining techniques. The study uses a dataset of ASL alphabet images and applies various data mining techniques, including feature selection, classification, and clustering. The features extracted from the images include shape, size, color, and texture. The use of neural networks, decision trees, and support vector machines speeds up the data categorization process. Techniques such as k-means clustering and hierarchical grouping are used to facilitate entity grouping. The results show that the best performance is achieved using a combination of feature selection and classification techniques. The decision tree algorithm with the selected features achieves an accuracy of 96.5% on the test dataset. The system can accurately recognize all the ASL alphabets and convert them into text or speech. The system developed can be used to improve communication between deaf and hearing individuals and can be extended to recognize other ASL gestures and phrases. Future research can focus on real-time detection and recognition of ASL gestures using data mining techniques. CNN algorithm is used to recognize the image based on the images collected. The accuracy rate gets low and the detection is inaccurate. Therefore, VGG16 algorithm is used to recognize the images and it is unique in convolutional models. © 2023 IEEE.

关键词： ASL CNN Gestures VGG16

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ASCM: Analysis of a Sequential and Collaborative Model for Recommendations

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SN computer Science 2024年第6期5卷 788页

作者： Tandon, Righa Verma, Ajay Gupta, P.K. Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of Computer Science and Engineering Jaypee University of Information Technology HP Solan 173 234 India Department of Data Science and Engineering School of Computing Mohan Babu University AP Tirupati 517 102 India

The recommender system can predict future lists of items based on the user’s sentiments and interactions. As the data is ubiquitous, we have a number of options available to make a selection. This is the scenario where the recommender system plays its role by narrowing the available options and making the selection process easy and effective. In this paper, we have proposed a sequential recommender system that considers the user interactions along with the items in sequential order and maps the sequences dynamically, in which the weight-age is given to current preferences and recent choices as explored by the user of the system. We have provided a detailed comparison of the proposed sequential recommender system with the commonly used matrix factorization model. The proposed recommender system is based on sequential dependencies and responds equally well to the cold start problem. We have obtained results using Movielens dataset, which reveal that the proposed recommender system is much faster with higher mean reciprocal rank (MRR) value. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Collaborative filtering Machine learning Matrix factorization Recommender systems Sequential model Singular value decomposition

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Beam Alignment for IEEE 802.11be Powered by Task Oriented Indoor UWB Localization 35

Beam Alignment for IEEE 802.11be Powered by Task Oriented In...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Karakaya, Semih Serhat Sari, Talip Tolga Ak, Elif Canberk, Berk Secinti, Gokhan Istanbul Technical University Department of Computer Engineering Istanbul Turkey Bts Group Istanbul Turkey Edinburgh Napier University School of Computing Engineering and the Built Environment United Kingdom Istanbul Technical University Department of Artificial Intelligence and Data Engineering Istanbul Turkey

ISBN: (纸本)9798350362244

Coordinated beamforming is one of the crucial improvements for WiFi7. However, to provide sufficient Quality of Service, the corresponding beams have to be aligned accurately. Conventional approaches search for optimal beams in the available codebook to align the beams. However, this introduces considerable delays because of high search space. To address this issue, we utilize Ultra-Wide-Band (UWB) assisted localization to swiftly adjust the beams for users to provide accurate beams. Then by using Received Signal Strength Indicator (RSSI) and state estimation error of the Kalman filter in the position system, we propose a task oriented beamforming alignment system that uses UWB assisted localization only when necessary. Our approach improves beamforming gain by 11.6% compared to conventional beam scanning approaches. On top of this, the proposed solution improves communication effectiveness by 55.6% against naive UWB localization-assisted beam alignment. © 2024 IEEE.

关键词： Quality of service

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Collaborative Filtering of Malicious Information from the MULTimedia data Using s 1

Collaborative Filtering of Malicious Information from the MU...

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1st International Conference on computer Science and Emerging Technologies, CSET 2023

作者： Parasuraman, Manikandan Abirami, G. Arockia Raj, A. Krishnan, Batri Munnangi, Ashok Kumar Kumaresan, M. School of Computer Science and Engineering Jain Deemed to Be University Bengaluru India Sathyabama Institute of Science and Technology Department of Computer Science Chennai India Madanapalle Institute of Technology and Science Department of Computer Science and Engineering - Data Science Madanapalle India Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (纸本)9798350341737

Due to high proliferation of unsolicited information in the text, image and video files, the social media analytics engine suffers from losing its user, user privacy and others. The unsolicited information is been a huge threat to the service provides as it affects the reputations of the services provided and hence it is necessary to develop an intelligent model. Modern machine learning techniques are applied to identify and filter unsolicited information. However, some of the conventional methods are designed in an insecure way while processing all the text file, image and video files. © 2023 IEEE.

关键词： Collaborative filtering

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CNN Based Features Extraction and Selection Using EPO Optimizer for Cotton Leaf Diseases Classification

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computers, Materials & Continua 2023年第9期76卷 2779-2793页

作者： Mehwish Zafar JaveriaAmin Muhammad Sharif Muhammad Almas Anjum Seifedine Kadry Jungeun Kim Department of Computer Science COMSATS University IslamabadWah CampusWah Cantt47040Pakistan Department of Computer Science University ofWahWah Cantt47040Pakistan National University of Technology(NUTECH) Islamabad44000Pakistan Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Worldwide cotton is the most profitable cash *** year the production of this crop suffers because of several *** an early stage,computerized methods are used for disease detection that may reduce the loss in the production of *** several methods are proposed for the detection of cotton diseases,however,still there are limitations because of low-quality images,size,shape,variations in orientation,and complex *** to these factors,there is a need for novel methods for features extraction/selection for the accurate cotton disease *** in this research,an optimized features fusion-based model is proposed,in which two pre-trained architectures called EfficientNet-b0 and Inception-v3 are utilized to extract features,each model extracts the feature vector of length N×*** that,the extracted features are serially concatenated having a feature vector lengthN×*** prominent features are selected usingEmperor PenguinOptimizer(EPO)*** method is evaluated on two publically available datasets,such as Kaggle cotton disease dataset-I,and Kaggle *** EPO method returns the feature vector of length 1×755,and 1×824 using dataset-I,and dataset-II,*** classification is performed using 5,7,and 10 folds *** Quadratic Discriminant Analysis(QDA)classifier provides an accuracy of 98.9%on 5 fold,98.96%on 7 fold,and 99.07%on 10 fold using Kaggle cotton disease dataset-I while the Ensemble Subspace K Nearest Neighbor(KNN)provides 99.16%on 5 fold,98.99%on 7 fold,and 99.27%on 10 fold using Kaggle cotton-leaf-infection dataset-II.

关键词： Deep learning cotton disease detection features selection classification efficientnet-b0 inception-v3 quadratic discriminant analysis subspace KNN

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Investigation on Nail Denting or Crumbling from Nail Psoriasis Patient Ailment data using the Machine Learning Algorithm and data Mining Techniques 8

Investigation on Nail Denting or Crumbling from Nail Psorias...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Janani, K. Santhoshkumar, S.P. Vikram, D. Priya, S. Vijayaraj, M. Priyanga, M. Manipal Institute of Technology Department of Data Science and Computer Applications Manipal India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Avadi Department of Computer Science and Engineering Chennai India Rathinam Technical Campus Department of Artificial Intelligence and Data Science Coimbatore India Rathinam Technical Campus Department of Information Technology Coimbatore India

ISBN: (纸本)9798350367904

Nail denting or crumbling may result from nail psoriasis. A nail condition known as psoriatic onychodystrophy or psoriatic nails. Psoriasis sufferers frequently experience it;reported occurrences range from 10% to 78%. Psoriatic nails are more common in the elderly and in people with psoriatic arthritis. By examining the nails on a human hand, numerous illnesses can be found early in the diagnosing process. The nail color of an individual can help in the diagnosis of specific medical disorders. In this case, the recommended method results in the decision-making process for sickness diagnosis. The system is fed by human nail art. The technique recognizes nail features unique to a given condition by analyzing images of nails. The suggested technique makes advantage of the various distinctive characteristics of human nails to detect illness by changing the colour of the nails. The first training set data, which is derived from an image of a patient's nails with a particular ailment, is handled by the Weka tool. To get the desired results, the feature discoveries of the picture are compared to the training dataset. The distortion of the nail unit is known as nail disease. Nail units have their own disease class due to their own indications, symptoms, causes, and consequences, which may or may not be related to other medical disorders. Nail problems are still difficult and ambiguous to identify. A novel machine learning method for recognising and classifying nail problems from photographs is presented in this study. This framework combines CNN models with data mining techniques to extract characteristics. This research was also contrasted with certain other province algorithms (Support vector, ANN, K - nearest neighbors, and RF) evaluated on datasets and showed positive results. The accuracy numbers show how well the model can distinguish nail conditions. This ground-breaking discovery lays the groundwork for precise diagnosis in dermatology, medical image processing, an

关键词： Medical image processing

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Class-Aware Contextual Information for Semantic Segmentation 48

Class-Aware Contextual Information for Semantic Segmentation

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Tang, Huadong Zhao, Youpeng Jiang, Yingying Gan, Zhuoxin Wu, Qiang University of Technology Sydney School of Electrical and Data Engineering Australia China University of Central Florida Department of Computer Science United States

ISBN: (纸本)9781728163277

Exploring spatial contextual information is a well-adopted approach to achieving better semantic segmentation performance. However, most existing methods neglect the class association between the neighboring pixels. In this paper, we propose a CACINet, which consists of a Semantic Affinity Module (SAM) and a Class Association Module (CAM), to generate class-aware contextual information among pixels on a fine-grained level. SAM analyzes the affiliation of any two given pixels belonging to the same or different class. It produces intra-class and inter-class pixel contextual information. CAM classifies the image into different class regions globally and then it encodes the pixel based on the degree of affiliation of the pixels with each class in the image. In this way, it augments the class affiliation of the pixels into the corresponding context calculation. Comprehensive experiments demonstrate that the proposed method achieves competitive performance on two semantic segmentation benchmarks: ADE20K and PASCAL-Context. © 2023 IEEE.

关键词： class-aware contextual information semantic segmentation

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Pancreatic Cancer Prediction Machine Learning using Multi Fusion Model

Pancreatic Cancer Prediction Machine Learning using Multi Fu...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Sathiyapriya, V. Devi, K. Nethra Deepa, S. Marimuthu, M. Manikandan, P. Prabu, S. Knowledge Institute of Technology Department of Computer Science and Engineering Kakapalayam India Gnanamani College of Technology Department of Artificial Intelligence and Data Science Namakkal India Vellore Institute of Technology School of Computer Science and Engineering Chennai India Srm Institute of Science and Technology School of Computing Tiruchirappalli India

ISBN: (纸本)9798350375442

Pancreatic cancer poses a significant challenge in early detection and treatment due to its malignant nature within the digestive tract. Recent studies, such as those conducted by the Pancreatic Cancer Action Network, predict that pancreatic cancer will rank as the world's second leading cause of cancer-related deaths in 2020. Various methods have been explored in the literature to segment pancreatic tumors and detect lesions from CT images, crucial for assessing severity. One such method is the Min-Max segmentation algorithm, which effectively removes the jaundiced sclera region from affected eyes by analysing color gradients, offering more feasible and discriminative solutions. Additionally, the utilization of Whale Optimized feature-based selection aids in extracting optimal features for optimization purposes. Through an analysis of different classification algorithms and the implementation of a suggested technique for achieving optimum solutions and maximum accuracy, the proposed work is structured into two phases. Phase I involves pre-processing and artifact removal, followed by segmentation, while Phase II focuses on feature extraction, feature selection, and classification. The initial step of pre-processing the image facilitates pixel location using intensity measures, crucial for accurate tumor segmentation and minimizing false detection. Ultimately, the proposed approach aims to design and develop an optimal feature selection strategy for classifying pancreatic cancer tumors in PET/CT images, resulting in increased classification efficiency 92% and improved cancer tumor detection rates with compare other existing approaches. © 2024 IEEE.

关键词： computerized tomography

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Graphene oxide-coated microbottle resonators for relative humidity sensing

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Optoelectronics Letters 2024年第7期20卷 400-405页

作者： Huda Adnan Zain Malathy Batumalay Md Ashadi Md Johari Hazli Rafis Abdul Rahim Sulaiman Wadi Harun Department of Electrical Engineering Faculty of EngineeringUniversity of MalayaKuala Lumpur 50603Malaysia Faculty of Data Science and Information Technology INTI International UniversityNilai 71800Malaysia1 Faculty of Electrical and Electronic Engineering Technology Universiti Teknikal Malaysia MelakaMelaka 76100Malaysia Faculty of Electronic and Computer Engineering Universiti Teknikal Malaysia MelakaMelaka 76100Malaysia

Graphene oxide(GO)is a 2D coating material used to improve fiber optics sensors’response to relative *** resonators(MBRs)have garnered more attention as sensing media *** MBR with a 190μm diameter was coated with ***,tapered fiber light coupling was used to investigate the relative humidity sensing performance in the range of 35—70%RH at 25℃.The MBR showed a higher Q factor before and after GO *** sensitivity of 0.115 dB/%RH was recorded with the 190μm GO-coated MBR sample compared to a sensitivity of 0.022 dB/%RH for the uncoated MBR *** results show that the MBR can be used in fiber optic sensing applications for environmental sensing.

关键词： humidity oxide bottle

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