Developing an AI-Powered Interactive Virtual Tutor for Enhanced Learning Experiences

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2024, Номер 10(4)

Опубликована: Дек. 22, 2024

The integration of artificial intelligence (AI) in education has opened new avenues for enhancing personalized learning experiences. This paper proposes the development an AI-powered interactive virtual tutor designed to support students throughout their educational journey. leverages advanced natural language processing (NLP) algorithms, sentiment analysis, and machine engage real-time, providing tailored guidance, explanations, feedback. By analyzing students' patterns, emotional states, progress, AI offers recommendations interventions, both cognitive aspects learning. system’s features, including voice recognition conversational AI, allow interact naturally, facilitating a more engaging immersive experience. also presents architecture proposed tutor, key technologies involved, its potential impact on student outcomes. Initial results demonstrate significant improvements engagement, satisfaction, academic performance, suggesting that AI-driven tutors could revolutionize education..

Язык: Английский

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Enhancing Ophthalmological Diagnoses: An Adaptive Ensemble Learning Approach Using Fundus and OCT Imaging

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2024, Номер 10(4)

Опубликована: Дек. 21, 2024

This study presents an innovative Ensemble Disease Learning Algorithm (EDL) for the detection and classification of retinal diseases using fundus images. We enhance our method by incorporating deep learning techniques multi-modal imaging data, including optical coherence tomography (OCT) images alongside photographs, to provide a more comprehensive understanding pathology. The advanced EDL integrates Convolutional Neural Networks (CNNs) attention mechanisms with Capsule (CapsNet) Support Vector Machine (SVM) classifiers nuanced feature extraction classification. introduce novel ensemble adaptive weighting approach that dynamically adjusts classifier weights based on performance across disease types severity levels, significantly improving algorithm's handling complex rare cases. To model interpretability, we implement explainable AI component provides visual heatmaps most significant regions each diagnosis clinicians. evaluate enhanced large, diverse dataset encompassing multiple diseases, diabetic retinopathy, age-related macular degeneration, glaucoma, various ethnicities age groups. Our results demonstrate superior accuracy, sensitivity, specificity compared previous other state-of-the-art approaches. A prospective clinical validation assesses real-world performance. research advances automated making it robust, accurate, clinically relevant, potentially patient outcomes global eye care through early treatment planning.

Язык: Английский

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Determination of Colorectal Cancer and Lung Cancer Related LncRNAs based on Deep Autoencoder and Deep Neural Network

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2024, Номер 10(4)

Опубликована: Дек. 29, 2024

Until recently, non-coding RNAs were considered junk RNA and always ignored, but studies have revealed that many such as miRNA, lncRNA, circRNAs play important roles in biological processes. A subclass of with transcripts longer than 200 nucleotides, called lncRNAs, cellular processes gene regulation. For this reason, since wet experimental to identify disease-related lncRNA are time-consuming, computational methods used. Many researchers applied similarity-based machine learning-based achieved very successful results. Due its high success rate, the deep learning technique is fields today. In study, we used Deep Autoencoder Neural Network method predict disease related lncRNAs. As input data Autoencoder, concatenated feature vector obtained from integrated similarity was To train neural network for predicting relationships between lncRNAs diseases, features extracted autoencoder’s output utilized. The prediction performance our evaluated commonly 5-fold cross validation an AUC value 0.9575 obtained. It can be seen proposed more other compared methods. Additionally, case on colorectal cancer lung conducted confirmed literature. a result, reliably candidate

Язык: Английский

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Survey on Resume Parsing Models for JOBCONNECT+: Enhancing Recruitment Efficiency using Natural language processing and Machine Learning

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2024, Номер 10(4)

Опубликована: Дек. 15, 2024

Due to the rapid rise of digital recruitment platforms, accurate and fast resume processing is needed speed hiring. JOBCONNECT+-specific algorithms improvements are extensively covered in investigation. Better parsing technologies may reduce candidate screening time resources, which this survey encourage. Despite breakthroughs Natural language Machine Learning (NLP ML), present fail extract categorise data from different forms, hindering recruiting. The Multi-Label Parser Entity Recognition Model (M-LPERM) employs entity recognition multi-label classification increase accuracy flexibility handle explosion complexity modern formats. adaptable approach satisfies JOBCONNECT+ criteria handles formats with varying language, structure, content. Automatic shortlisting, skill gap analysis, customised job suggestions included research. In a complete simulation examination, M-LPERM compared existing models for accuracy, speed, format adaptability.

Язык: Английский

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An Efficient Nano Scale Sequential Circuits with Clock Inherent Capability in QCA For Fast Computation Paradigm

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(1)

Опубликована: Янв. 10, 2025

Quantum-Dot Cellular Automata is a cutting-edge nanotechnology emerging in the globe, has supplanted conventional CMOS technologies. Because it doesn't use electric current, this method uses less power because of Coulomb interaction. This sequential circuit design concept most challenging approach field QCA technology. In proposed study, to novel D type flip-flop with pulse generator included. plan involves n-bit counter using frequency divider approach. worked generator. The Designer, which compares simulation findings suggested constructed circuits, used implement technique. Designer E tool verify usage, forms basis performance analysis. provides lowest and improved factors based on examination current approaches.

Язык: Английский

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The Impact of Organizational Justice on Job Satisfaction: A Computational and Experimental Analysis in Workplace Systems

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2024, Номер 10(4)

Опубликована: Дек. 24, 2024

Employees play a vital role in organization growth and development. This aims to understand the relationship between employee job satisfaction justice. It has been key component product better outcomes an productivity organizations .The present research tries examine various dimensions of organizational justice on Information Technology (IT) industry. Responses from IT employees were obtained using structured questionnaire based well established scales. Respondents for study chosen (Information Technology) industry Hyderabad metro city convenience sampling method. A total 88 responses full scale data analysis. We applied multiple regression analysis analyze data. SPSS software was used The results proved that exerted positive influence satisfaction.

Язык: Английский

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Lossy Video Compression Technique for High Quality Videos Using 3D-Biorthogonal Wavelet Transform

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2024, Номер 10(4)

Опубликована: Дек. 27, 2024

This paper presents a completely new range-forward 3D video compression algorithm based on the combination of Biorthogonal Wavelet Transform (3D-BWT), scalar quantization and Huffman coding allowing decompression with high quality. Spatial temporal correlations are captured through application multi-resolution representations which derived by 3D-BWT in data decomposition. is followed that reduces precision transformed coefficients were obtained, this results into extreme while quality degradation controlled at an acceptable level. The approach best achieved using scheme. encoded optimize bitstream well suited for transmission or storage. storage optimized coding. process inverse then used alongside dequantization decoding decompression. proposed technique has been demonstrated experimental to improve existing techniques respect number metrics including ratio, mean squared error, peak signal-to-noise ratio. evaluation confirms compared other approaches, performs better achieving improved overall performance whilst its efficiency making it applicable applications.

Язык: Английский

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