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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Deepak, Gerard Hariharan, Nitin Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering Alagappa Chettiar Government College of Engineering and Technology Karaikudi India

ISBN: (纸本)9783031889912

The need for extensions of documents for highly special sensitive domains like cultural studies which involves countries, and which involves cultural relationships and political scenarios between cultures and across countries. As the worldwide web continues to evolve, it is transitioning into a densely populated semantic web, which is considered the standard for the next generation of the web, commonly known as web 3.0. This paper suggests a framework for Document Recommendation framework for Cultural Studies (DRCS) that utilizes methods like term frequency - inverse document frequency (TF-IDF) and LDA (Latent Dirichlet Allocation) to analyze the term data set. Metadata generation is used to collect and arrange the information, employing transformers for classification. The XGBoost classifier, which is feature-controlled, is used for data set classification. To measure semantic relevance, strong similarity measures like APMI (Adaptive Pointwise Mutual Information) and Pianka Index, Ren-Konen Index, Sim Rank are used. The model incorporates LDA for topic modelling and utilizes resources like the Google Knowledge Graph API to access community-verified knowledge. To optimize the solutions, the UnderCricket Algorithm optimization algorithm is employed. The proposed model achieves high percentages of precision, recall, accuracy, and F-measure, specifically 94.75%, 96.74%, 95.745%, and 95.7346598% respectively. Additionally, the model achieves a low value of FDR (False Discovery Rate) at 0.06% is achieved. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Recommender systems

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Neural Network Based diagnostic Approach for Cervical Cancer Cell stratification from Pap Smear Images 11

Neural Network Based diagnostic Approach for Cervical Cancer...

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11th IEEE Region 10 Humanitarian technology Conference, R10-HTC 2023

作者： Bhavsar, Barkha Shrimali, Bela LDRP-ITR SVKM Computer Engineering Department Gandhinagar India Institute of Technology Nirma University Computer Science and Engineering Department Ahmedabad India

ISBN: (纸本)9798350326147

As per the recent statistics from the well-known organization WHO (World Health Organization), gynecologic cancer is fourth common cancers which affects the lives of women all over the globe. Early detection of cervical cancer, taking preventive measures, and performing primary care treatments are equally important to lower the mortality rate. Due to ambiguities in traditional diagnostic approaches, the Artificially Intelligent system-developed diagnosis comes in handy to provide a second opinion when it comes to the detection and classification of cervical cancer more quickly and efficiently. A neural network-based approach is presented in this study to classify the pap smear data at an early stage. Some important evaluation parameters such as accuracy, precision, recall, F1 score are measured to verify the efficacy of the network models. © 2023 IEEE.

关键词： Intelligent systems

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A Model for Predicting Chronic Kidney Diseases Based on Medical Data Using Reinforcement Learning

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SN computer Science 2024年第4期5卷 353页

作者： Nramban Kannan, Senthil Kumar Aseervatham, Joshi Moholkar, Kavita Palanimuthu, Mithun Marappan, Saranya Narendran, Muthusamy Sathar, Banu Sengan, Sudhakar Department of Computer Science & amp Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamil Nadu Chennai 600123 India Department of Computer Science and Business Systems Engineering JSPM’S Rajarshi Shahu College of Engineering Savitribai Phule Pune University Pune 411033 India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Tamil Nadu Chennai 600119 India Department of Information Technology Paavai Engineering College Tamil Nadu Namakkal 637018 India Department Information Technology KCG College of Technology Tamil Nadu Chennai 600097 India Department of Master of Computer Application T. J. Institute of Technology Chennai 600100 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli 627451 India

Kidney diseases (KD) are a global public health concern affecting millions. Early detection and prediction are crucial for effective treatment. Artificial intelligence (AI) techniques have been used in KDP to analyze past medical records, applying patients’ Electronic Medical Record (EHR) data. However, conventional statistical analysis methods conflict with fully comprehending the complexity of EHR data. AI algorithms have helped early KDP learn and identify complex data patterns. However, challenges include training heterogeneous historical data, protecting privacy and security, and developing monitoring system regulations. This study addresses the primary challenge of training heterogeneous datasets for real-world evaluation. Early detection and diagnosis of chronic kidney disease (CKD) is crucial for improved outcomes, reduced healthcare costs, and reliable treatment. Early treatments are crucial for CKD, as it often develops without apparent symptoms. Predictive models, particularly those using reinforcement learning (RL), can identify significant trends in complex healthcare information, which standard techniques may struggle with. The study makes KDP more accurate and reliable using RL methods on clinical data. This lets doctors find diseases earlier and treat them better by looking at static and changing health measurements. Machine learning (ML) algorithms can enhance the accuracy of AI systems over time, enhancing their effectiveness in detecting and diagnosing diseases. In the current investigation, the RL-ANN model is implemented for performing enforceable CKD by assessing the outcomes of multiple neural networks, which include FNN, RNN, and CNN, according to parameters such as accuracy, sensitivity, specificity, prediction error, prediction rate, and kidney failure rate (KFR). The recommended RL-ANN method has a lower failure rate of 70% based on the KFR data. Further, the proposed approach earned 95% in PR and 70% in analysis of errors. However, the RL

关键词： Alerting systems and diagnostic assistance Chronic kidney disease Machine learning Medical history RL-ANN

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A Disaster Prediction Ensemble Classifier Using Artificial Intelligence

A Disaster Prediction Ensemble Classifier Using Artificial I...

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2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Bharathi, V. Saranya, A. Shukla, Garima Shekhawat, Deepika Awasthi, Vanshaj Kumar, C.N.S. Vinoth Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Amity University Amity School of Engineering and Technology Department of Computer Science and Engineering Mumbai India Amity University Department of Computer Science and Engineering Mumbai India Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur Chennai India

ISBN: (纸本)9798331533311

Natural and man-made disasters may have severe impacts, but disaster prediction can help lessen such effects. The natural catastrophes that strike India on a regular basis cause immense devastation in terms of human lives and material possessions. Predicting the start and development of disasters in real-time is essential for reducing their effect. For more precise and trustworthy catastrophe predictions, this research presents an AI-powered catastrophe Ensemble B-Classifier network (DE BCN). The DPEC ensemble technique improves forecast accuracy over individual models by integrating various predictive outputs from numerous machine learning models that specialise in distinct kinds of disasters. This is where the data was originally pulled from Kaggle. To finish off the preprocessing, the norma clash filter was used. The next step is to use the optimisation method known as mustard twin swarm to extract characteristics that are connected to the tragedy. With any luck, the calamity Ensemble-B Classifier network will be able to forecast the calamity. Python and the Kaggle database were the environments used to carry out the investigation. According to the results of the simulations, the suggested approach is better than the current standard for predicting the catastrophic state. © 2024 IEEE.

关键词： Prediction models

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AI-Driven Assessment and Evaluation: Enhancing Sustainability in Educational Practices 3

AI-Driven Assessment and Evaluation: Enhancing Sustainabilit...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Chinnasamy, P. Pushpalatha, M. Nirmala, V. Vidhya, V. Ayyasamy, Ramesh Kumar Priya, Divya Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Department of Computer Science and Technology Karpagam College of Engineering Coimbatore India Department of MCA Annamacharya PG College of Computer Studies Rajampet Kadapa India India Kampar Perak Malaysia Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

ISBN: (纸本)9798331532420

AI-powered educational technologies are emerging as transformative forces in the quickly changing field of education, where innovation is essential to keeping ahead of the competition. Assessments are one area that is undergoing revolutionary transformation. Artificial Intelligence (AI) is redefining the way we evaluate learning outcomes as traditional evaluation approaches struggle to keep up with the demands of the digital age. The field of artificial intelligence-powered learning assessments has a lot of promising future developments ahead of it. This research proposes novel technique in assessment and evaluation based on online education system using Artificial intelligence in machine learning techniques. Here, the input has been analysed as student performance data based on online education and processed for removing of missing values with noise removal. Then this data has been analysed using spatio LSTM convolutional fuzzy neural network for classification of evaluation and assessment model. the experimental analysis has been carried out for various student performance analysis dataset in terms of training accuracy, average precision, specificity, F-measure and recall. Proposed method training accuracy 97%, Average precision 94%, F-measure 90%, RECALL 93%, SPECIFICITY 96%. © 2024 IEEE.

关键词： Adversarial machine learning

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AI-Driven Learning Management Systems:Modern Developments, Challenges and Future Trends during theAge of ChatGPT

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computers, Materials & Continua 2024年第8期80卷 3289-3314页

作者： Sameer Qazi Muhammad Bilal Kadri Muhammad Naveed Bilal AKhawaja Sohaib Zia Khan Muhammad Mansoor Alam Mazliham Mohd Su’ud Department of Computer Science Faculty of EngineeringScience and TechnologyIQRA UniversityKarachi75500Pakistan College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Engineering Department of Electrical EngineeringIslamic University of MadinahP.O.BOX 170Madinah41411Saudi Arabia Faculty of Engineering Department of Mechanical EngineeringIslamic University of MadinahP.O.BOX 170Madinah41411Saudi Arabia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Computing and Informatics(FCI)Multimedia University Cyberjaya63100Malaysia Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyNSW 2007Australia

COVID-19 pandemic restrictions limited all social activities to curtail the spread of the *** foremost and most prime sector among those affected were schools,colleges,and *** education system of entire nations had shifted to online education during this *** shortcomings of Learning Management Systems(LMSs)were detected to support education in an online mode that spawned the research in Artificial Intelligence(AI)based tools that are being developed by the research community to improve the effectiveness of *** paper presents a detailed survey of the different enhancements to LMSs,which are led by key advances in the area of AI to enhance the real-time and non-real-time user *** AI-based enhancements proposed to the LMSs start from the Application layer and Presentation layer in the form of flipped classroom models for the efficient learning environment and appropriately designed UI/UX for efficient utilization of LMS utilities and resources,including AI-based *** layer enhancements are also required,such as AI-based online proctoring and user authentication using *** extend to the Transport layer to support real-time and rate adaptive encrypted video transmission for user security/privacy and satisfactory working of *** also needs the support of the Networking layer for IP-based geolocation features,the Virtual Private Network(VPN)feature,and the support of Software-Defined Networks(SDN)for optimum Quality of Service(QoS).Finally,in addition to these,non-real-time user experience is enhanced by other AI-based enhancements such as Plagiarism detection algorithms and Data Analytics.

关键词： Learning management systems chatbots ChatGPT online education Internet of Things(IoT) artificial intelligence(AI) convolutional neural networks natural language processing

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Enhancing the Capability of Single Error Correction and Double Error Detection Method 2

Enhancing the Capability of Single Error Correction and Doub...

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2nd International Conference on Information and Communication technology, ICICT 2024

作者： Sadi, Muhammad Sheikh Molla, M.D. Peyal Asraf, Amanullah Ali, Md. Liakot Department of Computer Science and Engineering Khulna University of Engineering & Technology Khulna Bangladesh Institute of Information and Communication Technology Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (纸本)9798331508227

This paper proposes a new approach to tolerate double-bit soft errors. Soft error is becoming a matter of concern for computer systems. Critical systems and systems operated in electronic hostile environments are at high risk due to the sudden occurrence of soft errors. This paper proposes a new method for correcting double-bit soft errors in an n-bit dataword where n can be any integer number. The most efficient part of this paper is the temperate use of redundant bits. In this paper, the XOR method is used along with the SECDED (Single Error Correction - Double Error Detection) code to enhance the capability of SECDED toward double-bit error correction. This paper corrects all two-bit errors and can also correct almost every type of triple-bit error. ©2024 IEEE.

关键词： Errors

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Advancing Online Education: An Interactive Framework for Aligning Teaching Methods with Learning Styles

Advancing Online Education: An Interactive Framework for Ali...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： De Silva, D.I. Athukorala, K.S.N. Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Uva Wellassa University Department of Computer Science and Informatics Badulla Sri Lanka

ISBN: (纸本)9798331543617

This research develops an innovative method for enhancing existing distance education by adjusting the teaching process to students' learning styles. The study investigates the relationship between teachers' teaching styles and learners' preferences, refining Grasha's teaching style model to better suit students' needs. As a result, an online system was created that enabled teachers to modify their strategies during lessons, thereby improving student participation and performance. Furthermore, a mobile solution was also implemented to address the need for portable and easy-to-use options. To evaluate the adoption of the system, university students were administered questionnaires aimed primarily at determining their main learning styles. Based on the analysis, lecturers were trained to modify their teaching methods accordingly. The findings revealed significant improvements in student satisfaction and academic performance after the alignment of teaching methods with learning preferences. Cronbach's Alpha values confirmed the high reliability of the results, highlighting increased engagement and satisfaction among students. The contribution of this study lies in its practical approach to improving online education through personalized teaching methods. The platform provides educators with a real-time feedback loop, enabling dynamic adjustment of teaching strategies to optimize student engagement. The research demonstrates technical soundness, supported by rigorous data analysis, and has the potential to revolutionize online learning environments globally. © 2024 IEEE.

关键词： Contrastive Learning

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Mechanical properties prediction of metakaolin and fly ash - based geopolymer concrete using SVR

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Journal of Building Pathology and Rehabilitation 2024年第1期9卷 1-14页

作者： Pratap, Bheem Sharma, Sanjay Kumari, Pooja Raj, Sumit Department of Civil Engineering National Institute of Technology Jamshedpur Jharkhand Jamshedpur 831014 India Department of Electrical Engineering National Institute of Technology Jamshedpur Jharkhand Jamshedpur 831014 India Department of Computer Science and Engineering National Institute of Technology Jamshedpur Jharkhand Jamshedpur 831014 India

The field of construction materials and sustainable infrastructure has witnessed a significant shift towards environmentally friendly alternatives. Geopolymers, as an emerging class of materials, have gained substantial attention due to their eco-friendly nature and mechanical properties. One of the key ingredients in geopolymer formulations is metakaolin, a thermally activated aluminosilicate material derived from kaolin clay. This paper delves into an in-depth exploration of the diverse applications of fly ash and metakaolin in geopolymer concrete, highlighting its contributions to the construction industry, environmental sustainability, and technological advancements. In this investigation, the fly ash was substituted in varying proportions of 0%, 10%, 20%, 30%, and 40% across NaOH molar concentrations of 1 M, 3 M, 5 M, and 7 M. The objective was to assess the mechanical characteristics of the newly formulated geopolymer concrete. As consider the scenario where the concentration of NaOH was maintained at 5 M. In this case, the compressive strength of a geopolymer concrete (FM2), which comprised 20% metakaolin, demonstrated a maximum compressive strength of 55.28 MPa. Notably, the influence of metakaolin on bolstering compressive strength seems to be most noticeable when employed as a substitute at the 20% proportion. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Compressive strength Fly ash Geopolymer Metakaolin Sodium hydroxides

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Automated Resume Classification System Using Ensemble Learning 9

Automated Resume Classification System Using Ensemble Learni...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Spoorthi, M. Indu Priya, B. Kuppala, Meghana Karpe, Vaishnavi Sunilkumar Dharavath, Divya Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350397376

One of job recruiters' biggest challenges is selecting a suitable resume from the pool of resumes. For a single role, thousands of candidates send their resumes. Manually selecting the resume from a large number of applicants and assigning them suitable positions is time taking and not feasible. An automated system can make this process easy and efficient. This system takes candidates' resumes in word, pdf, or any format and classifies them according to the skill set mentioned in the resume. We propose an ensemble deep-learning model to classify the resume. © 2023 IEEE.

关键词： Deep learning

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