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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Sangeetha, M. Harin Fernandez, F. Mary Ganesh Ramachandran, A. Saravanan, S.K. Bhanumathi, M. University of Technology and Applied Sciences Department of Computer Engineering Nizwa Oman Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science Chennai India Saveetha School of Engineering Simats BigData and Network Security Department of Computers Science and Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350384277

Algorithmic advancements in machine learning have transformed drug discovery and pharmaceutical innovation. This study examines how machine learning might speed medication development and pharmaceutical innovation. Data from genomes, chemistry, and clinical trials is collected to start the research. Careful data cleansing, normalization, and feature engineering prepare the data for model building. Random Forest, SVM, NN, Decision Tree, and Logistic Regression are tested for prediction. ROC AUC results show that Random Forest is the most accurate model at 86%. Ensemble approaches have drug discovery potential. Beyond accuracy, models are assessed for precision, false positive, and false negative rates. Drug discovery relies on precision to measure positive predictions. The Random Forest model makes the most accurate positive predictions at 0.87. However, false positives and negatives also matter in drug discovery. The Decision Tree model contains the falsest positives (35), underlining the need to reduce them to save resources. The Neural Network approach reduces false negatives to 12, ensuring good medication candidates are not overlooked. The results show that different machine learning models have trade-offs, underscoring the necessity for a personalized strategy depending on objectives and restrictions. Algorithmic breakthroughs in pharmaceutical innovation reduce medication development time by 40% and cost by 25%, according to the study. © 2024 IEEE.

关键词： Drug discovery

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TS Fuzzy Prediction-based SVC Compensation of Wind Farms Flicker:A Dual-UKF Approach

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CSEE Journal of Power and Energy Systems 2022年第6期8卷 1594-1602页

作者： Saeedeh Ketabipour Haidar Samet Navid Vafamand School of Electrical and Computer Engineering Shiraz UniversityShirazIran Department of Electrical Engineering Eindhoven University of TechnologyEindhoventhe Netherlands

Fast fluctuations in wind farm power produce voltage flicker in the *** way to mitigate the flicker is to place a static VAr compensator(SVC).Due to the operating delay of SVCs,it is essential to predict the wind farm reactive ***,a novel fuzzy nonlinear modeling approach is suggested and used in the one-step-ahead prediction of the power *** base of the developed fuzzy modeling is the Takagi-Sugeno fuzzy representation and a dual-unscented Kalman filter(D-UKF).In other words,a nonlinear TS fuzzy system is trained online via the *** forecasted value is used as the SVC’s reference signal.A large amount of actual data gathered from a wind farm is used for the performance *** data is collected in winter and summer for different climate *** the actual data,a current source with changing amplitude and phase which is updated every half-cycle,is used to model the wind *** results,including the flicker indices,confirm the improvement in the SVC’s performance.

关键词： SVC voltage flicker wind farm

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Text Summarization for Research Papers using Transformers 9

Text Summarization for Research Papers using Transformers

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Mehta, Rutvi Mehta, Niyati Purohit, Vinay Saha, Ishani Mishra, Payal SVKMs Nmims Mukesh Patel School of Technology Management And Engineering Computer Engineering Department Mumbai India

ISBN: (纸本)9798350394474

Scientific research frequently begins with a thorough review of the body of previous work, which includes a wide range of publications. This study process might be shortened by automatically summarizing scientific publications, which would be of great use to researchers. Because scientific papers have a different structure and require citation phrases, summarizing them presents different issues than summarizing other forms of literature. Furthermore, algorithmic pseudocode, figures, tables, and other elements not typically present in generic text, often contain valuable content that is not readily visible in plain language. Our Research Paper assistant presents a comprehensive investigation into the development and analysis of the summary of a complex research paper leveraging a robust sequential model (seq2seq) using the Transformer model. We are performing a comparison between Extractive and Abstractive approaches as well to prove that this work highlights the necessity of Abstractive summary methodologies in research paper summarization, acknowledging the drawbacks of extractive methods. The complex structure and substance of research publications might be missed by extraction approaches, which results in a loss of context and coherence. Conversely, Abstractive summarization techniques provide an answer by producing unique, short, and cohesive text in addition to picking and rearranging sentences. In order to efficiently condense the huge amount of information and insights contained in research articles while preserving readability and informativeness, this shift towards Abstractive summarization is essential. © 2024 IEEE.

关键词： Text processing

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Brain Tumor Detection Using Xception Model

Brain Tumor Detection Using Xception Model

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2024 International Conference on Advances in Computing Research on Science engineering and Technology, ACROSET 2024

作者： Philip, Joshwa Joy Rani, Ruchi Kumar, Sumit School of Computer Engineering and Technology Dr Vishwanath Karad Mit World Peace University Department of Computer Science and Engineering Pune India School of Computer Engineering and Technology Dr Vishwanath Karad Mit World Peace University Department of Computer Engineering and Technology Pune India Department of Electronics and Telecommunication Maharashtra Pune India

ISBN: (纸本)9798350388800

Numerous morphological and histological characteristics of brain tumors make diagnosis and treatment very challenging. Traditional diagnosis methods often depend on the analysis of medical images, which can lead to unclear conclusions and errors. In recent years, machine learning (ML) and deep learning techniques (DL) have shown great scope in improving the detection and classification of brain tumors. These methods extract the features and classify medical imaging data, such as MRI, CT, and PET scans. Convolutional and recurrent neural networks give better performance even though there are challenges in the dataset. In the proposed work, Xception architecture is used which is a type of convolution neural network. This model is pretrained on the ImageNet dataset. The proposed model achieves 99.90% accuracy in brain tumor detection. © 2024 IEEE.

关键词： Convolutional neural networks

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The Effect of Artificial Intelligence on Data Security Systems 5th

The Effect of Artificial Intelligence on Data Security Syste...

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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Chaure, Suvarna Punjabi, Sunil Department of Computer Engineering SIES Graduate School of Technology Maharashtra Nerul India

ISBN: (纸本)9783031770807

Artificial intelligence (AI) in its many forms is leading the charge in spurring breakthroughs in digital security in response to the emerging challenges in the post-COVID environment. On the one hand, businesses are finding it challenging to handle security risks related to a range of concerns, including web domain, decision-making, quality control, and system openness, to name a few. However, to comprehend the relationship between AI and those problems, research over the past ten years has concentrated on security capabilities based on instruments like platform complacency, intelligent trees, modeling techniques, and outage management systems. The literature has long acknowledged how important artificial intelligence will be in directing industries and reshaping the transportation, health, and education sectors. This paper presents an Artificial Intelligence-based Security method for Banking Sector (AISBS). AI is one of the best technologies for mapping and preventing unforeseen hazards from consuming an organization. The proposed method can be used to categorize, and resolve cyberattack issues. Algorithms like the Enhanced Encryption Standard (EES) encrypt and decrypt data to guarantee the security of financial sector data. The K-Nearest Neighbor (KNN) algorithm generates predictions by using its training data to make predictions. © ICST Institute for computer Sciences, Social informatics and Telecommunications engineering 2025.

关键词： Decision making

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Enriching Transformer using Fourier Transform for Image Captioning 3

Enriching Transformer using Fourier Transform for Image Capt...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Joshy, Devika M Das, Abhijith Amijith, M.M. Sunil, Diya Theresa Safar, Seema Rajagiri School of Engineering and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350338607

Transformers have an encoder-decoder architecture which is widely used as an image captioning model. Identifying the key elements and the underlying relationships between them is the challenge of image captioning. The introduction of the Fourier transform in this architecture is expected to perform better than other state-of-the-art models. Fourier transform is a signal processing tool that allows us to decompose a signal into its frequency components. In signal processing, an image is often treated as a two-dimensional signal, where the brightness of each pixel represents the amplitude of the signal at that location. With this intuition, an additional Fourier layer is added to the transformer architecture to enhance its performance. The Flickr dataset is used for training the proposed model in its initial phase. The model is tested against state-of-art-model using various evaluation metrics like BLEU-n, meteor, rouge scores, etc. During the evaluation, the suggested approach produced better outcomes. © 2023 IEEE.

关键词： Fourier transforms

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Quantified emotion analysis based on design principles of color feature recognition in pictures

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Multimedia Tools and Applications 2024年第19期83卷 57243-57267页

作者： Chen, Chin-Ling Huang, Qing-Yang Zhou, Ming Huang, Der-Chen Liu, Ling-Chun Deng, Yong-Yuan School of Information Engineering Changchun Sci-Tech University Jilin Province Changchun130600 China Department of Computer Science and Information Engineering Chaoyang University of Technology Taichung City413310 Taiwan Xiamen Academy of Arts and Design Fuzhou University Fujian Xiamen361024 China School of Computer and Information Engineering Xiamen University of Technology Fujian Xiamen361024 China Department of Computer Science and Engineering National Chung-Hsing University Taichung402 Taiwan

When people observe pictures, different pictures will generate different emotions, and the painters often convey emotional energy to the audience through the media. Through the effect of this emotional transfer, people get emotional satisfaction psychologically, and the author can better express the emotion he needs to express by using the principle. For those who do not know art appreciation, it is difficult to accurately describe the feelings brought by pictures and describe them accurately. It is also difficult to quickly and accurately find the picture of the corresponding feeling sought by oneself. This paper presents a quantitative picture emotion analysis method. Based on the analysis of the characteristics and colors of the pictures that arouse people's emotions, the classification planning is carried out in RGB space and HSV color space respectively. And the combination analysis of the color features with strong influence is carried out to select a more appropriate color space for the expression of human feelings. At the same time, the paper also discusses the effect of the black-and-white filling and the brightness saturation of the image. Finally, it makes use of the corresponding design principles to carry out a quantitative analysis of the emotion generated by it. The system performs a combined analysis of a picture from these directions and presents the results to the user. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.

关键词： Color

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PUF Based Security for the Physical Hardware Devices of IoT Using White-Box Cryptographic Technique 4th

PUF Based Security for the Physical Hardware Devices of IoT ...

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4th International Conference on Advances in Information Communication Technology and Computing, AICTC 2022

作者： Makhija, Jigar Anantha Narayanan, V. Department of Computer Science and Engineering Amrita School of Engineering Coimbatore India

ISBN: (纸本)9789811998874

Hardware security for IoT systems has always been a concern to establish trust among its users and till now it doesn’t meet its reliable outcome, though many solutions are provided also. Using physical unclonable functions with WBC as a solution to secure the embedded firmware of the IoT device hardware gives a nominal level of security. To provide the solution to these issues, our approach gives the solution by implementation of White-Box AES cryptographic technique that can be integrated with the physical unclonable function (PUF) based key generation, this dynamic hash-key will be used to encrypt the firmware of embedded hardware as well as for device authentication which makes IoT devices more secure and reliable on SOC. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Innovative Approach for Early Heart Disease Prediction: Gaussian Mixture Model and K-Nearest Neighbour 3

Innovative Approach for Early Heart Disease Prediction: Gaus...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Surendran, R. Anandaraj, B. Deepa, P. Mageshwari, M. Balamurugan, K.S. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Madnapalle Institute of Technology & Science Andhra Pradesh Madanapalle India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer science and engineering Karpaga Vinayaga College of Engineering and Technology Tamil Nadu Chengalpattu India Department of Electronics and communication engineering Karpaga Vinayaga College of Engineering and Technology Tamil Nadu Chengalpattu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

An Electrocardiogram (ECG) contributes significantly to early diagnosis and classification of heart diseases, arrhythmia which means irregular heart rate. Regrettably, the process became difficult due to asymmetric and minimal amplitude from recordings of ECG and cannot do the extraction from multiple signals. By using machine learning technology, it can classify and extract multiple samples, but cannot extract the data features from complex data structures. To overcome the limitations, previously introduced metaheuristic optimization (MHO) to improve the classification and extraction. Proposed a method which supports and K-Nearest Neighbour (KNN) Gaussian Mixture Model (GMM) algorithms for determine the abnormalities, predicting cardiac events, and improve diagnostic accuracy. As a result, this can help us to get results more quickly and accurately with sensitivity 99.7% and accuracy 99.2%, for helping healthcare professionals in diagnosing arrythmias, myocardial infarction and any other heart conditions. © 2025 IEEE.

关键词： Adversarial machine learning

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Low Cost Active Learning Framework for Short Answer Scoring 20th

Low Cost Active Learning Framework for Short Answer Scoring

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20th Principle and Practice of Data and Knowledge Acquisition Workshop, PKAW 2024, held in conjunction with the 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Hori, Tatsuya Yamauchi, Koichiro Graduate school of Engineering Department of Computer Science Chubu University Kasugai Japan

ISBN: (纸本)9789819600250

Short answer scoring (SAS) that automatically scores learner’ answers based on given rubric. Because SAS uses different rubrics for different prompts, it is necessary to generate training data for each prompt, which is time-consuming and labor-intensive. We propose a human-in-the-loop framework that achieves SAS with a minimal amount of training data through active data creation and efficient training. We fine-tuned the Sentence BERT model with a small dataset by using SetFit (Lewis Tunstall, et al. (2022).) and then apply active learning (AL) to an unlabeled data pool. The grader scores the data selected by AL, creating new training data that are then used to further fine-tune the models. For the experiments, we used six prompts from a Japanese SAS dataset. Our experimental results demonstrate that the proposed method outperforms previous studies in most cases. We also examined the potential challenges when applying this framework in practical scenarios. This framework significantly reduces the graders’ time and effort by enabling automatic scoring with minimal training data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Active learning

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