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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Parth Dhananjay Akre Utkarsha Pacharaney Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

As artificial intelligence continues to advance at an unprecedented pace, the selection of programming languages significantly affects development processes, workflows, and outcomes. Mojo, a novel programming language, has gained attention through its integration of Python-like syntax, Rust-inspired safety features, and superior performance characteristics. This paper evaluates Mojo across four critical dimensions: distinctive features, performance metrics, ecosystem maturity, and practical applications, while examining potential adoption barriers. Positioned at the intersection of high-level programming simplicity and low-level performance optimization, Mojo demonstrates particular utility for artificial intelligence developers requiring both flexibility and computational efficiency. Through analysis of prominent libraries, including Endia, Lightbug HTTP, Basalt, and ***, this study examines Mojo's capabilities and limitations. By presenting a comparative analysis of Mojo's features, emerging ecosystem, and applicability to artificial intelligence development, this research addresses the current literature gap and provides substantive insights into this emerging programming language.

关键词： Reviews Ecosystems Syntactics Programming Libraries Real-time systems Safety artificial intelligence Optimization Python

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Enhancing Interpretability: The Role of Explainable AI in Healthcare Diagnostics

Enhancing Interpretability: The Role of Explainable AI in He...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Nikita Zade Meher Langote Prateek Verma Department of Artificial Intelligence & Data Science Faculty of Engineering & Technology Datta Meghe Institute of Higher Education (DU) Sawangi Maharashtra India Department of Artificial Intelligence & Machine Learning Faculty of Engineering & Technology Datta Meghe Institute of Higher Education (DU) Sawangi Maharashtra India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

XAI is now transforming the use of AI in diagnosing diseases by overcoming some of the problems inherent in most black-box approaches. In time-sensitive speciality areas like computer-aided diagnosis, image analysis, and predictive modelling, SHAP, LIME, and attention-based XAI techniques explain how AI decides to help doctors and other healthcare professionals trust AI. Therefore, this paper aims to clarify why XAI is needed in healthcare, introduce techniques, and show their examples in cancer diagnosis and disease prognosis. XAI improves diagnostic performance and satisfies regulatory and ethical requirements for increased usage in clinical practice. Because of XAI, clinicians can use the best of artificial intelligence to complement their knowledge in enhancing the treatment of patients. That is why the outlook of XAI in healthcare includes the next steps in achieving higher scalability, managing bias, and working with diverse regulatory frameworks, which will define the future of AI solutions in healthcare.

关键词： Renewable energy sources Explainable AI Scalability Closed box Medical services Predictive models Regulation artificial intelligence Prognostics and health management Cancer

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Attention-Based Residual Dense Shrinkage Network for ECG Denoising

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Computer Modeling in engineering & sciences 2024年第3期138卷 2809-2824页

作者： Dengyong Zhang Minzhi Yuan Feng Li Lebing Zhang Yanqiang Sun Yiming Ling Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer and Artificial Intelligence Huaihua UniversityHuaihua418000China School of Computer National University of Defense TechnologyChangsha410073China New Energy Centralized Control Center CHN Energy Hunan Power New Energy Co.Ltd.Changsha410000China

Electrocardiogram(ECG)signal is one of the noninvasive physiological measurement techniques commonly usedin cardiac ***,in real scenarios,the ECGsignal is susceptible to various noise erosion,which affectsthe subsequent pathological ***,the effective removal of the noise from ECG signals has becomea top priority in cardiac diagnostic *** at the problem of incomplete signal shape retention andlow signal-to-noise ratio(SNR)after denoising,a novel ECG denoising network,named attention-based residualdense shrinkage network(ARDSN),is proposed in this ***,the shallow ECG characteristics are extractedby a shallow feature extraction network(SFEN).Then,the residual dense shrinkage attention block(RDSAB)isused for adaptive noise ***,feature fusion representation(FFR)is performed on the hierarchicalfeatures extracted by a series of RDSABs to reconstruct the de-noised ECG *** on the MIT-BIHarrhythmia database and MIT-BIH noise stress test database indicate that the proposed scheme can effectively resistthe interference of different sources of noise on the ECG signal.

关键词： Electrocardiogram signal denoising signal-to-noise ratio attention-based residual dense shrinkage network MIT-BIH

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artificial Neural Network and Random Forest Machine Learning Algorithm Based TCSC Controllers for Mitigating Power Problems

Artificial Neural Network and Random Forest Machine Learning...

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作者： Agrawal, Niharika Khan, Faheem Ahmed Gowda, Mamatha Department of Electrical and Electronics Engineering Ghousia College of Engineering Karnataka Ramanagaram562 159 India Department of Artificial Intelligence and Data Science BGS College of Engineering and Technology Mahalakshmi Puram Karnataka Bengaluru560 086 India

The demand for electrical energy is rising every year. There is a need for more generation and transmission facilities which require huge investment in the erection of new power stations and lines. The conventional sources of energy are coal, oil, fossil fuel, and nuclear energy-based power plants which are fast depleting. The coal-based plants release different gases like nitrogen dioxide, sulfur dioxide, particulate matter (PM), mercury, and other substances which are very harmful to human life as these gases pollute the atmosphere. The erection of new power plants is costly and affects the environemnt too. The existing transmission line was overloaded which created stability problems in the system. Fixed capacitors (FC) were used in the transmission lines to meet this problem but there were problems like series resonance, wear and tear and slow response. Here comes the role of Power -Electronics based FACTS device Thyristor Controlled Series Capacitor (TCSC) to provide variable compensation and to mitigate these power problems and challenges without the need for investment in the construction of new lines. Traditionally Proportional Integral (PI) controllers have been used for power flow control by TCSC. In the present work the artificial Neural Network (ANN) technique and Random Forest Machine Learning Algorithm (RFMLA) which comes under the umbrella of artificial intelligence have been applied to enhance the power flow transfer capacity of the Kanpur Ballabhgarh transmission line. Power obtained at the receiving end is more with an ANN-based controller. Power obtained is furthermore by the use of RFMLA. The existing power system is utilized more effectively, the efficiency of the system is enhanced, the losses are reduced with modern technology enabled/based TCSC. Thus the objective of meeting the shortage of power without harming the environment is achieved with ANN and RFMLA-based TCSCs. The TCSC based on ANN and RFMLA is successful in meeting various other p

关键词： MATLAB

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Particle Swarm Optimization based Detection of Diabetic Retinopathy using a Novel Deep CNN 3

Particle Swarm Optimization based Detection of Diabetic Reti...

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3rd International Conference on artificial intelligence and Smart Energy, ICAIS 2023

作者： Revathi, B. Elizabeth, S.K.Kezial Nagaraj, P. Birunda, S.Selva Nithya, D. Ramco Institute of Technology Department of Artificial Intelligence and Data Science Virudhunagar India Mangayarkarasi College of Engineering Department of Computer Science and Engineering Madurai India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar India Theni Kammavar Sangam College of Technology Department of Computer Science and Engineering Theni India

ISBN: (纸本)9781665462167

Diabetic Retinopathy (DR) is a disease. Diabetic patients are mostly affected by this disease. This causes damage to the eyes. Most probably the disease affects both the back of the eye and the blood vessels of the light-sensitive. Initially, this DR disease causes only minor problems with vision. But eventually, the eyesight will go away. This condition may occur in any diabetic patient. When the disease is more affected, the blood sugar level is high. DR detection is the application of image processing to medical examinations. To diagnose DR the retinal images should be evaluated. However, it takes a lot of time and resources to physically assess the images so that the severity of the DR can be removed. The blood vessels in the retina are damaged due to this problem can be noticed. When it starts to bleed the small vessels, then it forms a way on the retina. To overcome the challenges of DR, CNN with PSO is introduced. There are different stages implicated in the diabetic retinopathy detection technique. They are pre-processing, optimization and classification. The main objective of the proposed work is to use the CNN algorithm to analyze the disease that seems to be most affected and classify and report only that area from the given input. PSO is used to optimize the given classified images, and then finally PSO with CNN technique will produce accurate results. © 2023 IEEE.

关键词： Blood vessels

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Leveraging Bayesian Networks for Enhanced Transparency in Financial data Provenance Tracking

Leveraging Bayesian Networks for Enhanced Transparency in Fi...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Nithya, K. Selvan, Mary Gnanavel, V.K. Solainayagi, P. Venkatesh, K. Department of Electronics and Communication Engineering Chennai India R.M.K College of Engineering and Technology Department of Artificial Intelligence and Data Science Puduvoyal India Asan Memorial College of Engineering and Technology Department of Computer Science and Engineering Korea Republic of Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India Kalasalingam Academy of Research and Education Department of CSE India

ISBN: (纸本)9798331542344

This work describes a probabilistic model based on Bayesian Networks for improving the record-keeping process of financial data and, thus, increasing the traceability of such information. To enhance fraud control and data provenance, the proposed system captures the probabilistic relationships between financial variables to enhance the identification of anomalous behaviours. The validation of the system was performed on the large data set of financial transactions and the system claimed an accuracy of 91 percent. 5%, precision of 89.2%, and recognition of 87. A special ranking of 8% in fraud detection was achieved, which is higher than the Decision Tree model with 85.3%and Random Forest model with 88. 7%. The performance of the Bayesian Network in terms of tracing was evaluated with the results for a single transaction being 28 ms, and for a sequence of transactions being 134 ms which is slightly slower than the traditional method but the advantage of utilizing a BN is its good level of interpretability. The system also obtained a True Positive Rate (TPR) of 89 percent. 8%) False Positive Rate 5%. 1% more than its rivals which proves its efficiency in the detection of frauds. Worst case performance analysis revealed the fact that though the framework is highly efficient for small to medium amounts of data the time required for processing data increases with the increase in the size of the data. There is also an aspect of interpretability which The Bayesian Network received a Score of 9. by financial experts only, it received a 5/10 score mainly on issues regarding the organization's decision making which is transparent. These outcomes show that Bayesian Networks are a stable and understandable solution for elements of financial data traceability, especially in highly responsible financial systems as applied in ISE. Further research will be directed towards achieving higher scalability for large scale use. © 2024 IEEE.

关键词： Decentralized finance

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Enhancing Agricultural Precision: A Hybrid CNN-SVM Approach for Multidimensional Weed Disease Classification 5

Enhancing Agricultural Precision: A Hybrid CNN-SVM Approach ...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Upadhyay, Deepak Manwal, Manika Deep, Pragya Kukreja, Vinay Sharma, Rishabh Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science and Engineering Uttrakhand Dehradun India Iimt College of Engineering Artificial Intelligence & Data Science Uttar Pradesh Greater Noida India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350383522

A new category of weed disease classification in precision agriculture based on a hybrid model using convolutional neural networks (CNN) and support vector machine (SVM) has been presented in the proposed study. In line with overcoming the shortcomings of conventional disease detection approaches, this study introduces a machine learning solution that improves accuracy and productivity in the identification of weed diseases. The hybrid model utilizes the strengths of CNNs for feature extraction and its power with SVM in classification, yielding a stable system that enables multilevel classification of weed disease severity. Creation of a complete collection of weed disease image datasets for the training, validation, and testing phase. The findings from the study show that the use of the model outperforms standalone CNN & SVM models as it achieves a substantial increase in 96.21% accuracy across all the severity levels. The model also uses Grad-CAM visualizations to ensure interpretability and enable a better understanding of the decision-making process. The results imply that the proposed hybrid CNN-SVM model is both efficient in terms of weed disease classification and feasible for use across a range of agricultural settings. This research provides a valuable tool for the development of precision agriculture, which enables early and precise intervention in plant diseases that positively impact sustainable farming. Future work may include increasing the application potential of the model and investigating its integration into automated agricultural systems. © 2024 IEEE.

关键词： Deep Learning Performance Analysis Support Vector Machine Weed Diseases

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Classification of Electroencephalogram Signals Using LSTM and SVM Based on Fast Walsh-Hadamard Transform

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Computers, Materials & Continua 2023年第6期75卷 5271-5286页

作者： Saeed Mohsen Sherif S.M.Ghoneim Mohammed S.Alzaidi Abdullah Alzahrani Ashraf Mohamed Ali Hassan Electronics and Communications Engineering Department Al-Madinah Higher Institute for Engineering and TechnologyGiza12947Egypt Department of Artificial Intelligence Engineering Faculty of Computer Science and EngineeringKing Salman International University(KSIU)South Sinai46511Egypt Department of Electrical Engineering College of EngineeringTaif UniversityTaif21944Saudi Arabia Department of Electronics and Communications Engineering October High Institute for Engineering and Technology6th of October CityGiza12596Egypt

Classification of electroencephalogram(EEG)signals for humans can be achieved via artificial intelligence(AI)***,the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic *** this perspective,an automated AI technique with a digital processing method can be used to improve these *** paper proposes two classifiers:long short-term memory(LSTM)and support vector machine(SVM)for the classification of seizure and non-seizure EEG *** classifiers are applied to a public dataset,namely the University of Bonn,which consists of 2 classes–seizure and *** addition,a fast Walsh-Hadamard Transform(FWHT)technique is implemented to analyze the EEG signals within the recurrence space of the ***,Hadamard coefficients of the EEG signals are obtained via the ***,the FWHT is contributed to generate an efficient derivation of seizure EEG recordings from non-seizure EEG ***,a k-fold cross-validation technique is applied to validate the performance of the proposed *** LSTM classifier provides the best performance,with a testing accuracy of 99.00%.The training and testing loss rates for the LSTM are 0.0029 and 0.0602,respectively,while the weighted average precision,recall,and F1-score for the LSTM are 99.00%.The results of the SVM classifier in terms of accuracy,sensitivity,and specificity reached 91%,93.52%,and 91.3%,*** computational time consumed for the training of the LSTM and SVM is 2000 and 2500 s,*** results show that the LSTM classifier provides better performance than SVM in the classification of EEG ***,the proposed classifiers provide high classification accuracy compared to previously published classifiers.

关键词： Electroencephalogram LSTM SVM fast Walsh-Hadamard transform seizure accuracy sensitivity specificity

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Feature fusion-based weighted broad learning system for imbalanced data 8

Feature fusion-based weighted broad learning system for imba...

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8th IEEE Information technology and Mechatronics engineering Conference, ITOEC 2025

作者： Li, Yanting Wang, Yusha Jin, Junwei Philip Chen, C.L. Zhengzhou University of Light Industry College of Computer Science and Technology Zhengzhou China Henan University of Technology College of Artificial Intelligence and Big Data Zhengzhou China South China University of Technology School of Computer Science and Engineering Guangzhou China

ISBN: (纸本)9798331517915

Broad Learning System (BLS), characterized by its flat architecture as an efficient neural network, has garnered significant interest for its benefits in terms of training velocity and network scalability. However, BLS does not perform well when dealing with imbalanced classification problems. First, conventional BLS neglects the intrinsic logical information contained in the original input data, which may be critical for classification and prediction. Second, BLS fails to emphasize the significance of minority classes in imbalanced data scenarios. In this paper, we propose a feature fusion-based weighted broad learning system. Unlike traditional BLS, we combine the original features and broad features as fused features for training. Additionally, we introduce a weight generation mechanism that assigns higher weights to minority classes, enhancing the model's focus on these classes. A closed solution is available for the issue, and the transformation matrix can be effectively resolved. The experimental results on multiple imbalanced datasets show that our method is superior to other imbalanced methods. © 2025 IEEE.

关键词： Calibration

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A Novel Breast Cancer Detection and Classification Using Enhanced ANN Classification 5

A Novel Breast Cancer Detection and Classification Using Enh...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Mathiyalagan, P. Kiruthiga, G. Rasmi, A. Vadivel, M. Mahalakshmi, K.V. Poornappriya, T.S. Sri Ramakrishna Engineering College Department of CSE Coimbatore India Karpagam College of Engineering Dept of Artificial Intelligence and Data Science Coimbatore India RR Institute of Technology Information Science and Engineering Bangalore India Excel Engineering College Department of Information Technology TamilNadu Namakkal India B v Raju Institute of Technology CSE Telangana 502313 India Aspire Systems Pvt Ltd Data and Analytics Karnataka Bengaluru560037 India

ISBN: (纸本)9798331518097

The second most prevalent and leading cause of cancer death is breast cancer, which is the most common malignancy among women. Because the causes of breast cancer are not fully known, there is currently no reliable technique to prevent it. In recent years, breast cancer has become a widespread condition among women. Over the past several years, the issue of breast tumour categorization has become a significant issue. This article offers logical reasoning for the order in which to identify malignant lesions, a significant development in the mammography diagnosis of breast cancer. As a result, we concentrate on studying and analysing cancer imaging, with a particular emphasis on breast cancer. Mammography produces and processes pictures that are used in breast cancer detection. Sensitivity, specificity, and classification accuracy are the metrics used to assess the experimental findings. Results of an improved ANN classifier for differentiating between normal and abnormal cases in pictures from applied advanced mammography. © 2024 IEEE.

关键词： Lung cancer

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