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4th International Conference on Signal Processing and Communication, ICSPC 2023

作者： Virani, Jash Daredi, Nikita Bhanushali, Aayush Shukla, Madhu Shah, Pooja Marwadi University Computer Engineering Big-Data Analytics Department Gujarat Rajkot India Marwadi University Computer Engineering Artificial Intelligence Department Gujarat Rajkot India Pandit Deendayal Energy University Cse Department Gujarat Gandhinagar India

ISBN: (纸本)9798350300772

The rise in mental health problems among people and the need for effective solutions for mental health have led to a lot of research in the machine learning domain for its applications in mental healthcare Many people's lives have been affected by the exponential rise in mental health and depression, and various factors must be revised. Different situations can have an impact on one's mental stability. So, taking the factors in mind like gender, age, insomnia, loneliness, depression level, aggressiveness, stress level etc. we are going to analyze the most affecting factors for poor mental health. Nowadays mostly people are facing some kind of mental health problems but very few people are giving attention to their mental health. So, our main purpose is to analyze the factors and find those factors which are responsible for a person's poor mental health. Those factors are also anonymous by the person itself and They don't even know that they have weak mental health. For that we have taken the initiative to analyze and point out the main causes for poor mental health by circulating a google form Which has questions regarding regular lifestyle and from that we have collected data of different types of persons who are from different professions like students, employees, professors etc. After collecting the data, we had done preprocessing on that data and then we applied machine learning algorithms for classification and prediction. By applying machine learning algorithms, we found different types of outputs and by taking those outputs we can say which are more affecting factors on a person's mental health and we can suggest how people should improve their mental health and what type of things they should avoid for their good mental health. © 2023 IEEE.

关键词： Machine learning

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The application and comparison of Deep Learning models for the prediction of chest cancer prognosis

The application and comparison of Deep Learning models for t...

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2023 International Conference on Smart Applications, Communications and Networking, SmartNets 2023

作者： Hasan, Mahadi Al Mamun, Miraz Das, Madhab Chandra Hasan, Md Maruf Islam, Asm Mohaimenul The University of Tennessee at Chattanooga Chattanooga Ms in Data Analytics Tennessee United States Christus Health Health Outcomes & Analytics IrvingTX United States University of Information Technology and Sciences Electrical and Electronic Engineering Dhaka Bangladesh University of South Dakota Department of Computer Science VermillionSD United States

ISBN: (纸本)9798350302523

Lung cancers of all varieties, esophageal cancers, and cancers of the mediastinum (the area between the lungs), pleura (the membrane lining the chest cavity and surrounding the lungs), trachea, thymus gland, and heart are all classified as chest cancers, often known as thoracic cancers. Chest cancer can also spread from cancers that start in other places of the body. Chest pain is one of the usual signs of chest cancer, including hemoptysis or a cough that produces blood. Also, Coughing that hurts or a cough that does not go away is a sign of chest cancer. Mesothelioma, a cancer that begins in the lining of the chest or abdomen, frequently affects the lungs and other thoracic organs and tissues, which has prompted us to continue with this disease so that this research will aid in early detection. Chest X-rays and computed tomography (CT) pictures are the two diagnostic techniques that are most frequently utilized for these disorders. This study suggests a multiclassification deep learning model for detecting chest cancer using a dataset of chest CT-Scan pictures. While a chest CT scan is helpful even before symptoms show up and precisely detects the aberrant features that are found in images, a chest X-ray is less effective in the early stages of the ***, employing these kinds of photos will improve classification precision. To the best of our knowledge, no deep learning model in the literature can choose between these disorders. The current work considers the effectiveness of three architectures - CNN, ResNet50, and DenseNet121 -. A thorough assessment of various deep learning architectures is performed using publicly available digital CT datasets with four classifications (Adenocarcinoma, Large Cell Carcinoma, Squamous Cell Carcinoma, and Normal). The study's findings revealed that the DenseNet121 model performs better than the three other suggested models. CNN demonstrated 56.19% accuracy, whereas ResNet50 demonstrated 56.51% accuracy. The DenseNe

关键词： Diseases

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Hybrid solver with deep learning for transport problem in porous media

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Discover Geoscience 2025年第1期3卷 1-14页

作者： Trifonov, Vladislav Illarionov, Egor Voskresenskii, Anton Petrosyants, Musheg Katterbauer, Klemens Artificial Intelligence and Data Analytics Aramco Innovations Moscow Russia Digital Petroleum LLC Moscow Russia Reservoir Engineering Division Saudi Aramco Dhahran Saudi Arabia

In this work, a hybrid solver with deep learning is proposed for numerical modeling of fluid flow in porous media. The classical simulation procedure is complemented with a neural network model to obtain an initial guess for fluid saturation that is closer to the solution in the Newton–Raphson iterative algorithm. The simulation setup is a 3-dimensional immiscible two-phase flow with fluid motion caused by multiple production and injection wells. Approximation of the initial guess with a neural network model accelerates the numerical modeling up to 14% in terms of nonlinear iterations. Extensive experiments with dynamic and static reservoir features revealed that improving predictive accuracy does not necessarily improve fluid modeling. Different training procedures (e.g., different loss functions) and feature spaces (e.g., more past time steps used) can lead to better prediction quality but a higher number of nonlinear iterations. These results demonstrate that not only the closeness to the solution, but also the spatial distribution of residuals affects the “quality” of the starting point in Newton’s method.

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Optimized Machine Learning for Classifying Colorectal Tissues

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SN Computer Science 2023年第5期4卷 461页

作者： Tripathi, Ashish Misra, Anuradha Kumar, Kuldeep Chaurasia, Brijesh Kumar Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Lucknow India Center for Data Analytics Bond Business School Bond University Gold Coast QLD Australia Department of Computer Science and Engineering Pranveer Singh Institute of Technology Uttar Pradesh Kanpur India

Due to numerous deaths, colon cancer treatment and diagnosis are viewed as societal and financial challenges. The most severe reason for death worldwide is colorectal cancer. The classification of colon cancer tissues through images is presented in this paper as a multifaceted task. Classifying an illness at a premature stage increases its chances of existence, as late detection can be mortal which results in metastasis and a poor prognosis. The microscopic examination and classification of infected colon tissue sample images is a complex task. Also, the failure to manually detect the abnormality in the tissue by a pathologist might increase the severity of the disease. With the aid of intelligent machines, and automated diagnosis the classification of tissues from images can be done in much less time. These algorithms can learn by analyzing the patterns in the images and support the pathologist in completing the task with greater accuracy. In this research article, we proposed a tuned machine learning model, with the application of five machine learning techniques (K-Nearest Neighbor, Decision Trees, Random Forest, Categorical Boosting, and Gaussian Naive Bayes) for accurately classifying histopathological colon cancer tissues images of National Center for Tumor diseases Bank. The results demonstrate that the Categorical Boosting model has the best performance and is the most viable approach (accuracy: 0.9067, F1-Score: 0.9053, specificity: 0.9739, and sensitivity: 0.9757). © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Colorectal tissue classification Differential-Box-Count Histopathological image classification Machine Learning

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Optimizing User Interaction with Combined Avatars in BIM Systems 15

Optimizing User Interaction with Combined Avatars in BIM Sys...

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15th IEEE International Conference on Cognitive Infocommunications, CogInfoCom 2024

作者： Horváth, Ildikó Berki, Borbála Baranyi, Péter Corvinus Institute for Advanced Studies Institute of Data Analytics and Information Systems Corvinus University of Budapest Budapest Hungary Hungarian Research Network HUN-REN Budapest Hungary University of Pécs Faculty of Engineering and Information technology Pécs Hungary

ISBN: (纸本)9798350378245

Integrating virtual reality with Building Information Modeling (BIM) has been recognized for its numerous benefits in enhancing visualization, spatial recognition, distance and real-time collaboration, and efficiency in construction projects. However, there is a growing interest in exploring the cognitive aspects associated with using different types of avatars in these systems. This paper explores the cognitive implications of avatars in BIM systems, examining how they influence user experience, perception, decision-making, and overall cognitive load. © 2024 IEEE.

关键词： Virtual environments

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Intent Identification Using Few-Shot and Active Learning with User Feedback 25th

Intent Identification Using Few-Shot and Active Learning wi...

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25th International Conference on Web Information Systems engineering, WISE 2024

作者： Yuvaraj, Senthil Ganesan Benatallah, Boualem Motahari-Nezhad, Hamid Reza Rabhi, Fethi School of Computer Science and Engineering University of New South Wales Sydney Australia Insight SFI Research Centre on Data Analytics Dublin City University Dublin Ireland UpBrains AI Inc. RestonVA United States Macquarie University Sydney Australia

ISBN: (纸本)9789819605729

Collaboration tools contain many intents in workplace conversation, and identifying these intents is important to increase workplace productivity. However, labelling these intents for a large collection of conversations is expensive and data availability for new intents is quite limited. The pre-trained models show outstanding results in text classification tasks and large language models have recently produced accurate models with few samples. We explored few-shot learning methods and active learning strategies for this problem. Our proposed method, "SetFit with AL" is a combination of Sentence Transformer Fine-tuning (SetFit) and active learning for intent identification. This method fine-tunes a sentence-transformer model to develop accurate models. The intent classification evaluation dataset was used to evaluate this method. The results show that our proposed method outperforms state-of-the-art large language model GPT-3.5 and is comparable to GPT-4. This method also can utilize user feedback to adapt to new data and develop personalized models. Thus, the contribution of this paper is that fine-grained intents are identified using minimal data and the model is adaptable based on user feedback. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Active learning

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Dermatological Diagnostics: A Unified Deep Learning Framework for Skin Lesion and Cancer Classification 1

Dermatological Diagnostics: A Unified Deep Learning Framewor...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kumar, Vinod Jain, Isha Dongla, Durga Sritha Rai, Shivangi Rastogi, Vani Kaushik, Eva Kaushik, Rohit George Washington University School of Engineering and Applied Science WashingtonDC United States Dr. Akhilesh Das Gupta Institute of Technology and Management Computer Science and Engineering Department New Delhi India Gandhi Institute of Technology and Management Computer Science and Engineering Department Andhra Pradesh Visakhapatnam India Data Scientist DXC Technology Noida India Data Analytics Program University of Illinois CS and Mathematics Department Springfield United States

ISBN: (纸本)9798350356816

Skin pathologies encompass a spectrum of conditions, with malignancies such as melanoma representing a critical diagnostic urgency. This investigation delineates the deployment of Convolutional Neural Networks (CNNs) for the classification of dermatological anomalies, benchmarking CNN diagnostic fidelity against dermatological expert evaluations. The study underscores the efficacy of CNN classifiers in expediting the diagnostic workflow for various cutaneous disorders. Advocating for an automated diagnostic framework, the research introduces a CNN-based system aimed at reducing human diagnostic load, accelerating diagnostic timelines, and enhancing survival outcomes. Utilizing advanced image processing algorithms and deep learning architectures, the research presents an automated classification system for skin pathologies, addressing both benign and malignant presentations. The classification matrix includes nine dermatological conditions: actinic keratosis, basal cell carcinoma, benign keratosis, dermatofibroma, melanoma, nevus, seborrheic keratosis, squamous cell carcinoma, and vascular lesions. The objective is to engineer a CNN model with robust diagnostic performance across a diverse lesion dataset, ensuring accurate identification and categorization of dermatological conditions. © 2024 IEEE.

关键词： Convolutional neural networks

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TMRE:Novel Algorithm for Computing Daily Reference Evapotranspiration Using Transformer-Based Models

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Computers, Materials & Continua 2025年第5期83卷 2851-2864页

作者： Bushra Tayyaba Muhammad Usman Ghani Khan Talha Waheed Shaha Al-Otaibi Tanzila Saba National Center of Artificial Intelligence(NCAI) Al Khwarizmi Institute of Computer Sciences(KICS)Lahore54890Pakistan Department of Computer Science University of Engineering and Technology LahoreLahore54890Pakistan Artificial Intelligence&Data Analytics Lab Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Reference Evapotranspiration(ETo)iswidely used to assess totalwater loss between land and atmosphere due to its importance in maintaining the atmospheric water balance,especially in agricultural and environmental *** estimation of ETo is challenging due to its dependency onmultiple climatic variables,including temperature,humidity,and solar radiation,making it a complexmultivariate time-series *** machine learning and deep learning models have been applied to forecast ETo,achieving moderate ***,the introduction of transformer-based architectures in time-series forecasting has opened new possibilities formore precise ETo *** this study,a novel algorithm for ETo forecasting is proposed,focusing on four transformer-based models:Vanilla Transformer,Informer,Autoformer,and FEDformer(Frequency Enhanced Decomposed Transformer),applied to an ETo dataset from the Andalusian *** novelty of the proposed algorithm lies in determining optimized window sizes based on seasonal trends and variations,which were then used with each model to enhance prediction *** custom window-sizing method allows the models to capture ETo’s unique seasonal patterns more ***,results demonstrate that the Informer model outperformed other transformer-based models,achievingmean square error(MSE)values of 0.1404 and 0.1445 for forecast windows(15,7)and(30,15),*** Vanilla Transformer also showed strong performance,closely following the *** findings suggest that the proposed optimized window-sizing approach,combined with transformer-based architectures,is highly effective for ETo *** novel strategy has the potential to be adapted in othermultivariate time-series forecasting tasks that require seasonality-sensitive approaches.

关键词： Reference evapotranspiration ETo transformer informer autoformer FEDformer timeseries forecasting self-attention

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Simplified Micropayment Mechanism to Eliminate the Risk of Double Payment in E-Commerce

Simplified Micropayment Mechanism to Eliminate the Risk of D...

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2023 International Conference on Advances in Intelligent Computing and Applications, AICAPS 2023

作者： Chahar, Narendra Kumar Singh, Krishan Pal Hussain, Muzzammil Central University of Rajasthan Dept. of Data Science and Analytics Ajmer India Malaviya National Institute of Technology Dept. of Computer Science and Engineering Jaipur India Central University of Rajasthan Dept. of Computer Science and Engineering Ajmer India

ISBN: (纸本)9798350333817

In recent years, the emergence of the Internet and E-commerce has steered significant growth in digital transactions. Businesses today need mobile wallets, credit and debit cards, and e-cash to digitize payments. Digital payment systems are in transition and promise amazing advancements, but they also pose many risks and as the number of online transactions is increasing tremendously, we need a security system that follows all security norms. In this paper, we study digital transaction systems and evaluate various components of E-commerce plat-forms to address the security of these services. We evaluate the attributes that affect the security of digital payment processes and identify several barriers that hinder their performance to propose a simplified payment mechanism for micro-payments that eliminates the double payment problem. © 2023 IEEE.

关键词： Online systems

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A Novel Hybrid AI Framework for Real-Time Prediction and Risk Assessment of Vector-Borne Epidemics using Spatiotemporal data 4

A Novel Hybrid AI Framework for Real-Time Prediction and Ris...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Munirathnam, Rajesh Maindola, Meenakshi Bhargav, H.K. Patil, Prachi Ramesh Bhatt, Pooja Natrayan, L. TechnoGen Inc. Data & Analytics Division NJ United States Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Department of Computer Science and Engineering Tumakuru India Jagdambha College of Engineering and Technology Department of Electronics and Telecommunication Engineering Yavatmal India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Computer science and Engineering Vadodara India Saveetha School of Engineering Simats Department of Mechanical Engineering Chennai India

ISBN: (纸本)9798350352931

Vector-borne epidemics constitute major threats to global health that call for improved modeling systems for managing diseases. In order to incorporate such factors into an actual model, a traditional forecasting process may fail to grasp the nature of disease transmission adequately. We present a hybrid AI system based on LSTM and GCN for forest bug outbreak detection in real time. The framework also integrated a spatial-temporal analysis of climate variability, vector population fluctuations, and human mobility with climate variables across 15 regions over a five-year period (2019-2023). The hybrid framework also produced superior results, achieving a 94.2% prediction rate for health risk disease outbreaks, compared to a 17.3% improvement in traditional machine learning. When used to distinguish between high-risk and low-risk zones, the model demonstrated the following metrics: sensitivity of 92.8% and specificity of 95.1%. Real-time risk assessment demonstrated 89.6% effectiveness in indicating early warning and the best prediction lead time average of 21 days. This framework significantly enhances the prediction of vector-borne diseases by offering a dependable, real-time platform for disease surveillance and risk assessment. Its high accuracy and early warning capabilities make the system a useful tool in epidemiological and public health campaigns and control measures. © 2024 IEEE.

关键词： Prediction models

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