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

作者： Sharma, Sumit Kumari, Surbhi Goyal, Suprateek Nirala, Ravikant Department of Computer Science and Engineering Uttar Pradesh India

ISBN: (纸本)9798350383522

The complex interactions between location, property attributes, prevailing economic conditions, and market trends define the real estate market, which is dynamic and prone to ongoing changes. In order to make educated judgments, buyers, sellers, investors, and legislators are among the stakeholders in the real estate ecosystem that understand how crucial precise price forecasting is. The goal of this project is to create a reliable and accurate real estate price prediction model by utilizing machine learning. The real estate market is known for its dynamism, which is influenced by a wide range of elements including location, property characteristics, prevailing economic conditions, and market trends. For many parties involved in real estate, including buyers, sellers, investors, and legislators, accurate price forecast is essential. The goal of this research is to use machine learning methods to build a solid and accurate real estate price forecast model. In order to conduct this study, we assemble a large dataset of historical real estate transaction records that includes a wide range of property qualities, geographic data, economic indicators, and market trends. To assure the data's quality and relevancy, we preprocess and sanitise it. In order to extract useful information from the dataset, feature engineering approaches are used. Exploratory data analysis (EDA) is then carried out to learn more about the distribution of the data and the relationships between the variables. © 2024 IEEE.

关键词： Algorithm Decision tree Linear Regression modules range SVM

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E-commerce Products Image Classification using EfficientNetB5 with Transfer Learning 7

E-commerce Products Image Classification using EfficientNetB...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Shah, Archi Goel, Parth Gandhi, Vaibhav C. Computer Science & Engineering Department Anand Changa India

ISBN: (纸本)9798350359299

The image classification is a research field where images are classified without human intervention. E-commerce image classification is the application of machine learning where techniques automatically categorize and label product images in E-commerce platforms. This study focuses on the complex task of e-commerce product image classification, which aims to develop a model capable of accurately classifying diverse products. This approach helps to take an acquired image from a designated device, decoding the acquired image, and applying a classification algorithm to categorize the products. The dataset has been organized into four categories, which are T-shirts, TVs, couches, and jeans. The E-commerce product dataset has been utilized with 796 images. The proposed work is based on transfer learning. In the proposed framework, pre-trained EfficientNetB5 model is used for fine-tuning for the e-commerce product classification task. The proposed framework achieved a 98% accuracy, 97.5% precision, 97% recall, and 97.75% F1- Score. © 2024 IEEE.

关键词： Image classification

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Multicriteria Decision Analysis for Optimal Internet Service Provider Selection Using Calibrated Random Forest 1

Multicriteria Decision Analysis for Optimal Internet Service...

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1st Asian Conference on Communication and Networks, ASIANComNet 2024

作者： Mazid-Ul-Haque, Md. Ahmed, Shakil Aftab, Rakin Sad Miah, Md Saef Ullah Akanda, Wahiduzzaman Bhowmik, Abhijit American International University-Bangladesh Department of Computer Science Dhaka Bangladesh Aarhus University Department of Computer Science Aarhus Denmark University of Delaware Department of Computer & Information Sciences Newark United States

ISBN: (纸本)9798350367003

The Internet is integral to modern life, with Internet service provider (ISP) offering appealing deals to meet the demand for unlimited data. However, reality often falls short of expectations. While recommendation systems exist, user-centric options are rare. This paper proposes a novel ISP selection methodology using user experience data and a calibrated random forest (CRF) model. Unlike traditional methods that focus on advertised features, this approach emphasizes user-defined criteria such as cost, device connectivity, and technical support experience. By analyzing survey data, the model highlights the critical link between user needs and support quality, enabling users to choose ISPs that prioritize customer service. The model demonstrates promising results with a strong R-squared value and low mean squared error (MSE). This user-centric approach fosters informed decision-making, potentially driving competition and encouraging ISPs to improve service standards, laying a foundation for future developments in ISP selection. © 2024 IEEE.

关键词： Mean square error

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Robust Relatable Explanations of Machine Learning with Disentangled Cue-specific Saliency 25

Robust Relatable Explanations of Machine Learning with Disen...

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30th International Conference on Intelligent User Interfaces, IUI 2025

作者： Abichandani, Harshavardhan Sunil Zhang, Wencan Lim, Brian Y Department of Computer Science National University of Singapore Singapore Singapore

ISBN: (纸本)9798400713064

Concept-based explanations help users understand the relation between model predictions and meaningful cues. However, under noisy real-world conditions, data perturbations lead to distorted and deviated explanations. We hypothesize that these corruptions affect specific cues rather than all, so disentangling them may help reduce model dependency on degraded cues. For the application of explaining emotional speech recognition, we propose RobustRexNet to explain with disentangled and discretized saliency maps for separate speech cues (e.g., loudness, pitch) to improve robustness against noise. Modeling evaluations show that RobustRexNet improved both model performance and explanation faithfulness in noisy and privacy-preserving distortions. User studies further indicate that the robust explanations aligned better with human intuition and improved user emotion labeling under noise. This work contributes toward robust explainable AI to improve user trust under real-world conditions. © 2025 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Patterns of Widespread Structural Abnormalities in Autism Spectrum Disorder using Diffusion Tensor Imaging

Patterns of Widespread Structural Abnormalities in Autism Sp...

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2024 International Conference on Communication, computer sciences and Engineering, IC3SE 2024

作者： Bharti, Dilip Kumar Singh, Sanjay Kumar Kumar, Mukesh Kanaujia, Vishal Kumar Aeri, Manisha Abes Engineering College Computer Science And Engineering-Data Science Ghaziabad India Kashi Institute Of Technology Department Of Computer Science & Applications Mirzamurad U.P Varanasi India Graphic Era Hill University Computer Science And Engineering Uttarakhand Dehradun248002 India Abes Engineering College Computer Science And Engineering Data Science Department Ghaziabad India

ISBN: (纸本)9798350366846

Diffusion tensor imaging (DTI) is a neuroimaging approach that lets in for the visualization and quantification of the structural integrity of white depend fibers in the brain. In latest years, DTI has come to be an essential tool for investigating the underlying neurobiological mechanisms of autism spectrum sickness (ASD), a neurodevelopmental disorder characterized via problems in social communication and repetitive behaviors. Through the usage of DTI, researchers have identified good sized styles of structural abnormalities in the brains of individuals with ASD. these abnormalities encompass changes in the microstructure of white remember fibers, along with decreased fiber density, changes in fiber directionality, and decreased connectivity between mind areas. Additionally, there may be evidence of bizarre mind development in early formative years, with a few research finding that these structural variations are present even in very younger children with ASD. One constant finding in DTI studies of ASD is reduced white rely integrity inside the corpus callosum, a large fiber package deal that connects the two hemispheres of the mind. This abnormality has been connected to deficits in social communiqué and language in people with ASD, as well as difficulties in integrating records from distinctive brain areas. The results showed extensive structural abnormalities in people with ASD in comparison to TD individuals. © 2024 IEEE.

关键词： Diffusion tensor imaging

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A survey on adoption of blockchain in healthcare

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International Journal of Business Intelligence and Data Mining 2022年第1-2期22卷 131-143页

作者： Shalini, K. Shantha Nithya, M. Department of Computer Science and Engineering VMRF India Department of Computer Science and Engineering VMKV Engineering College VMRF India

In this technology and automation era, blockchain technology travels in the direction of consistent studies and adoption in different sectors. Blockchain technology with a chain of the block provides security and establishes a trusted environment between individuals. In the past couple of years, blockchain technology attracted many research scholars, industrialists to study, analyse and apply the technology in their own application needs. The major advantage of blockchain technology is the security, user privacy preserved, transparency. The purpose of this proposed paper is to provide a survey on blockchain scope in healthcare providing high security of patient health information's during sharing and their impact to reduce the operational and capital investments. Also, this paper briefs on the new business opportunities in the health sector integrating blockchain technology. © 2023 Inderscience Enterprises Ltd.

关键词： Blockchain

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Comprehensive Large Language Model for Bathymetry Data 4

Comprehensive Large Language Model for Bathymetry Data

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Marina, T.S. Mampilly, Tessa S. Deepa, S. Vinay, M. Jayapriya, J. Christ University Department of Computer Science Bangalore India

ISBN: (纸本)9798331529635

Only 5% of the world's oceans have been explored, leaving the rest unexplored. Many incidents have been reported surrounding the mystery of random disappearances and intriguing flora and fauna found in these unexplored places. More than 80% of the oceans have not been mapped, so tracking becomes an uphill task. Existing work has been done to find a solution to tackle this rising issue. As Generative Artificial Intelligence(AI) is at the forefront of the race regarding advanced technology, projects have been taken up to leverage AI's capabilities to solve this issue. This paper proposes a framework that handles bathymetric data and harnesses it to empower ocean tracking advancements by incorporating large language models(LLMs). © 2024 IEEE.

关键词： Bathymetry

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Unleashing the Power of Feature Selection Techniques for High-Dimensional Cancer Data Classification 3

Unleashing the Power of Feature Selection Techniques for Hig...

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3rd Odisha International Conference on Electrical Power Engineering, Communication and Computing Technology, ODICON 2024

作者： Sahoo, Chetna Dash, Rasmita University Department of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350354379

The world has seen a definite increase in the number of Cancer cases which also indicates that there is requirement of techniques for diagnosis of this disease with every possible information available. This study works on deciding upon how to implement feature selection methods on high dimensional cancer data so as to make them suitable for classification. Use of Machine Learning in medical field can be utilized to resolve the diagnosis process with the only available numeric data that can lead to faster detection and treatment. A comparative study of various normalization and feature selection techniques are included to select the most appropriate methods. Datasets are considered of various variety that includes both binary and multiclass classification datasets, so as to make the model acceptable for diverse situations in cancer field that is whether detecting presence of cancer cells, stages of cancer or it's subtypes. Finalized with four normalization and four feature selection techniques. And for classification purpose three machine learning algorithms, which are Logistic Regression, Random Forest and Support Vector Machine (SVM) are used to evaluate four genomic datasets. Multiple combinations of these are used to find the best performing algorithm to train the model prepared for classification. © 2024 IEEE.

关键词： Adversarial machine learning

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The prediction of hairfall pattern in a person using artificial intelligence for better care and treatment 4

The prediction of hairfall pattern in a person using artific...

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Farooq, Sheikh Afaan Ali, Aleem Bashir, Anzah Chandigarh University Department of Computer Science Mohali India

ISBN: (纸本)9798350307757

Alopecia, also known as hair loss, is a term used to describe hair loss from the scalp or other parts of the body. It can be caused by a number of factors, such as genetics, hormonal fluctuations, illness or external stressors. The five typical types of hair loss are male pattern baldness, which is typically hereditary and hormonal, characterized by receding hairline and thinning of the crown, then female pattern baldness, which usually causes general thinning without noticeable receding of the hairline, especially the crown. Genetic and hormonal factors are involved. Next comes Alopecia Areata, which results in round bald spots caused by sudden patchy hair loss that the immune system has targeted in the hair follicle. Then there is Telogen Effluvium, which denotes temporary hair loss is caused by illness, stress or hormonal fluctuations, resulting in hair loss especially in the seam and hairline area. Finally, there is Traction Alopecia, which causes the hair to gradually fall out and results in tight combs or constant pulling. This study aims to utilize the AI-related methods to predict these above mentioned hair loss patterns. © 2024 IEEE.

关键词： Artificial intelligence

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Audio Description of Videos Using Machine Learning 9

Audio Description of Videos Using Machine Learning

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

作者： Roy, Robin Manepalli, Shreya Rajan, Shreeya Mishra, Sharanya Sampatrao, Gambhire Swati Pes University Department of Computer Science Bengaluru India

ISBN: (纸本)9798350394474

This research paper proposes an innovative approach leveraging machine learning techniques to enhance the accessibility of visual content, such as movies and videos, for individuals with visual impairments. Our strategy is based on the usage of Audio Description, which enables viewers to understand visual context by offering a thorough audio description of the scenes, activities, and related audio in the video. Our model's main goal is to produce voice output captions that succinctly and vividly explain the events as they happen in the video. We tried this method on several videos across several domains, and it achieved great accuracy in providing audio descriptions for these videos. Regardless of their visual ability, our method guarantees that people with visual impairments may completely understand and appreciate the information. By bridging the accessibility gap, our research contributes to the creation of multimedia content that is more inclusive and valuable for all viewers. © 2024 IEEE.

关键词： Long short-term memory

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