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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Wanjari, Ketan Verma, Prateek Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Skin cancer is the most commonly reported type of cancer globally and one of the few cancers that can be effectively treated if detected in its early stages. Recent advancements in artificial intelligence (AI) have significantly improved skin cancer diagnosis through Machine Learning (ML) and Deep Learning (DL) models. This review explores ML and DL methodologies, including Random Forest, Convolutional Neural Networks (CNNs), Inception Networks, ResNets, and Support Vector Machines (SVMs), which have been widely used for automated diagnosis. However, existing AI-based systems face several challenges, including the need for balanced datasets, model interpretability issues, computational complexity, and difficulties in generalizing across diverse populations. Moreover, multimodal detection systems and Explainable AI (XAI) present additional challenges in making AI-driven diagnoses more reliable and transparent. This article discusses these challenges while also highlighting emerging techniques that integrate multiple data sources and individualized diagnostic tools to enhance precision. By addressing these challenges, this review aims to provide insights into the practical advancements and future directions of AI in skin cancer detection and treatment. © 2025 IEEE.

关键词： Adversarial machine learning

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A Review on the Applications of Machine Learning and Deep Learning Algorithms for Image Recognition 4

A Review on the Applications of Machine Learning and Deep Le...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Wanjari, Ketan Verma, Prateek Department of Computer Science and Engineering Faculty of Engineering and Technology Maharashtra Wardha442001 India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Modern image recognition has experienced dramatic improvements because of Machine Learning and Deep Learning algorithms together. This study investigates CNNs and SVMs for recognition enhancement while reviewing image recognition technologies extensively through literature. This paper demonstrates how the applications of healthcare and security systems and social media analysis influence society. New developments in technology have not resolved multiple unresolved obstacles such as data bias as well as computational complexity and privacy concerns together with real-time processing restrictions. The latest image processing techniques include ViTs alongside GANs and Few-Shot Learning but developers need to achieve better results in future improvements. The main goal of this research examines present-day advancements in ML and DL with a review of their capabilities as well as constraints before recommending future study paths to overcome problems encountered today. This paper evaluates both the future potential and benefits alongside drawbacks of ML and DL models applied to image recognition. © 2025 IEEE.

关键词： Adversarial machine learning

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Big data Analysis using Elasticsearch and Kibana: A Rating Correlation to Sustainable Sales of Electronic Goods

HighTech and Innovation Journal

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HighTech and Innovation Journal 2023年第3期4卷 583-591页

作者： Henderi, Henderi Irawatia, Ranty Indra, Indra Dewi, Deshinta Arrova Kurniawan, Tri Basuki Department of Informatics Engineering University of Raharja Indonesia Informatics Technical Department Universitas Budi Luhur Indonesia Faculty of Data Science and Information Technology INTI International University Nilai Malaysia Faculty of Science and Technology Universitas Bina Darma Indonesia Faculty of Technology and Information Science Universiti Kebangsaan Malaysia Malaysia

Big data collection involves enormous amounts of raw data. To boost the sustainability of corporate value and support business intelligence and decision-making systems, in-depth data analysis is necessary. The data storage, analysis, and visualization methods, as well as the discovery of patterns and linkages, all depend on extensive data analysis. This study aims to process datasets to learn things like how ratings impact market sales transactions and how much of an impact factor connected to consumers and items have on ratings. Elasticsearch and Kibana were used for the dataset processing. This study evaluated traits related to the test parameters using a variety of test procedures. The product is scored as a representation of the product types involved in the sales transaction, and the name is assessed as a reflection of the customer. Kibana and Elasticsearch, a full-text search engine, were used in this work to do extensive data analysis on data sets. It is a visualization tool that is employed in a controlled environment to evaluate how ratings impact market exchanges for electronic goods, and it offers suggestions. The study found a substantial relationship between electronic product sales on the Amazon marketplace from 2012 to 2018. It suggested the importance of buyer constituents as users and how different product categories relate to ratings in business transactions. © 2023, Ital Publication. All rights reserved.

关键词： Decision making

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Smart Home Prototype for Rain Prediction Using Artificial Neural Network Algorithm 11

Smart Home Prototype for Rain Prediction Using Artificial Ne...

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11th International Conference on Computer, Control, Informatics and its Applications, IC3INA 2024

作者： Ilmi, Muhammad Adam Fahmil Doewes, Afrizal Wiharto Wardani, Dewi Wisnu Cahyono, Hasan Dwi Department of Informatics Faculty of Information Technology Data Science Universitas Sebelas Maret Surakarta Indonesia

ISBN: (纸本)9798331542313

Unpredictable weather changes, such as sudden rain, can disrupt daily activities, particularly tasks like drying clothes. To address this challenge, this study presents a smart home system that forecasts rain using a trained Artificial Neural Network (ANN) algorithm. The system employs IoT sensors to collect five critical weather parameters—temperature, humidity, air pressure, wind speed, and light intensity. data from these sensors are used to train the Backpropagation ANN model. The practical application of this model is demonstrated through a prototype that integrates a smart house roof and a servo-driven clothesline system. When rain is predicted, the servo moves the clothesline under the roof;otherwise, it positions the clothesline outside. The system achieved an accuracy of 0.86334, validating its effectiveness in mitigating the impact of sudden weather changes on clothes drying. © 2024 IEEE.

关键词： Rain

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Implementation of CBDC System for the Customised Development of Finance Service System 5

Implementation of CBDC System for the Customised Development...

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

作者： Mehra, Devansh Vamsi, A. Sri Kavin, Prabhu Srm Institute of Science and Technology School of Computing College of Engineering and Technology Faculty of Engineering and Technology Department of Data Science and Business Systems Chennai India

ISBN: (纸本)9798350383522

CBDC-as-a-problem looks like a mania of interest from central banks and financial institutions all over the world. It may describe CBDC technology and CBDC technology development, the positive aspects of CBDC and its future perspectives as follows: The CBDC is linked to the existing banking systems in the past which was based on the past findings of the blockchain, DLT, etc., that possessed the qualities the previous ones did not have - security, trust, and transparency. Innovation, design and CBDC safety that falls below this paper are discussed in this section below. The paper proceeds to show several paths that central banks are taking to CBDC development given the regulation and changing dynamic of the financial world. However, the studies do not end there;information on the potential of CBDC for financial stability, monetary policy formulation trajectories, transaction privacy, and the role of banks is included. Given that in field of finance the CBDCs are becoming more and more important, this research paper shall ensure to provide consistent background to policymakers, financial institutions and society at large on CBDC and its structural implications. This guarantees that everyone is in the know so it will be possible for them to come up with compliant plans and decisions informed by the knowledge in this changing landscape that simply does not stop evolving. © 2024 IEEE.

关键词： CBDC DLT

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Optimizing Speech Feature Extraction: A Comparative Study of MFCC, MFDWC, and MFWPC for Integration with Xception Block-Based CNN in Speech Emotion 7

Optimizing Speech Feature Extraction: A Comparative Study of...

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7th International Conference on data science and Its Applications, ICoDSA 2024

作者： Ismail, Kevin Sihwi, Sari Widya Cahvono, Hasan Dwi Universitas Sebelas Maret Faculty of Information Technology and Science Data Department of Informatics Surakarta Indonesia

ISBN: (纸本)9798350365351

The experiments conducted in this research examines the performance of various mel-frequency based feature extraction modules such as Mel-Frequency Cepstral Coefficient (MFCC), Mel-Frequency Discrete Wavelet Coefficient (MFDWC), and Mel-Frequency Wavelet Packet Coefficient (MFWPC) on the Speech Emotion Recognition task. Empirical studies proved that MFDWC performs better than MFCC and MFWPC. Besides that, in the emotion classification module, this study tested the performance of Xception block as the convolutional building blocks that requires lower number of learnable parameters. The model with Xception Block has better efficiency against Residual Block as the compared building block. The final stage of the research resulted in the output of an ideal Convolutional Network architecture and settings that had been trained in a single-corpus Speech Emotion Recognition scenario on each of the Ravdess, EMOVO, and Emo-DB datasets. This study evaluates the performance of MFCC, MFDWC, and MFWPC as feature extraction modules for Speech Emotion Recognition, demonstrating that MFDWC outperforms the others © 2024 IEEE.

关键词： Emotion Recognition

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AI-Powered Personalization in E-Commerce 2

AI-Powered Personalization in E-Commerce

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Chaudhari, Aarti Hajare, Shital Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Sawangi442001 India

ISBN: (纸本)9798331528713

The nature of e-commerce is constantly changing how the business is carried out and how things are bought. It has been made possible to do so because of technological advancements and the capacity of the internet that goes as far as allowing the customers to view, buy and get products from the ease of their homes. The idea of e-commerce has developed to cut through both the retail environment and the interface of customer expectations. This is the outcome of artificial intelligence being adopted by the e-commerce sector, namely in the area of customization. More specifically, when AI algorithms are applied, machine learning is becoming more and more significant in e-commerce customization. AI technologies keep developing further and further which enables to analyse the detailed information about the shopping habits, interests and preferences of every client so that every e-commerce unit may be able to cater to every need of a customer. This document focuses on the AI methods related to the e-commerce personalization processes: machine learning, deep learning, and natural language processing (NLP) methods. It focuses on the procedures of gathering and analysing data because these are crucial in understanding the buying patterns of consumers and what they prefer. © 2024 IEEE.

关键词： Adversarial machine learning

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A Variational Inference-Based LSTM-Enhanced Deep Neural Model for Sequential Recommendations

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IEEE Access 2025年 13卷 95945-95962页

作者： Ngaffo, Armielle Noulapeu Kamgang, Inès Raïssa Djouela Maka, Ebenezer Maka Malong, Yannick University of Douala Laboratory of Computer Science Data Science and Artificial Intelligence National Higher Polytechnic School of Douala Douala Cameroon University of Buea Faculty of Engineering and Technology Department of Computer Engineering Buea Cameroon

Long Short-Term Memory (LSTM) networks are particularly useful in recommender systems since user preferences change over time. Unlike traditional recommender models which assume static user-item interactions, LSTM models capture sequential dependencies and temporal dynamics, making them ideal for personalized, session-based, and sequential recommendation tasks. However, traditional LSTM-based recommender systems do not model uncertainty since they perform deterministic predictions. Moreover, those recommender models exhibit poor generalization on small datasets, and poor-handle cold-start and data sparsity problems. To overcome those limitations, this paper presents an LSTM-based recommender model that leverages an improved deep matrix factorization to accurately address the data sparsity problem. In addition, an enhanced variational inference coupled to the Evidence Lower Bound Optimization is applied on LSTM unit layers to perform uncertainty-aware predictions. Thereafter, latent vectors obtained from LSTM networks are effectively involved in the sequential recommendation process. Experiments have been conducted on several real-world datasets to evaluate our proposal and the results show the proposed model presents significant performances compared to state-of-the-art models. © 2013 IEEE.

关键词： Deep neural networks

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Breast Cancer Detection using Ultrasound Images based and Convolutional Neural Network 6

Breast Cancer Detection using Ultrasound Images based and Co...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Langote, Meher Hiwase, Kunal Wande, Radha Tale, Abhay Kalekar, Harsh Gundewar, Swapnil Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Sawangi India Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Maharashtra Sawangi India

ISBN: (纸本)9798331522667

Breast cancer forms one of the most common types of cancer among women worldwide;therefore, it highlights the importance of having rapid and timely diagnosis methods. Here, we will survey the use of convolutional neural networks (CNNs) for mutually exclusive cancer detection in ultrasound images using detectors. A dataset of benign and malignant breast tumors visualized via ultrasound is taken as the base for the training and testing of the CNN model. The CNN architecture is arranged to select valuable features from the ultrasound pictures only, but not malignant lesions. The performance and the evaluation of the CNN model are done through metrics like accuracy, precision, recall, and F1 score. The findings confirm the efficiency of using the CNN here, as it shows the extent and solidity of reading textures with excellent precision and quick performance in breast cancer screening. This investigation is the advancement of computer-aided diagnostic tools for breast cancer screening. As medical practice sees these benefits, the efficiency of healthcare delivery and patient outcomes are bound to be optimized. © 2025 IEEE.

关键词： Ultrasonic imaging

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The Utilization of Google Cloud in the Healthcare Industry 2

The Utilization of Google Cloud in the Healthcare Industry

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Kapse, Rishikesh Gourshettiwar, Palash Faculty of Engineering and Technology Dept. of Artificial Intelligence and Data Science Maharashtra Wardha India Faculty of Engineering and Technology Dept. of Computer Science And Medical Engineering Maharashtra Wardha India

ISBN: (纸本)9798331505745

This essay's primary focus is on Google Cloud's use in healthcare and how it affects data management, teamwork, and cost-effectiveness. With a focus on Google Cloud, which improves the processing, storing, and accessibility of medical data, the study examines the trends, advantages, and persistent challenges that come with the increasing usage of cloud computing technology. These consist of remote patient monitoring, 'artificial intelligence,' and 'machine learning' applications in diagnostics, diagnostics, and personalized medicine. data privacy, security issues, and concerns with the HIPAA incident are among the problems that are discussed along with all the significant elements that could facilitate the cloud migration of healthcare. © 2025 IEEE.

关键词： Theranostics

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