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IEEE Punecon

作者： Anjali Sharma Aradhana Pratap Kishan Vyas Sashikala Mishra PRN:*** Department of Artificial Intelligence and Machine Learning Symbiosis Institute Technology Pune India PRN:*** Department of Artificial Intelligence and Machine Learning Symbiosis Institute Technology Pune India PRN:*** Department of Artificial Intelligence and Machine Learning Symbiosis Institute Technology Pune India Department of Computer Science and Engineering Symbiosis Institute Technology Symbiosis International (Deemed) University Pune India

ISBN: (纸本)9781665498982

The rise of multiple company competitors during the COVID-19 outbreak resulted in fierce competition among competing firms for new clients and the retention of current ones. As a result of the foregoing, exceptional customer service is required, regardless of the size of the organization. Furthermore, any company's ability to know each of its customers' desires will provide it an advantage when it comes to providing specialized customer care and establishing customized marketing plans for them. The term “Consumer Buying Behavior Analysis” refers to a comprehensive assessment of the company's ideal clients/customers. In this project, we're utilizing the K-Means Algorithm to divide clients into two groups: “Highly Active Customers” and “Least Active Customers.” Then, utilizing the Apriori Algorithm, we use Association Rule Mining to recommend the best goods to clients based on their purchasing history and associations. We take one step further and use Logistic Regression to validate our Clustering operation by doing Binary Classification with our clusters as the label, resulting in accuracy and an F1 score of 91%.

关键词： COVID-19 IEEE Sections Customer services Clustering algorithms Companies machine learning Behavioral sciences

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IoT Based Monitoring System for Health During Menstruation

IoT Based Monitoring System for Health During Menstruation

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Sentiment Analysis and Deep learning (ICSADL), International Conference on

作者： Chinmaya Khadse Shital Hajare Nishad Sheikh Roshni Dahule Priya Kate Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawani Wardha Maharashtra India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

The integration of IoT and ML brought forth new prospective solutions in menstrual health management, pertaining to continuous monitoring of physiological signals and personalization of insights for women. This article discusses the usage of IoT, smartphones, and cloud-based systems for tracking vital signs, temperature, pulse rate, and flow of menstruation that will enable pro-active management of the cycle. The use of ML algorithms, including supervised models-like Random Forest and Logistic Regression-and complex techniques like RNNs and LSTM networks-offers added advantages for predicting menstrual cycles and ovulation. These technologies offer personalized health recommendations, early diagnosis of menstrual disorders, and help in communicating with healthcare providers in a very short span of time. While offering huge potential to improve women's health, concerns around data privacy, ethical considerations, and equitable access to these digital health solutions are highlighted. The existing systems showed improvements yet they encountered problems because of insufficient data diversity and health system integration as well as insufficient personalization and imprecise cycle predictions. Data privacy issues and security threats that stem from ML models along with constant bias concerns prevent broad acceptance of ML systems in general use. This research study analyzes IoT and ML applications in menstrual health today and introduces their advantages as well as the barriers that require resolution before mass acceptance can occur. The proposal introduces an approach to create menstrual tracking software through a combination of rule-based systems with ML algorithms that generates precise forecasts alongside tailored information for women who have PCOS or PCOD.

关键词： Data privacy Ethics Software algorithms Prediction algorithms Software Internet of Things Biomedical monitoring Wearable devices Smart phones Monitoring

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Pushing the Boundaries of Combinatorial Graph Isomorphism Algorithms

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2022年第1期49卷 207-215页

作者： Balaji, N. Karthik, Pai B H Devidas Shastry, K Aditya Disha, D.N. Assistant Professor of Department of Information Science and Engineering NMAM Institute of Technology Nitte Karkala - 574 110 Karnataka Udupi India Professor of Department of Information Science and Engineering NMAM Institute of Technology Nitte Karkala - 574 110 Karnataka Udupi India Associate Professor of Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Karnataka Benagluru India Assistant Professor of Department of Artificial Intelligence and Machine Learning NMAM Institute of Technology Nitte Karkala - 574 110 Karnataka Udupi India

A computational problem well studied in the field of Complexity Theory is Graph Isomorphism. Color Refinement is a classical technique for testing the non-existence of isomorphism between two given graphs. Tinhofer’s algorithm is an extension of the Color Refinement algorithm and the same technique does not succeed for all graphs. We characterize Tinhofer graphs algebraically. We propose and study in detail, a new graph hierarchy based on Tinhofer’s algorithm which also prove for all the new graph classes in the hierarchy a sufficient condition. We look at the relations of the new graph classes with each other and also with the already known classes. We also facilitate an effective graph isomorphism algorithm for the lower classes in the hierarchy. Getting an algebraic characterization for the new hierarchy classes, understanding the exact location of other graphs in this hierarchy and extending the efficient algorithm for higher graph classes are identified as potential areas of future interest. © 2022. All Rights Reserved.

关键词： Color

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Optimized Constrained Application Protocol for IoT Smart Home Data Security: A Novel Lightweight Approach

Optimized Constrained Application Protocol for IoT Smart Hom...

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Recent Advances in Science and engineering technology (ICRASET), International Conference on

作者： R Subhashini Dg Jyothi Department of Computer Science and Engineering Bangalore Institute of Technology Affiliated to Visvesvaraya Technological University Karnataka India Department of Artificial Intelligence and Machine Learning Bangalore Institute of Technology Affiliated to Visvesvaraya Technological University Karnataka India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

By increasing simplicity and efficiency, a smart environment aims to enhance human existence. The IoT paradigm has evolved into a crucial technology for creating smart buildings, and the majority of computer networks now have IoT technologies. Nevertheless, during this shift, IoT devices are susceptible to cyberattacks. Thus, in practical smart home applications, security and privacy are key issues. In order to improve data security in smart home settings while lowering computing costs, this publication proposes the Broken-Stick Regressive Lightweight Speck Ephemeral Cryptography-based Constrained Application Protocol (BRLSC-CoAP). Real-time data is gathered by IoT devices and sent to authorized users. BRLSC-CoAP includes processes for authentication, encryption, decryption, and registration to safeguard this process. Forbes indexive Broken-stick regression is used to verify user authentication, and a lightweight Speck ephemeral cryptography technique is utilized for encryption. The experimental findings demonstrate that BRLSC-CoAP performs better than traditional techniques by increasing confidentiality, accuracy, and precision in authentication while lowering computing overhead, delay, and cost..

关键词： Privacy Protocols Costs Accuracy Smart buildings Authentication Smart homes Real-time systems Encryption Internet of Things

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Customer Segmentation Using Clustering Analysis

Customer Segmentation Using Clustering Analysis

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Information technology, Electronics and Intelligent Communication Systems (ICITEICS), IEEE International Conference on

作者： Moses Makuei Jiet Aahash Kamble Chetan Puri Prajyot Yesankar Prateek Verma Rajendra Rewatkar Department of Computer Science & Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India Department of Artificial Intelligence & Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India Department of Artificial Intelligence & Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India Department of Biomedical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

This research focuses on the crucial role of the clustering technique in data mining, specifically in market forecasting and planning. The study presents a comprehensive report on utilizing the k-means clustering technique and data visualization tools to develop a real- time system for a supermarket, predicting sales across various annual cycles. An open-source dataset of a mall with n = 200 customers divided into k = 3 groups, demonstrated high accuracy in predicting day-to-day sales statistics when compared with actual results. The research highlights the effectiveness of the clustering technique in real-time sales prediction for supermarkets.

关键词： Sociology Refining Decision making Data visualization Prediction algorithms Real-time systems Planning

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Hybrid Deep learning with TSOA Model for Predicting the Parkinson Disease using Speech Features

Hybrid Deep Learning with TSOA Model for Predicting the Park...

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Data Science and Information System (ICDSIS), IEEE International Conference on

作者： Piyush Kumar Pareek Pankaj Zanke Anoop N Prasad Mohan B R Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Senior Technical Business Analyst Sapiens International Corporation Department of Computer Science and Engineering East West Institute of Technology Bengaluru India

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

True early identification of Parkinson’s illness (PD) is absolutely fundamental to stopping the ill health course and planning affected role with access to disease-modifying medications. To do this, significant attention should be paid to the premotor stage of PD. Based on premotor characteristics, a unique deep-learning algorithm is provided to swiftly detect whether or not a person has PD. By identifying and assessing indicators that indicate speech attribute and degree of worsening, a voiced numerical biomarker may supplement the laborious traditional physical test. The project attempts to develop a revolutionary end-to-end deep learning model using speech data for Parkinson’s disease identification. The current study employs a mixed deep learning model to evaluate and identify PD. The study’s objectives were to identify vocal anomalies in PD patients and create an automatic test technique that can distinguish concerning PD patients’ voices and those of fit informs while also grading voice quality. his study investigates the effectiveness of utilizing deep learning architectures for speech data analysis in the context of [insert context]. Specifically, we employ a convolutional neural network (CNN) architecture with recurrent layers (e.g., Long Short-Term Memory, LSTM) to extract hierarchical features from raw speech signals. Our methodology involves preprocessing the raw audio data to extract relevant acoustic features, such as mel-frequency cepstral coefficients (MFCCs) and spectrograms. These features are then fed into the CNN-LSTM model, which learns discriminative representations for various speech tasks, including [mention specific tasks, e.g., speech recognition, emotion recognition, etc.].The category accurateness was evaluated using the Italian PVS speaking quantities and the Lithuanian PD voice dataset. With values of 0.92 showing that the results are medically adequate, the findings demonstrated that the suggested model is highly generalizable.

关键词： Deep learning Speech recognition Feature extraction Data models Numerical models Object recognition Convolutional neural networks

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Disease and Medications Text Visualization Using Scattertext

Disease and Medications Text Visualization Using Scattertext

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Rama Krishna, K. Sandhya, Kaipa Praveen Gujjar, J. Devadas, Raghavendra M. Hiremani, Vani Sapna, R. Impact college of Engineering and Applied Sciences Department of Artificial Intelligence and Machine Learning Bengaluru India Impact college of Engineering and applied sciences Department of Data Science Bengaluru India Faculty of Management Studies Bengaluru India Department of Information Technology Manipal India University Pune India

ISBN: (纸本)9798331527495

Before text data can be analysed and visualised, it must be thoroughly cleaned due to its messy nature. data visualizations use the data to tell an engaging and simple-to-read story. That is what the Scattertext tool does. Scatter text allows which terms stand out in your corpora and how their frequency varies across categories. The dataset used in this paper includes reviews of drugs. This dataset offers a useful window into how people discuss their thoughts and experiences using various drugs. The drug names, user ratings, and the actual written reviews are among the details included in the dataset. This dataset consists of drug reviews collected from various sources, hence expressing a wide range of opinions and feelings. Each row in this dataset represents the experience a user had with a given medication and hence allows us to explore those emotions that are associated with any particular set of drugs. Understanding opinion expressed in the review about drugs might go a long way in helping decision-making processes in both healthcare and pharmaceuticals. The scatter plot distinguishes between labelled points and unlabelled ones by using a custom algorithm for Scattertext. By hovering their cursor over the labelled areas done client-side, users can interactively see which terms correspond to which points. © 2025 IEEE.

关键词： Data visualization

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LSTM deep learning model for Alzheimer’s disease prediction based on cost-effective time series cognitive scores

LSTM deep learning model for Alzheimer’s disease prediction...

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Novel Intelligent and Leading Emerging Sciences Conference (NILES)

作者： Hager Saleh Nora El-Rashidy Tamer Abuhmed Shaker El-Sappagh Faculty of Computers and Artificial Intelligence South Valley University Hurghada Egypt Machine Learning and Information Retrieval Department Faculty of Artificial Intelligence Kafrelsheiksh University College of Computing and Informatics Sungkyunkwan University South Korea Faculty of Computer Science and Engineering Galala University Suez Egypt

Alzheimer’s disease (AD) is a complex chronic neurodegenerative disease that propagates over time. Deep learning (DL) models can be used to learn time series data to extract deep temporal features and make robust decisions. The fusion of multimodal time series data has been proven to enhance model performance [1]. For instance, cognitive scores (CSs) including clinical dementia rating, and Alzheimer’s disease assessment scores have been integrated with other modalities like MRI to predict the future status of AD patients [2]. They enhanced the performance of DL models. These scores can be collected easily and in a cost-effective way in hospitals. No study in the literature has built a deep learning model to predict AD based on the cost-effective and multimodal time series data of CSs. In this study, we propose an LSTM-based DL architecture to predict AD based on multiple time series CSs. The proposed model has been optimized using a Bayesian optimizer to select the best architecture, and it has been compared with multiple classical machine learning models like random forest and others. The proposed LSTM architecture achieved better results than other models and provided stable and robust performance.

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Partial Convolution U-Net for Inpainting Distorted Images

Partial Convolution U-Net for Inpainting Distorted Images

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International Conference on Computing and Networking technology (ICCNT)

作者： R Rashmi Adyapady B Annappa Prem Sagar Department of Artificial Intelligence and Machine Learning NITTE (Deemed to be University)NMAM Institute of Technology Nitte Karnataka India Department of Computer Science and Engineering DISCOVER Lab National Institute of Technology Karnataka Mangalore Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Image inpainting is a domain in which researchers have shown considerable interest, and when it comes to deep learning techniques, realistic problems become interesting and challenging. In image inpainting, a corrupted facial image with missing holes or significant holes can be restored and compared to the original image to see if it is real or fake. In addition to fixing the texture of the image and getting the image’s high-level abstract properties, it may also recover semantic images such as human faces. In the field of image-inpainting models, the Attention model with features learned through semantic approaches and progressive networks has become particularly popular. The proposed model introduces (i) Attention blocks in each decoder layer of U-Net architecture and (ii) a hybrid loss function leveraging both Mean Square Error (MSE) and Mean Absolute Error (MAE). The proposed Attention-based U-Net showed remarkable performance with SSIM and PSNR by 0.1067 and 13.63, respectively, compared to the previous approaches.

关键词： Deep learning Convolution Semantics Mean square error methods Predictive models Generative adversarial networks Decoding Image restoration Security Faces

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A Study on Integrating machine learning Techniques for Waste Management

A Study on Integrating Machine Learning Techniques for Waste...

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Ruchika Vaidya Rahul Dattangire Divya Biradar Prateek Verma Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Data Engineering Publicis Sapient Houston Texas USA Department of Computer Science University of Texas Arlignton Texas USA

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

The current study focuses on how various machine learning (ML) algorithms can be used in waste management and emphasizes the significance of intelligent technologies in addressing contemporary issues. By leading a broad writing survey, the paper features the meaning of incorporating these advances into waste management practices. The system segment subtleties the information assortment, preprocessing, model determination, and assessment measurements utilized in the review. The accuracy and performance metrics of various classifiers, including K Nearest Neighbors, Linear Support Vector Classifier, Logistic Regression, Decision Tree, Multi-Layer Perceptron, and Random Forest, are demonstrated by the results of experiments with them. Especially noteworthy is the MLP classifier's 91.81% accuracy. The discoveries highlight the viability of ML approaches in ssmart waste management. In addition, a comparison table that summarizes the classifiers' accuracy scores provides insight into their suitability for applications in waste management.

关键词： Waste management Surveys Productivity Measurement Accuracy Reviews Vectors

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