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

作者： Tejas Gawande Prateek Verma Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artifical Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

AI together with ML technology now provides detailed rapid medical data evaluation to enhance cancer diagnosis and treatment methods. Advanced algorithms installed in these technologies help medical staff identify cancer indicators which human experts commonly miss leading to better diagnostic accuracy. AI-powered Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scan analysis allows doctors to perform quicker and more efficient medical diagnosis. The usage of ML models enables medical professionals to detect prognostic trace elements thus they can enhance treatment choices by analyzing tumor cell biological classification groups. AI actively participates in protein and gene targeting together with clinical trial planning to move personal cancer treatment forward. AI applications face multiple obstacles that stop their effective adoption in the field of oncology. The majority of present-day barriers to AI implementation in oncology stem from problems with data privacy together with algorithmic biases and model validation methods while the high complexity of systems and decreasing model interpretability specifically hinder usage. The success of AI models depends on extensive and diverse datasets although the actual availability of relevant data sets combined with standardization procedures continues to be difficult. Medical diagnostic applications where AI performs its decision-making work present important transparency issues that create doubts about its effectiveness. The analysis of data privacy through federated learning and XAI for interpretability and transfer learning for efficiency and deep learning for accuracy improvement are recent solutions being researched to resolve these problems. The primary aim of this research explores how AI and ML influence cancer diagnosis together with treatment methods while studying present challenges alongside suggested methods to boost their operational performance. The combination of better data governance toget

关键词： Technological innovation Accuracy Federated learning Magnetic resonance imaging Oncology Data models Medical diagnosis artificial intelligence Medical diagnostic imaging Cancer

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Semantic Segmentation of Areal Images using Pixel Wise Segmentation

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Procedia Computer Science 2025年 259卷 463-472页

作者： Swathi Gowroju V. Swathi Narsimhulu K. Shilpa Choudhary Associate Professor Department of Artificial Intelligence & Machine Learning Sreyas Institute of Engineering and Technology Hyderabad Telangana Assistant Professor Department of Computer Science Sreyas Institute of Engineering and Technology Hyderabad Telangana Assistant Professor Department of Computer Science Matrusri Engineering College Hyderabad Telangana Associate Professor Department of Computer Science NGIT Hyderabad Telangana

Particularly in regions hit by flooding, precise semantic segmentation of aerial photographs is essential for post-disaster evaluation and urban planning. The high-resolution aerial photography collection FloodNet, created for pixel-by-pixel segmentation of flooded and non-flooded landscapes, is presented in this work. Aerial photos taken from various geographic regions make up the collection, and segmentation masks are used to detect flooded roads, houses, cars, plants, and other vital infrastructure. In order to conduct semantic segmentation on aerial photos and capture the fine-grained information necessary for flood damage assessments, the suggested model makes use of a U-Net architecture. Morphological post-processing is also used to improve object delineation and smooth segmentation borders. The model showed consistent performance with little overfitting, achieving training accuracy of 84% and validation accuracy of 81% over 200 epochs. Effective generalization to unknown data was demonstrated by the validation loss stabilizing at 0.55 and the training loss declining from 1.1 to 0.45. The findings show that the suggested method can reliably identify and categorize areas damaged by flooding from aerial photos, enabling automated flood assessment and speeding up disaster relief activities. There is a lot of promise for integrating this model and dataset combination into real-time catastrophe monitoring, urban planning, and geospatial analytic pipelines.

关键词： Satellite images areal images semantic segmentation UNet using morphology Pixel wise segmentation

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Enhancing Healthcare Outcomes by Real-Time Data Collecting and Predictive Analytics: Implementing IoHT to Remote Patient Monitoring 1

Enhancing Healthcare Outcomes by Real-Time Data Collecting a...

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1st International Conference on Emerging Technologies and Innovation for Sustainability, EmergIN 2024

作者： Karunkuzhali, D. Nasreen, M. Ayeesha Shobana, D. Aroulanandam, Vijay Vasanth Tamilselvi, M. Princy, Anni B. Department of Information Technology Panimalar Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai India Department of Mechatronics Engineering Rajalakshmi Engineering College Tamil Nadu Chennai India Networking and Communications Department School of Computing SRM Institute of Science & Technology Tamil Nadu Kattankulathur India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Artificial Intelligence and Machine Learning Panimalar Engineering College Tamil Nadu Chennai India

ISBN: (纸本)9798350391268

Innovative healthcare solutions have been made possible by the fast growth of technology, especially with the introduction of the Internet of Healthcare Things (IoHT). With a focus on remote patient monitoring, this study explores how the Internet of Health Things (IoHT) could improve healthcare outcomes through the use of predictive analytics and real-time data collecting. With the rise of chronic diseases, inaccessible healthcare, and problems with patient management, the Internet of Health Things (IoHT) presents a revolutionary solution to these problems through the use of wearable sensors, smart medical devices, and mobile health apps. The significance of real-time data collection, which allows healthcare practitioners to remotely acquire health-related information, check vital signs, and monitor patients continually, is highlighted in this paper. Healthcare providers may anticipate their patients' needs, spot any dangers, and take preventative measures by using cutting-edge data analytics tools like machine learning and artificial intelligence. Timely interventions can greatly improve patient outcomes and decrease healthcare costs;this is especially true in the management of chronic illnesses like diabetes and cardiovascular diseases, which the research demonstrates in a number of use cases of IoHT. The research goes on to discuss the difficulties of deploying IoHT solutions, such as privacy and data security issues, device compatibility, and the necessity of a solid healthcare infrastructure. The article goes on to talk about how regulatory frameworks play a part in making sure IoHT technologies are deployed safely and effectively. In order to improve the quality of treatment patients receive, the research seeks to solve these obstacles and offer a complete framework for integrating IoHT into current healthcare systems. This research enhances our understanding of IoHT and its effects on healthcare delivery by reviewing the literature, conducting case studies,

关键词： Remote patient monitoring

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Determining the Optimal Parameter for Wheat and Paddy Crops using Smart Farming: An Investigation

Determining the Optimal Parameter for Wheat and Paddy Crops ...

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International Conference on Computer Communication and Informatics (ICCCI)

作者： R. P. Ram Kumar Sanjeeva Polepaka Syed Khasim Numaan Manasa Kumari Srigana Pulikantham Mohith Peddineni Department of Artificial Intelligence & Machine Learning Engineering Gokaraju Rangaraju Institute of Engineering and Technology (GRIET) Hyderabad India Department of AI & ML Engineering Gokaraju Rangaraju Institute of Engineering and Technology (GRIET) Hyderabad India

Automation is a way to communicate with computers and mobile phones to monitor simple aspects of life. One such thing is the Smart Cultivation. One of the most important factors is it’s very cheap, can be installed anywhere and ensures safety. India contributes a significant share of Agriculture with its allied sectors and is the largest source of livelihood in India. Seventy percent of its rural households primarily rely upon agriculture for their livelihood, with Eighty-Two percent of farmers being small and marginal. When people try to plant and set up their own farm, they will be very cautious at maintaining them in their early stages. But later on, they will neglect and crops will get destroyed. The main objective is to identify the soil features, then predict the crop using KNN algorithm and then maintain the garden automatically using precision agriculture. It plays a very important role and serves as a good companion for the crops. The Internet of Things (IoT) allows us to remotely control and sense things. It consists of a microcontroller through which other sensors are monitored. The Smart Garden consists of ESP 32 and makes use of GISMO-VI Board. All the other sensors like temperature, soil moisture is connected. Firebase is the database on the Internet to which values from the sensors are updated every second. A mobile application is built through Kodular, adopted to develop the proposed method. Through Firebase, the collected data is stored and retrieved for subsequent processing. The data collected through Firebase can be used to monitor the parameters from anywhere. For future enhancements, Soil NPK sensors can be installed to supply the required fertilizers to the garden precisely. Also plant health identification can be done using web- scraping and image augmentation technique is used to increase the amount of data. Finally, a two-class classification (healthy / unhealthy) problem is solved.

关键词： Temperature sensors Temperature measurement Computers Smart agriculture Soil moisture Crops Safety

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Real-Time Incident Detection and Response with Isolation Forest Algorithm and Cloud Infrastructure 8

Real-Time Incident Detection and Response with Isolation For...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Basavaraddi, Chethan Chandra S. Ranganathan, Chitra Sabapathy Sindhuja, K. Mohankumar, N. Jagadeeswaran, M. Murugan, S. R&d Don Bosco Institute of Technology Vtu Department of Artificial Intelligence and Machine Learning Karnataka Belagavi India Mphasis Corporation Tamil Nadu Chennai India J.J College of Engineering and Technology Department of Information Technology Tamil Nadu Tiruchirappalli India Symbiosis Institute of Technology Nagpur Campus Maharastra Pune India Vinayaka Missison's Kirupanandha Variyar Engineering College Department of Computer Science and Engineering Tamil Nadu Salem India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376425

This research proposes an innovative approach to make incineration facilities more secure using the Isolation Forest algorithm and cloud infrastructure to identify and respond to real-time incidents. Incineration plants present special security concerns because of the high-stakes nature of their work and the dangers to people and the environment that may result from an incident. Traditional security procedures aren't always quick enough to respond to new dangers. Using the Isolation Forest method to identify anomalies in operational data streams provides a proactive security system. Using a cloud-based architecture, it accomplishes the scalability, flexibility, and real-time processing capabilities necessary for quick threat detection and response. It shows that methods successfully detect anomalous occurrences with few false positives and confirm it experimentally using data from simulated incineration facilities. Real-world consequences architecture may be integrated with current security systems to protect vital infrastructure and improve incident reaction times. It provides a thorough solution to the rising security risks in incineration plants, advancing their security posture. © 2024 IEEE.

关键词： Anomaly detection Critical infrastructure Environmental risk Incident response Operational data streams Threat identification

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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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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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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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machine learning Approach: Consumer Buying Behavior Analysis

Machine Learning Approach: Consumer Buying Behavior Analysis

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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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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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