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An Efficient Biomedical Solicitation in Liver Cancer Classification by Deep learning Approach

An Efficient Biomedical Solicitation in Liver Cancer Classif...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Vasundhara, N. Nandan, Archana S Hemanth, S.V. MacHerla, Sivudu Madhura, G.K. Cmr Technical Campus Department of Cse Hyderabad India Gitam School of Technology Gitam University Department of Computer Science & Engineering India Hyderabad Institute of Technology and Management Department of Computer Science and Engineering Telangana India Pace Institute of Technology & Science Andhra Pradesh India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798350398465

The liver, a significant physical organ, is located in the upper right and left abdominal cavities just below the diaphragm. It produces several different chemicals that the body needs to function properly. Researchers can benefit from converting images into data to more easily exchange and generate precise results. Since the process of conversion relies on technology and an algorithm, it eliminates the possibility of human error. Liver cancer has the highest fatality rate of any cancer since its symptoms don't present until late in the disease's progression, making early detection difficult. Skimming, sifting, segmenting, feature abstraction, and presentation via ANN (artificial Neural Network) are the primary topics of the first stage of the debate. Real-time data sets use Feed-Forward Neural Network (FFNN) for identifying liver cancer and classifying its severity. Filtering has two primary applications: noise suppression and edge rounding. Then, segmentation is employed to isolate the relevant area, allowing for more compact data storage. The Gray Level and Co-occurrence Matrix (GLCM) is used to extract features, and the resulting matrix can have many different forms. This criterion helps classify tumors as benign or malignant. Metrics such as accuracy, sensitivity, positive and negative predictive values, and precision are used to assess the rate. The experimental method for identifying liver tumors uses CT liver pictures to achieve an average accuracy of 99.45% for malignant images. © 2023 IEEE.

关键词： Computerized tomography

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Forecasting Financial Frontiers: Real-time Insights in Stock Price Prediction through LSTM, Linear Regression, and Sentiment Analysis 6

Forecasting Financial Frontiers: Real-time Insights in Stock...

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6th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Khalifa, Ahmed A. Ramesh, G. Deekshith, R. Harsha, K. Shreyas, J. Department of Networking and Security College of Information Technology University of Fujairah Fujairah United Arab Emirates Switching and Traffic Department National Telecommunication Institute Cairo Egypt Dept. of Artificial Intelligence and Machine Learning Alva’s Institute of Engineering and Technology Mangalore India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal576104 India

ISBN: (纸本)9798350394634

The ever-changing world of financial markets makes it extremely difficult to predict stock values with any degree of accuracy. By combining real-time data analysis with cutting-edge methods including sentiment analysis, linear regression, and long short-term memory (LSTM), this study explores the field of financial frontier forecasting. Using LSTM's ability to recognize complicated patterns in time series data, the study makes its way through the challenges of stock price prediction. Sentiment analysis adds a qualitative element by estimating market sentiment from textual data, and linear regression, which supports LSTM, offers a strong foundation for modeling linear correlations. This research presents the efficacy of integrating these approaches, highlighting their capacity to reveal latent insights and improve predictive accuracy in stock price modeling through an empirical investigation. The findings point to the significance of real-time data integration and the synergy that can be attained by combining various analytical techniques, opening the door to more strategic financial forecasts and well-informed investment choices. © 2024 IEEE.

关键词： Long short-term memory

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Design of NBW-MHO with BERT model for prediction of Breast Cancer in IoT Healthcare System 4

Design of NBW-MHO with BERT model for prediction of Breast C...

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4th International Conference on Emerging Research in Electronics, Computer Science and technology, ICERECT 2022

作者： Ghatkamble, Rajlakshmi Pareek, Piyush Kumar Parameshachari, B.D. New Horizon College of Engineering Department of Information Science and Engineering Bangalore India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Nitte Meenakshi Institute of Technology Department of Electronics and Communications Engineering Bengaluru India

ISBN: (纸本)9781665456357

The key to successful early recovery and treatment of breast cancer in today's healthcare system is an accurate and prompt diagnosis. Over the last several years, the IoT has undergone a transition that makes it possible to analyse both real-time techniques. Medical diagnostics are aided by the Internet of Medical Things, which connects various medical equipment and artificial intelligence applications with the healthcare network. Most women with breast cancer don't make it because the disease isn't detected early enough using today's best methods. Therefore, doctors and scientists are confronted with a significant challenge in recognizing breast cancer at an primary stage. We present a medical IoT-based diagnostic system that can distinguish between patients with cancer and those without it in an Internet of Things setting. Malignant vs benign categorization is performed using an unique transfer learning technique called BERT, which is based on a previously learned language model. In particular, this research looks at how well novel fine-tuning approaches based on transfer learning might improve BERT's capacity to capture significant context. This research improves the BERT model's classification accuracy by using a Black Widow-meta-heuristic Optimization (NBW-MHO) feature selection strategy to refine feature selection from the breast cancer dataset. The WDBC dataset served as a testbed for the suggested method. The suggested model's classification accuracy using the BERT model and NBW-MHO was 95.20 percent. © 2022 IEEE.

关键词： Internet of things

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Brain Cancer Tumor Detection by U-Net Deep learning Algorithm from MRI Images 12

Brain Cancer Tumor Detection by U-Net Deep Learning Algorith...

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12th International Conference on Internet of Everything, Microwave, Embedded, Communication and Networks, IEMECON 2024

作者： Das, Utpal Chandra Benjapolakul, Watit Gupta, Manoj Vitoonpong, Timporn Suanpang, Pannee Somthawinpongsai, Chanyanan Butdisuwan, Sujin Nanthaamornphong, Aziz Center of Excellence in Artificial Intelligence Machine Learning and Smart Grid Technology Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand Department of Electrical Engineering Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur India Dept. of Rehabilitation Medicine King Chulalongkorn Memorial Hospital Dept. of Rehabilitation Medicine Faculty of Medicine Chulalongkorn University Bangkok10330 Thailand Department of Information Technology Faculty of Science and Technology Suan Dusit University Bangkok Thailand Shinawatra University Pathum Thani12160 Thailand College of Computing Prince of Songkla University Phuket Campus Phuket Thailand

ISBN: (纸本)9798350387315

This research looks at the genomic subtypes of low-grade glioma tumors and their shape characteristics by deep learning magnetic resonance image (MRI) segmentation. We analyzed preoperative imaging and genetic data from 110 patients with low-grade glioma from the Cancer Genome Atlas. Three shape features were recovered to quantify the two- and three-dimensional aspects of the malignancies. Based on gene expression, DNA copy number, IDH mutation, 1p/19q co-deletion, DNA methylation, and microRNA, previously identified clusters were found in genomic data. We used the exact trait test to investigate the connection between chromosomal clusters and imaging traits. Our findings show a significant correlation between the margin fluctuation-bounding ellipsoid volume ratio and the RNA Seq clusters. Furthermore, a correlation was discovered between RNA-seq clusters and angular standard deviation. The U-net deep learning algorithm demonstrated a test accuracy of 94\% and a mean Dice coefficient of 90\%. These findings suggest that tumor shape characteristics derived from MRI can be projected through genomic subtypes in lower-grade gliomas. © 2024 IEEE.

关键词： Gene expression

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YOLO Network Based Intelligent Municipal Waste Management in Internet of Things 4

YOLO Network Based Intelligent Municipal Waste Management in...

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4th International Conference on Emerging Research in Electronics, Computer Science and technology, ICERECT 2022

作者： Ghatkamble, Rajlakshmi Parameshachari, B.D. Pareek, Piyush Kumar New Horizon College of Engineering Department of Information Science and Engineering Bangalore India Nitte Meenakshi Institute of Technology Department of Electronics and Communications Engineering Bengaluru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9781665456357

It's becoming more worrying that unchecked urban garbage buildup might lead to environmental contamination and potential health risks for city dwellers. People don't use their recycling bins correctly, thus they aren't very effective. IoT and AI advancements have made it possible to replace the outdated trash management infrastructure with one that incorporates smart sensors for real-time monitoring and more efficient waste management. Having a sophisticated, computer-based system to handle garbage is crucial. Hand-picking, or the human separation of garbage into its many components, is an essential part of the waste management process. An intelligent waste material classification system is proposed in this study to streamline the process;it is created utilising a novel methodology based on image processing methods, the diagnosis of photos from waste management. The YOLOv5 public dataset of trash management was used for training, fine-tuning, and testing the suggested technique. The plan also includes the introduction of a smart garbage can's architectural design, which incorporates a microprocessor and many sensors. In order to keep an eye on things, the suggested system uses the Internet of Things and Bluetooth connection. An integral aspect of any effective municipal waste management system is a central monitoring facility that use that data to plan for things like the deployment and maintenance of adaptive equipment, as well as the garbage collection and vehicle routing strategies that bring it all home. © 2022 IEEE.

关键词： Deep learning

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Blockchain-Enabled Federated learning Systems with Explainable AI: A Review

Blockchain-Enabled Federated Learning Systems with Explainab...

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machine learning and Autonomous Systems (ICMLAS), International Conference on

作者： Nayan Potdukhe Palash Gourshettiwar Sujal Zade Atharva Waghale Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Meghe Wardha Maharashtra Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Meghe Wardha Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The convergence of Blockchain with Federated learning (FL) technology and Explainable artificial intelligence (XAI) serves as a new framework to solve privacy protection problems, security issues, and interpretability needs. Model training collaborations supported by FL technology allow different entities to develop models while safeguarding raw data from exposure during training processes, thus addressing privacy needs in healthcare and finance. The combined challenges of model vulnerability to malicious attacks, data poisoning vulnerabilities, and explainability issues block broad industrial adoption. Blockchains strengthen FL security systems by providing decentralised solutions that produce unchangeable audit trails through their proof of transparency, immutability, and distributed consensus models. By enabling model interpretability, XAI methods build stakeholder trust and ensure system accountability. The study combines the technologies to analyse current systems and identify gaps that integrated systems should resolve. A detailed review examines explainable blockchain models and privacy preservation methods to tackle performance and implementation limitations within the framework. Combining these technologies builds a transformative process to create secure AI systems that maintain interpretability functions and protect data privacy during system development as researchers develop frameworks for wider application deployment.

关键词： Training Privacy Data privacy Explainable AI Reviews Federated learning Data security Data models Blockchains Protection

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A Review on the Risk of Musculoskeletal Disorder in Humans Originating from Excessive Use of Smartphone

A Review on the Risk of Musculoskeletal Disorder in Humans O...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： Trishul Dhale Prateek Verma Siddhi Dhande Prajwal Bandre Praveen Kumar Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Maharashtra India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

A musculoskeletal disorder is a condition that affects the muscles, bones, and joints, causing pain, stiffness, and limited mobility. These disorders can result from various factors, including repetitive movements, poor posture, or traumatic injury. The use of smartphones can negatively impact musculoskeletal health by causing repetitive strain injuries, particularly in the neck, shoulders, and thumbs. Prolonged use and poor posture while using smartphones can lead to conditions such as “text neck” and tendinitis, resulting in pain and reduced mobility. The study explores the several musculoskeletal conditions, such as Carpal Tunnel Syndrome, Osteoarthritis Syndrome, and Text Neck Syndrome that are linked to the excessive use of electronic devices such as smartphone and laptop. This review paper emphasizes the growing tendency of neck pain in younger populations as a result of extended use of personal technology devices through a thorough analysis of the literature. The absence of an affordable pain management device that can monitor the movement continuously in the market makes this problem even more severe. The monitoring is generally carried out using manual methods or semi-automatic methods resulting in short duration of data collection. Hence, these results present a lot of inaccuracies in the analysis of musculoskeletal disorder. Therefore, there arises the need of an automatic pain management device that can collect the data and monitor it over a long duration of time. Hence, this study presents the problems of musculoskeletal disorder, its types and challenges in long term monitoring. The thorough analysis of this problem may help to raise the awareness among the humans about the need to monitor the musculoskeletal movement and would help the researchers to develop a device that can provide an early warning to the users.

关键词： Musculoskeletal system Carpal tunnel syndrome Pain Reviews Neck Public healthcare Osteoarthritis Smart phones Monitoring Consumer electronics

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Constructing the Evaluation Index System of Chinese-Portuguese machine Translation using the Delphi and Analytic Hierarchy Process Methods 3

Constructing the Evaluation Index System of Chinese-Portugue...

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3rd IEEE International Conference on Power, Electronics and Computer Applications, ICPECA 2023

作者： Sun, Yuqi Hoi, Lap Man Kei Im, Sio Macao Polytechnic University Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence Ministry of Education Macao China Macao Polytechnic University Faculty of Applied Sciences Macao China Macao Polytechnic University Macao China

ISBN: (纸本)9781665472784

Quality assessment is an important part of ensuring the quality and long-term development of machine translation. There is still much room for research into the evaluation of machine translation from Chinese to Portuguese, and no specific evaluation metrics have yet been developed. This study uses the Delphi method and Analytic Hierarchy Process (AHP) to construct an index system of Chinese-Portuguese machine Translation and analyzes the relative index weight. The system includes four first-level indicators and nine second-level indicators. The weight of primary indicators, from high to low, is "Accuracy, fidelity"(52.5%), "Readability"(22.9%), "Fluency"(16.8%), and "Acceptability & Usability"(7.8%). The secondary indicators with the highest weight of each primary indicator are "Correction", "Lexicon", "Readability", and "Utility". AHP was used to transform the qualitative problems in evaluating Chinese-Portuguese machine Translation into the quantitative problem in order to provide a theoretical basis and practical guidance for the future manual evaluation of Chinese-Portuguese machine Translation, and for the eventual modification of automatic machine translation evaluation methods. © 2023 IEEE.

关键词： Analytic hierarchy process

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Spam SMS Classifier Using machine learning Algorithms

Spam SMS Classifier Using Machine Learning Algorithms

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artificial intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), International Conference on

作者： Bhagyashri Mankar Mohini Wanjari Diksha Gabhane Department of Information Technology Yeshwantrao Chavan College of Engineering (YCCE) Nagpur India Department of Artificial Intelligence and Machine Learning J.D. College of Engineering Nagpur India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

Spam via short messaging service (SMS) is becoming more prevalent as spammers use it as a means of reaching their targets. While spam SMS messages can be annoying, they can also be dangerous since they might attempt to steal personal information or contain links to malicious websites. The status of SMS spam detection is surveyed in this research. We go over the various kinds of SMS spam; characteristics that help identify spam, and machine learning techniques that have been employed to address the issue. We also note some of the difficulties and potential paths forward in the identification of SMS spam. Support Vector machine (SVM): SVMs are a class of machine learning algorithms that are applicable to applications involving both regression and classification. SVMs function by locating a hyper plane in the data that has the greatest margin of separation between the various classes. Multinomial Naive Bayes (MNB): A kind of Naive Bayes classifier called MNB is made especially for classification problems involving categorical data. If the data's features are independent of one another, MNB applies the Bayes theorem to determine the likelihood of each class given the data's features. Extra Trees Classifier (ETC): The random forest method serves as the foundation for the ensemble learning algorithm known as ETC. To arrive at a final prediction, ETC builds several decision trees using arbitrary subsets of the data and averages their forecasts. Random Forest Classifier (RFC): Based on the decision tree method, RFC is another kind of ensemble learning algorithm. In order to get at a final prediction, RFC builds several decision trees using arbitrary subsets of the data and averagetheirforecasts. To enhance the overall performance of the model, the predictions made by each individual classifier are combined using the Voting Classifier and Stacking Classifier ensemble models.

关键词： machine learning algorithms Predictive models Message services Prediction algorithms Data models Classification algorithms Naive Bayes methods Decision trees Ensemble learning Random forests

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Big Data in Social Media: Analyzing Trends, Patterns and Challenges 2

Big Data in Social Media: Analyzing Trends, Patterns and Cha...

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2nd International Conference on machine learning and Autonomous Systems, ICMLAS 2025

作者： Jikar, Nayan Tale, Yash Tale, Abhay Barhate, Aditya Verma, Prateek Jikar, Aman Datta Meghe Institute of Higher Education and Research Sawangi Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Sawangi Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

The huge volumes of data produced in social media provide both new possibilities and challenges to analytics. The present paper emphasizes Big Data analytics and machine learning (ML) methods to uncover trends, patterns, and user behavior from social media-related data. Such vital concepts, like the 'Four Vs' of Big Data: Volume, Velocity, Variety, and Veracity, and innovative tools for datacollection, storage, and preprocessing are covered here as well. Sentiment analysis, trend forecasting, and user behaviormodeling are some of the examples of what is possible using ML and artificial intelligence (AI) for deriving actionable insights. Marketing campaigns, crisis communications, and political campaigns are some of the scenarios that are covered to bring out real world applications of such technologies, along with their transformative effects. Lastly, data privacy, quality, and scalability issues are noted, along with ethical concerns and biases. Directions for the future point to developments in real-time data analysis, anonymization, and the changing role of AI in social media platforms. This research emphasizes the significance of Big Data and machine learning in driving innovation and decision-making in the digital era. © 2025 IEEE.

关键词： Tweets

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