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Predicting Lung Cancer with Artificial Neural Network: Analyzing Performance and Feature Importance

Predicting Lung Cancer with Artificial Neural Network: Analy...

Cognitive Computing in Engineering, Communications, sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Vatsal Mehta Varun M Ashwini Kodipalli Roshan Joy Martis Shridhar B. Devamane Ushasree. A Dept. of AI & Data Science Global Academy of Technology Bangalore Karnataka India Dept. of Electronics & Communication Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science School of Computing Mysuru Dept. of Computer Science & Engineering Gokaraju Lailavathi Engineering College Hyderabad India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

Lung cancer is the primary cause of cancer-related deaths globally. This cancer is majorly observed in smokers, increasing air pollution has also begun to affect non-smokers. Research indicates that exposure to polluted air significantly raises the risk of developing lung cancer, posing a major threat to public health. Despite advancements in medical technology, the mortality rate from lung cancer remains high, highlighting the importance of early diagnosis. Machine learning and deep learning offer promising solutions for early and accurate prediction through advanced data analysis. In our study, we utilized a dataset with 26 attributes for discern lung cancer, categorizing cases into three risk levels: high, medium, and low. We applied various algorithms and develop an Artificial Neural Network (ANN), comparing the overall performance with various machine-learning and deep-learning models. Our results show that the ANN achieved a high accuracy of 94.9% using the Adam optimizer, making it an effective model for lung cancer prediction.

关键词： Deep learning Accuracy Computational modeling Biological system modeling Lung cancer Mortality Artificial neural networks Predictive models Prediction algorithms Public healthcare

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Leveraging UAV technology for Enhanced Disaster Management: Innovations and applications

Leveraging UAV Technology for Enhanced Disaster Management: ...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Upadhyay, Govind Murari Vats, Prashant Shanker, Surabhi Bhushan, Saloni Mittal, Sonu Mandot, Manju Manipal University Jaipur Dept. of Computer Application Rajasthan Jaipur India Manipal University Jaipur School of CSE Dept. of CSE Rajasthan Jaipur India K.R. Mangalam University School of Engineering & Technology Dept. of CSE Sohna Road Haryana Gurugram India Chhatrapati Shivaji Maharaj University Dept. of Computer Science/IT Maharashtra Panvel India Amity Institute of Information Technology Amity University Noida Dept. of CSE Uttar Pradesh India J. R. N. Rajasthan Vidyapeeth University Dept. of Computer Science/IT Rajasthan Udaipur India

ISBN: (纸本)9798350385199

Unmanned Aerial Vehicles (UAVs) have substantially advanced the sector by providing improved real-time monitoring, data collecting, and situational awareness capabilities when integrated into disaster management techniques. This study examines the creative uses of unmanned aerial vehicles (UAVs) in disaster relief, emphasising how they may speed up reaction times and increase operational effectiveness in emergency situations. With an emphasis on their usefulness in human detection, damage assessment, and coordination efforts, we propose a thorough framework that describes the design, deployment, and evaluation of UAV systems customised for catastrophe situations. The study shows how UAVs may be used to enhance processes of decision-making and enable more efficient recovery and catastrophe response through instances and quantitative evaluation. The results demonstrate how UAV technology may revolutionise conventional disaster management procedures and provide insightful information for future studies and real-world applications in the field. © 2024 IEEE.

关键词： Disaster prevention

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A System to Track Fabricated Content in Social media using Machine Learning Approach

A System to Track Fabricated Content in Social media using M...

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2024 IEEE International Conference on Recent Advances in science and Engineering technology, ICRASET 2024

作者： Hegde, Ramakrishna Soumyasri, S.M. Shivashankara, S. Kavyashree, M.K. Pemula, Rambabu Harshitha, M. Vidyavardhaka College of Engineering Affiliated to Visvesvaraya Technological University Dept. of Computer Science and Engineering Mysuru India Vidya Vikas Institute of Engineering and Technology Affiliated to Visvesvaraya Technological University Dept. of Computer Applications Mysuru India Kr Pete Krishna Govt. Engg. College Dept. of Computer Science and Engineering Krishnarajapet Mandya India Sjce Jssstu Mysore India Vidya Jyothi Institute of Technology Dept. of Artificial Intelligence Hyderabad India Vidya Vikas Institute of Engineering and Technology Dept.of Information Science and Engg. Mysore India

ISBN: (纸本)9798350388602

In today's society, when the internet is pervasive, everyone gets their news from a variety of online sources. Information can swiftly reach millions of users in a short amount of time due to the growing usage of social media sites like Facebook, Twitter, and others. Fake news has far-reaching effects, ranging from swaying election outcomes in favor of particular politicians to fostering prejudiced viewpoints. The primary platforms for disseminating fake news are Instagram, WhatsApp, and numerous other social media platforms. By presenting a machine learning-based fake news detection algorithm, this work provides a remedy. This model needs fictitious data that was gathered from multiple information sources. data is extracted using web analytics, which is mostly used to build datasets. Real data sets and fake data sets are the two primary categories into which the data is separated. Random forest, logistic regression, decision trees, KNN, and gradient boosting are the classifiers used to classify the data. The data is categorized as true or false data based on the version that was received. The user will be able to determine whether or not the information on the website is fraudulent based on this. © 2024 IEEE.

关键词： Adversarial machine learning

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Fingerprint definition for song recognition using Machine Learning Algorithm

Fingerprint definition for song recognition using Machine Le...

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2023 International Conference on Smart Systems for applications in Electrical sciences, ICSSES 2023

作者： Arunakumari, B.N. Shashidhar, R. Sahana, B. Jagadamba, G. Manjunath, A.S. Roopa, M. Bms Institute of Technology and Management Dept. of Computer Science and Engineering Bengaluru India Jss Science and Technology University Dept. of Electronics & Communication Engineering Mysuru India Rv College of Engineering Dept. of Electronics & Communication Engineering Bengaluru India Siddaganga Institte of Technology Dept. of Information Science and Engineering Tumkuru India Jss Science and Technology University Dept. of Master of Computer Applications Mysuru India Dayananda Sagar College of Engineering Dept. of Electronics & Communication Bengaluru India

ISBN: (纸本)9798350347296

A condensed content-based signature that summarises an audio recording is known as an audio fingerprint. Audio The capability to recognize music regardless of design and without the use of meta-data or watermark embedding has made fingerprinting technologies popular. In. tegrity checking, watermark support, and content-based aural retrieval are additional applications for fingerprinting. The various methods of fingerprinting have been discussed using various justifications and terminologies, such as pattern matching, multimedia information retrieval, or cryptography. It is a distinct and condensed digest created using perceptually important elements of a video. Audio material can be monitored using fingerprinting technologies without requiring embedding of metadata or watermarks. However, audio fingerprinting has extra applications The algorithm is trained using thousands of mini audio speech commands. In order to improve the training efficiency, audio features are extracted during a pre-processing. The accuracy of the developed model is 83.7%. © 2023 IEEE.

关键词： Cryptography

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A Review Paper on Various Energy Saving Techniques in WSN

A Review Paper on Various Energy Saving Techniques in WSN

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2022 International Conference on Electronics and Renewable Systems, ICEARS 2022

作者： Josephine V. L., Helen Rajan, Dhivya Rajalakshmi, K. Cmr Institute of Technology Dept. of Computer Applications Karanataka Bengaluru India Montfort College Dept. Of. Computer Science Karanataka Bengaluru India

ISBN: (纸本)9781665484251

WSN (Wireless Sensor Network) is a unique variety of temporary type of Local area networks that are highly distributed self-organized systems. There is a detailed collection of various sensor nodes which are vastly deployed throughout the environment. Sensor node is a very small electronic device that is attached to a sensor network. They have an uncomplicated working mechanism that collects the data from any physical movement of an event that occurs or event query that is time driven. The notable feature of the sensor network is data gathering. Each sensory node can gather data, analyse that information and make that information to move to the location that is a destination. Routing algorithms play a vital role in making routing decision that delivers the packet to the exact point through optimal route. Since there is an energy restriction in WSNs, the energy related economisation becomes the most avoidable objective of various routing protocols. routing protocols of various sensor networks is reviewed and presented in this paper which provides a clear classification of various categories and comparison of the various methods. © 2022 IEEE.

关键词： Energy efficiency

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Effectiveness of Teletherapy in Managing Voice Disorders: A Systematic Review

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Indian Journal of Otolaryngology and Head & Neck Surgery 2025年 1-17页

作者： Varma, Maya Nayak, Srikanth Rao, Rohini R. Devadas, Usha Department of Speech and Hearing Manipal College of Health Professions Manipal Academy of Higher Education Manipal India Department of Audiology and Speech-Language Pathology Yenepoya Medical College Yenepoya University (Deemed to be University) Mangalore India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

Telerehabilitation has developed as one of the important aspects of health care delivery in the current era. Hence the present study aimed to systematically review the literature and understand the effectiveness of teletherapy in the management of voice disorders. A comprehensive search was performed in the PUBMED, CINAHL, and Scopus databases using Boolean operators, adhering to the PRISMA. The methodological quality of the selected studies was assessed using the ROBINS-I tool. The review included 12 articles meeting the inclusion criteria, featuring various study designs. All studies reported positive outcomes from televoice therapy intervention across multiple voice outcome measures, irrespective of whether participants received direct or indirect or a combination of voice therapy techniques. Studies comparing teletherapy with face-to-face therapy indicated that teletherapy is as effective as in-person therapy. However, the overall risk of bias assessment for the included studies, using the ROBINS-I tool, ranged from moderate to serious. This systematic review revealed that teletherapy is a feasible alternative to in-person therapy for voice disorders. However, there is a need to understand the effectiveness of teletherapy in demonstrating different voice therapy techniques. Further studies are needed to understand the application of various/particular voice therapy techniques via telepractice.

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A comparative Analysis of ensemble methods and their efficiency in the classification of ‘HIT AND RUN’ cases in an imbalanced dataset (Traffic Crashes) with and without using "Synthetic minority oversampling technique"

A comparative Analysis of ensemble methods and their efficie...

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International Conference on computer Theory and applications (ICCTA)

作者： Manjula Shenoy K R Srinivas Prabhu Information and Communication Technology Department Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Data Science and Computer Applications Department Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (数字)9798350309584

ISBN: (纸本)9798350309591

In some cases of developing a predictive model for a classification problem, the distribution of classes is skewed, meaning a majority of the training samples belong to a single class and a very small number of samples belong to a minority class. If Machine Learning techniques are applied directly to such data sets, it may result in a model which either ignores the minority class as a whole or gives less importance to the samples belonging to the minority class. Such datasets are often called imbalanced datasets. There are multiple ways to deal with these problems some include Random Over Sampling, Random Under Sampling, Threshold value tuning, one class classification to find the outlier, etc. One such technique is called the "Synthetic minority oversampling technique (SMOTE)". In this paper we are going to analyze the efficiency of ensemble methods for the classification of imbalanced datasets with and without using SMOTE. The dataset used for this purpose is an open-source Traffic crashes dataset available on the Chicago data portal. The target variable for our study is the "HIT_AND_RUN_I" column available in the dataset. This is a dichotomous variable that has 2 valid values. "Y" for the cases that are of positive outcomes as in "HIT_AND_RUN_I" = yes. "N" for the cases that are of negative outcomes as in "HIT_AND_RUN_I" = No.

关键词： Training Predictive models computer crashes data models Ensemble learning Tuning Portals

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UAVC: Unmanned Aerial Vehicle Communication Using a Coot Optimization-Based Energy Efficient Routing Protocol

UAVC: Unmanned Aerial Vehicle Communication Using a Coot Opt...

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2023 International Conference on Recent Advances in science and Engineering technology, ICRASET 2023

作者： Kumar, E. S. R. Ravi Babu Chandolu, Suresh Kumar, K. Parish Venkata Rao, Muvva Venkateswara Muralidhar, V. Nagarjuna, Kaja Vellela, Sai Srinivas V. R. Siddhartha Engineering College Dept. of Mathematics Vijayawada India Dhanekula Institute of Engineering and Technology Dept. of Cse Vijayawada India V. R. Siddhartha Engineering College Dept. of Computer Applications Vijayawada India Nri Institute of Technology Dept. of Cse Vijayawada India VVIT-Nambur Dept. of Csm Guntur India Stanley College of Engineering & Technology for Women Dept. of Information Technology Hyderabad India Chalapathi Institute of Technology Dept. of CSE-Data Science Guntur India

ISBN: (纸本)9798350306927

Because of their ability to be deployed in open environments with a high degree of flexibility and mobility, Unmanned Aerial Vehicles (UAVs) have garnered a considerable amount of attention in both military and civilian applications with the goals of enabling ubiquitous connectivity and fostering dynamic communications. The challenge is caused by aspects other than mobile ad hoc networks (MANET), specifically aerial mobility in three-dimensional space and frequently shifting topology. One and the same node in the UAV network is responsible for all three functions. The communication path is typically made up of multiple hops, and the routing decision has a substantial impact on how well the network performs. When choosing the most effective routing system, performance analysis should receive a significant amount of attention and effort. As a result of this impetus. The COAER-UAVC approach that has been given creates efficient communication pathways between the UAVs. To a large extent, it is predicated on the behaviors of Coot in their natural environments, such as fighting and hunting iguanas and evading predators. In addition, the COAER-UAVC technique that has been provided places an emphasis on the development of fitness functions with the goal of minimizing both energy consumption and communication delay. In order to show the optimum performance of the COAER-UAVC system, a diverse collection of simulations have to be carried out. The results of the experiments demonstrated that using the COAER-UAVC strategy resulted in significantly enhanced performance in comparison to using other methods. © 2023 IEEE.

关键词： Routing protocols

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ReFuSeg: Regularized Multi-Modal Fusion for Precise Brain Tumour Segmentation

arXiv

引用

arXiv 2023年

作者： Kasliwal, Aditya Sagaram, Sankarshanaa Srivastava, Laven Seth, Pratinav Khan, Adil Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Computer Science Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Mechanical Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

Semantic segmentation of brain tumours is a fundamental task in medical image analysis that can help clinicians in diagnosing the patient and tracking the progression of any malignant entities. Accurate segmentation of brain lesions is essential for medical diagnosis and treatment planning. However, failure to acquire specific MRI imaging modalities can prevent applications from operating in critical situations, raising concerns about their reliability and overall trustworthiness. This paper presents a novel multi-modal approach for brain lesion segmentation that leverages information from four distinct imaging modalities while being robust to real-world scenarios of missing modalities, such as T1, T1c, T2, and FLAIR MRI of brains. Our proposed method can help address the challenges posed by artifacts in medical imagery due to data acquisition errors (such as patient motion) or a reconstruction algorithm’s inability to represent the anatomy while ensuring a trade-off in accuracy. Our proposed regularization module makes it robust to these scenarios and ensures the reliability of lesion segmentation. © 2023, CC BY.

关键词： Magnetic resonance imaging

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Unleashing the Potential of Women in Leadership Roles within the Evolving Landscape of Machine Learning and technology

Unleashing the Potential of Women in Leadership Roles within...

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2024 IEEE International Conference on Intelligent Systems and Advanced applications, ICISAA 2024

作者： Tiwari, Madhuri Giri, Neeraj Suman, Sanjay Kumar Chandanan, Amit Kumar Peneti, Subhashini Choudhury, Shibanjali GGITS Dept of Data Science MP Jabalpur India IES Institute of Technology and Management IES University Department of Computer Science and Engineering MP Bhopal462044 India St. Martin's Engineering College Dept. of ECE Telangana Secunderabad India Department of Computer Science and Engineering C G Bilaspur India MLR Institute of Technology Dept. of CSE Telangana Hyderabad India Techno College of Engineering Dept. of CSE Tripura Agartala India

ISBN: (纸本)9798331539948

The innovative Gender-Empowered Leadership Development (GELD) programme promotes gender equality in fast-paced technology and machine learning leadership positions. This was a US government initiative. BiasMitigator, Skillenhancer, and CultureCatalyst work together to eliminate gender prejudice, improve employee skills, and improve workplace morale. Programs collaborate to achieve these aims. CultureCatalyst promotes a pleasant environment, SkillEnhancer improves skills over time, and BiasMitigator eliminates gender prejudice across the firm. All three projects assist the organization achieve its aims. This study compares GELD to normal approaches to better understand the situation. This illustrates how intricate its procedure is. © 2024 IEEE.

关键词： BiasMitigator CultureCatalyst GenderEmpowered Leadership Development Inclusion Leadership Development Machine Learning SkillEnhancer technology Traditional Methods Women in Leadership

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