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A Novel Deep Learning Based Approach for Detecting and Localization of Pulmonary Cancer 14

A Novel Deep Learning Based Approach for Detecting and Local...

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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Mishra, Amit Kumar Sharma, Rahul Singh, Jagendra Singh, Prabhishek Manoj, Diwakar Tiwari, Mohit Graphic Era Hill University Department of Computer Science & Engineering Dehradun India D. Y. Patil International University Pune India School of Computer Science Engineering and Technology Bennett University Greater Noida India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Bharati Vidyapeeth's College of Engineering Dept. of Computer Science & Engineering Delhi India

ISBN: (纸本)9798350344837

In the ever-evolving field of scientific diagnostics, the early diagnosis of pulmonary most cancers continues a key undertaking. This observation proposed a unique deep learning-primarily based approach, in particular using Generative Adversarial Networks (GANs), intending to modernise the identification and localization of pulmonary malignancies through scientific imaging. Our models, trained using a varied dataset, demonstrated a promising accuracy fee of 70% within the sample set, suggesting its ability to adeptly differentiate between malignant and non-malignant instances in scientific images. While the conclusions suggest a significant growth in lung cancer detection, also they highlight locations demanding in addition refinement. The balance of technological prowess and scientific significance, as reflected through criteria like sensitivity and specificity, remains a focus topic for future projects. The outcomes of this research are substantial. Beyond the on the spot discoveries, the take a look at emphasizes the transformational possibility of incorporating sophisticated AI approaches into healthcare. As the scientific network grapples with the difficulties of early cancer identification, gear like the one displayed in this study ought to usher in a new age in diagnostics-marked by accuracy, efficiency, and patient-centricity. In conclusion, this have a look at now not simplest adds a fresh diagnostic tool to the sector but moreover sets the way for future innovations within the confluence of AI and healthcare. © 2024 IEEE.

关键词： Generative adversarial networks

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NeuroNet: An Attention-Driven Lightweight Deep Learning Model for Improved Brain Cancer Diagnosis

NeuroNet: An Attention-Driven Lightweight Deep Learning Mode...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Istyak Nabil, Hadiur Rahman Abir, Golam Rabbani Hossain, Tazdik Das, Aritra Mridha, M.F. American International University-Bangladesh Dept. of Computer Science Dhaka Bangladesh Bangladesh University of Business and Technology Department of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350369106

This paper introduces NeuroNet architecture, a lightweight deep-learning framework designed for brain tumor identification, integrating a spatial attention-driven convolutional neural network (CNN) architecture. NeuroNet aims to enhance the classification accuracy of brain tumors, specifically targeting three types: gliomas, meningiomas, and general brain tumors, using Magnetic resonance imaging (MRI) data. The framework is estimated on the Bangladesh Brain Cancer MRI Dataset, which consists of 6,056 MRI images divided into three balanced classes. Unlike traditional machine learning techniques that rely on handcrafted features, NeuroNet's architecture leverages CNN layers with a spatial attention mechanism, enabling the model to focus on critical tumor regions. This approach reduces manual intervention and enhances feature representation. The proposed model performed a high classification accuracy of 99.04%, with a weighted F1-score of 99.02%, outperforming conventional models. These results indicate NeuroNet's potential to assist medical professionals by providing an accurate and reliable diagnostic tool, thus improving brain tumor treatment outcomes. This research underscores the promise of attention mechanisms within deep learning frameworks for advancing cancer diagnosis and medical imaging. © 2024 IEEE.

关键词： Diseases

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An Improved Diagnostic Approach for Classifying Tomato Leaf Diseases using Ensemble Deep Learning based Technique 8

An Improved Diagnostic Approach for Classifying Tomato Leaf ...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Batra, Salil Chakraborty, Gouri Shankar Bortty, Joy Chakra Polok, Sourav Das Ahmed, Tamjid Taki, Tanvir Ahmed Lovely Professional University Dept. of Computer Science and Engineering Punjab Phagwara India Westcliff University Department of Computer Science IrvineCA United States

ISBN: (纸本)9798350367904

Tomato is one of the most common vegetables that has high commercial importance for abundant production to meet the huge demand world-wide. There are several challenges that put impact on the mass production of tomato, and which are required to be resolved timely. One of the major challenges is tomato leaf diseases where different types of diseases are there which need to be identified correctly to protect the plants, otherwise it can cause severe harm to the plants which ultimately effects the production of tomato. To have a solution, several scientists and researchers have been working on this to classify diseases in a very initial stage. Diseases of the plants can be classified from leaf image, but it's difficult to identify disease manually by observing through eyes. Deep learning techniques are being used to identify different types of disease using the leaf image. Several existing approaches have been discussed in this study where different detection processes have been proposed. But, in most of the cases, several limitations have been observed regarding the performance of those traditional frameworks. In this study, a novel disease detection approach has been proposed to detect multiple types of tomato leaf disease through analyzing the leaf images. Here, after combing the performances of four CNN models (MobileNetv2, ResNet50, VGG19, VGG16), an ensemble deep learning technique has been applied to identify different types of the tomato leaf disease as well as to classify the healthy and affected leaf. The performance achieved from the proposed ensemble framework ensures optimum accuracy of 96.7% with efficiency. A proper comparative analysis has been presented that supports the novelty and potentiality of the work. © 2024 IEEE.

关键词： Fruits

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Real Time Machinery Health Detection Via Thermal Imaging 10

Real Time Machinery Health Detection Via Thermal Imaging

引用

10th International Conference on Signal Processing and Communication, ICSC 2025

作者： Khan, Jahiya Nene, Manisha J Dept. of Computer Science and Engineering Pune India

ISBN: (纸本)9798331511890

Marine vessels are equipped with advanced and integrated complex machines that operate in challenging conditions, where the failure rate is high. One of the most vital elements of these machines is fault diagnosis and maintaining 100 percent operational states. Any machinery breakdown at sea during maritime operations can have severe consequences leading to operational impacts and reducing combat efficiency. The existing maintenance routines - PPM (Planned Preventive Maintenance) and CBPM (Condition Based Predictive Maintenance) are reactive approaches that may miss early signs of material degradation, leading to unplanned machinery breakdown at sea. An efficient method to counter this issue in real time is thermal imaging. The dataset required to develop a thermal prognostic model for real-time machinery health detection is not available. This paper presents a systematic approach to develop a prognostic model for real time machinery health detection via thermal imaging. We have developed a custom thermal image dataset representing various machinery conditions-normal, defective and degraded. We have developed our real-time prognostic model 'Thermal Prognostic Model'. This model targets fuel systems to prevent major fires and accidents at sea by predicting potential failures and taking corrective actions with mAP50 0.956, precision 0.945 and recall 0.957. © 2025 IEEE.

关键词： Predictive maintenance

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Detection of Multiple Plant Diseases by Leaf Diagnosis Using Deep Learning

Detection of Multiple Plant Diseases by Leaf Diagnosis Using...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Mohd, Noor Mehta, Saransh Mehta, Prajjuwal Ghazi, Mohd. Rehan Chaturvedi, Narayan Martha, Sheshikala Dept. of Cse Dehradun India Department of Computer Science and Engineering Dehradun India Sr University Department of Computer Science and Artificial Intelligence Warangal India

ISBN: (纸本)9798350379716

India is a land of agriculture, and it has a deep impact in the economic growth of our country. Around 10-20% crops in India, suffer from various diseases that can lead to those crops getting wasted therefore this is one of the major concerns for the farmers to protect their crops from such diseases. To prevent the plants from getting wasted the disease should be detected as soon as possible so that the farmer can take the necessary steps to save the crops from getting wasted. Farmers have been using their conventional methods for years to identify diseases, but it is hard for them to identify various diseases with high accuracy. Now with the advancement in Artificial Intelligence and computer vision, we can use various techniques to solve this problem of disease detection in different plants with more accurate results. This will lead to a fast and early diagnosis of the disease and will help to decrease crop wastage making it more beneficial for the farmer and for the whole country. In this research paper, we have made use of Deep Learning a very popular technology nowadays as it provides various models and functionalities to work with images. We have used the Convolutional Neural Network (CNN) model for this research paper as it is one of the best in image classification and is also good at extracting smaller details from the image which will work well for the detection of disease in a plant by diagnosing the images of its leaf. We have made use of Plant Village Dataset for our model which contains more than 60,000 RGB images of both healthy and unhealthy leaves as well. © 2024 IEEE.

关键词： CNN Deep Learning Plant Disease Plant Village Dataset

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Heart Disease Detection Using Machine Learning Models 2

Heart Disease Detection Using Machine Learning Models

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Singh, Amrit Mahapatra, Harisankar Biswal, Anil Kumar Mahapatra, Madhumita Singh, Debabrata Samantaray, Milan Berhampur India Berhampur India Dept. of Computer Science Udayanath Autonomous College of Science and Technology Cuttack India Dept. of Computer Science and Engineering Delhi Technical Campus UP Greater Noida India Bhubaneswar India Department of Computer Science and Engineering Silicon Institute of Technology Bhubaneswar India

An enormous number of deaths occur every year as a result of heart disease, making it a major concern in world health. Improving patient outcomes and lowering death rates, early detection and correct diagnosis of cardiac disease play a key role. When the heart's arteries become blocked, oxygen-poor blood cannot reach the heart properly, resulting in coronary heart disease. Early detection of cardiac disease is viable because it reduces medical costs and potentially saves the patient's life. Recently presented methods have improved heart failure detection accuracy on testing data without sacrificing accuracy on training data, yet most of these algorithms are suffering from the issue of overfitting. Models that were created end up fitting the test data too well. In this study, we create a novel diagnostic system to address this issue, and the resulting system demonstrates high intelligence and excellent performance on both training and testing data. Machine learning (ML) algorithms have demonstrated promising potential in assisting healthcare professionals with timely and accurate diagnosis. In this paper, is based on supervised machine learning methods are decision tree (DT), random forest (RF), Support vector Machine (SVM), Principal Component Analysis(PCA). We compare their accuracy with each other by using bar plot. © 2024 Elsevier B.V.. All rights reserved.

关键词： Blood pressure

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A Highly Scalable Automated Universal Binary Malware Analysis Framework for Healthcare and Medical SaaS 24

A Highly Scalable Automated Universal Binary Malware Analysi...

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8th International Conference on Medical and Health Informatics, ICMHI 2024

作者： Cheng, Shin-Ming Fan, Chin-Chi Chien, Bo-Han Chang Chen, Chia-Yi Lin, Wei-Hung Liao, Chen-Chien Dept. of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei City106335 Taiwan Dept. of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei City106335 Taiwan

ISBN: (纸本)9798400716874

In recent years, there has been a growing emphasis on information security, with major companies introducing cybersecurity teams to ensure the safety of data. However, there is a lack of convenient channels for general users to engage in cybersecurity protection, leaving them with no choice but to rely on various antivirus software installations to safeguard their privacy and financial assets. This phenomenon is particularly evident in the digital transformation of healthcare and medical information systems, where the substantial amount of digitized patient data has become a crucial asset within hospital systems. Unfortunately, it has also made healthcare systems potential targets for cyberattacks. The databases of major hospitals have become vulnerable to malicious virus invasions, posing significant threats to patient privacy and the operational integrity of healthcare institutions. Faced with such threats, there is an increasing need for comprehensive cybersecurity protection mechanisms. To address this issue, we have developed the Universal Binary Malware Analysis Framework (UBMAF), an easily accessible binary file analysis framework for the general public. UBMAF integrates multiple open-source static and dynamic analysis tools into an automated module, deployed as Software as a Service (SaaS) in the cloud for healthcare and medical systems. This eliminates the need for users to install applications, and the framework interface is optimized for intuitive usability. During the usage process, users can freely choose module combinations. After uploading files to UBMAF, the framework conducts corresponding tool analyses or file processing based on the selected modules. Ultimately, it provides users with downloadable results and analysis reports. This design enables large healthcare and medical systems to quickly and conveniently enhance their cybersecurity defenses while ensuring the security of digital medical data, effectively addressing the challenges brought abo

关键词： Cybersecurity

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Real-time Health Monitoring of Patients in Emergencies Through Machine Learning And IoT Integrated With Edge Computing 14

Real-time Health Monitoring of Patients in Emergencies Throu...

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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Mishra, Amit Kumar Sharma, Rahul Singh, Jagendra Singh, Prabhishek Diwakar, Manoj Tiwari, Mohit Graphic Era Hill University Department of Computer Science & Engineering Dehradun India D. Y. Patil International University Pune India School of Computer Science Engineering and Technology Bennett University Greater Noida India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Bharati Vidyapeeth's College of Engineering Dept. of Computer Science & Engineering Delhi India

ISBN: (纸本)9798350344837

With employing the Internet of Things (IoT) sensors and modern-day machine learning algorithms, this study presents a modern inquiry at the intersection of healthcare and era. This have a look at studies the capability of Long ShortTerm Memory (LSTM) and Artificial Neural Network (ANN) fashions in forecasting affected person fitness consequences by utilising the exploitation of sensor facts inclusive of temperature, blood strain, ECG, EEG, and pulse price. The study extensively examines the models' performance utilising a broad variety of performance benchmarks. Results demonstrate that once compared to the LSTM model, the ANN version performs better in terms of prediction accuracy, precision, do not forget, and Fl score. This enhanced accuracy indicates how effectively the ANN model can spot challenging patterns in the dataset. The predicted fitness possibilities for 15 human individuals also are proved in a result table, reaffirming the ANN models constant advantage in prediction. This look at proactive patient care, more intelligent treatment regimens, and improved overall patient outcomes is driving the business and is a significant step towards personalised, data-driven healthcare solutions. © 2024 IEEE.

关键词： Forecasting

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Nova: Revolutionizing Agile Project Management with Scrum and Kanban Integration for Enhanced Team Collaboration and Productivity 5

Nova: Revolutionizing Agile Project Management with Scrum an...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Manaswini, Poluri Shaik, Sajjad Ashwani, Siddhant Balakrishnan, Roshni M. Rajagopal, Shinu M. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

In the realm of software project management, Agile methodologies such as Scrum and Kanban have become quintessential for fostering adaptability and efficiency. "Nova,"a novel project management tool, embodies these methodologies to offer a user-friendly interface that supports the dynamic needs of modern software development teams. This paper explores Nova's unique approach that integrates the foundational principles of Agile which are Scrum, and Kanban, thereby facilitating a seamless and effective management platform. Through a comparative analysis with traditional project management tools, this study evaluates how Nova enhances team collaboration, project tracking, and outcome predictability. Emphasizing its ease of use and adaptability, the research highlights Nova's potential to improve project delivery times and overall team productivity. © 2024 IEEE.

关键词： Lean production

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Image Colorization Using Auto Encoders 3

Image Colorization Using Auto Encoders

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3rd Odisha International Conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Panda, Susmita Ranjan Panda, Mohit Prakash, Abhigyan Dept. of Computer Science Engineering Iter Soa University Odisha Bhubaneswar India School of Computer Engineering Kiit Deemed to Be University Odisha Bhubaneswar India

ISBN: (纸本)9798350354379

Image colorization is a useful technique in various fields such as photography, art, and design. It can be used to restore old photos or add color to artistic renditions of real-world scenes and objects to make them appear more natural. It is a challenging task because it requires the model to understand the context and semantics of the image to predict accurate colors. The code uses a convolutional neural network (CNN) to predict the color of each pixel in an image based on its surrounding pixels. These techniques can be used for the restoration of old black and white photos. By colorizing these photos, they can be brought to life and preserved for future generations. This paper can benefit various domains such as art, history, and education. For instance, museums and historians can use this proposed method to enrich their collections and documents by adding color. © 2024 IEEE.

关键词： Pixels

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