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Segmentation of Neural Cells by Instance Using Deep Learning Methods

Segmentation of Neural Cells by Instance Using Deep Learning...

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2024 international conference on advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Ramya, V. Brindavathi, B. Varun, K. Dev, M.J.V.S.A. Satya Charan, T. Sai Anusha, V. Information Technology Gmr Institute Of Technology Rajam India Gmr Institute Of Technology Department Of Cse Rajam India

ISBN: (纸本)9798350371567

Neurological disorders, encompassing neurodegenerative diseases like Alzheimer's and brain tumors, pose a significant global health challenge, leading to high mortality and disability rates. Assessing the efficacy of treatments for these disorders remains challenging. Accurate instance segmentation, facilitated by computer vision, holds the potential for groundbreaking drug discoveries to address these disorders effectively. The precision of the current methods is restricted for neuronal cells, and the neuroblastoma cell line SH-SY5Y is a constant source of difficulties. The peculiar, asymmetric, and concave form of neuronal cells may cause this difficulty in segmenting them with traditional techniques. The paper applies many methodologies, including TL, Machine Learning, and Deep Learning techniques, to train and validate instance segmentation models targeting neuronal cells with excellent accuracy using phase contrast microscopy pictures. A successful outcome in this project holds the potential to advance neurobiology research by providing robust quantitative data, thereby facilitating easier measurement of the effects of diseases and treatments on neuronal cells. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Design and Development of Expense App

Design and Development of Expense App

引用

2024 international conference on advances in Computing Research on science engineering and technology, ACROSET 2024

作者： Girdhar, Gunit Kumar, Sachin Bhardwaj, Anuj Sharma, Megha Chandigarh University Dept. of Computer Science & Engineering Mohali India

ISBN: (纸本)9798350388800

With the help of Expense Tracker, you can easily and intuitively manage your revenue and costs while keeping track of your everyday, weekly, monthly, and annual spending. Users may choose the kind of expenditure, add location, upload more data, choose the amount of expense, and more. Additionally, the relational database will save this data. Users have the option to see and sort spending weekly, monthly, or annually. We may track the spending and minimize the amount of human computations for their costs by employing this. This allows a user to enter their income to get an estimate of their daily costs, and the results are saved for each individual user. Individuals who frequently go on excursions or to the theatre with friends might use this tracker to keep tabs on their spending. They will find it simple to split the bill using this tracker. This will show graphs according to the chosen perspective. Additionally, a user may write in this TR his monthly income or monthly expenditure cap. An integrated feature set is offered by this tracker system to assist you in managing your spending and cash flow. © 2024 IEEE.

关键词： Cost benefit analysis

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Green Time Extension Method of Transit Signal Priority Control for Phase Overlapping Intersection 2

Green Time Extension Method of Transit Signal Priority Contr...

引用

2nd international conference on Automation, robotics and Computer engineering, ICARCE 2023

作者： Yan, Yuting Liu, Haiqing Cui, Xue Yu, Chunyan Zhao, Yue College of Transportation Shandong University of Science and Technology Shandong China School of Transportation and Logistics Engineering Shandong Jiaotong University Shandong China Jinan Labor Information Technology Co. Ltd Shandong China

ISBN: (纸本)9798350308341

Aiming at the transit priority control problem under the existence of phase overlapping constraints at signalized intersection, this paper proposes a green time extension method based on non-linear programming. On the basis of the traditional ring-barrier, an improved phase signal timing structure is proposed to address the issue of overlapping. Further, a non-linear programming model for green time extension is presented, in which the objective function aims to optimize the transit priority effect while minimize the adjustments required for non-transit phases, and the constrains include minimum green time, maximum green time, overlapping rules and migration of coordinated phase. The numerical analysis results indicate that under the constraint of a fixed overlapping duration of 4 seconds and the existence of phase coordination, the proposed method can extend the green time for transit priority phases by 166.7% more than the traditional timing scheme. Moreover, the standard deviation of green time adjustments for non-transit phases is reduced by 43.1% compared to the traditional timing scheme under the same extension effect. © 2023 IEEE.

关键词： Nonlinear programming

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Retinal Blood Vessel Segmentation using The U-NetModel with the XAI Technique

Retinal Blood Vessel Segmentation using The U-NetModel with ...

引用

2024 IEEE international conference on Recent advances in science and engineering technology, ICRASET 2024

作者： Indikar, Muskan Baligar, Vishwanath P. Kle Technological University Department of Computer Science Karnataka Hubballi580031 India

ISBN: (纸本)9798350388602

Segmenting retinal blood vessels is important because the retinal microvasculature is a vital biomarker for severalclinical disorders. On the other hand, the conventional approach is thought to be a laborious and time-consuming process. The potential of deep learning and artificial intelligence in particular has created new opportunities for effectively segmenting retinal arteries. In our research, we provide a strategy to extract retinal blood vessels from fundus visuals using the U-Net model, whichis commonly used in semantic segmentation. A dataset for the study is obtained from the Kaggle resource. We performed pre-processing before feeding the images into the models based onthe need for deep-learning models. Moreover, we applied a U-Netmodel to segment the stereophotogrammetric converted images. Concerning the quality of the approach, the Intersection of Union (IoU), accuracy, and F1-Score measurements were employed. Furthermore, to enhance the grain of the segmentation results and to build confidence in the method we discuss the incorporation of eXplainable AI known as Grad-CAM. Therefore, this work shows that deep learning can be highly effective for retinal vessel segmentation and at the same time underlines the need for interpretability issues in medical image analysis. © 2024 IEEE.

关键词： Semantic Segmentation

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A Method for Predicting Cardiovascular Disorder using Machine Learning Techniques

A Method for Predicting Cardiovascular Disorder using Machin...

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2024 IEEE international conference on Recent advances in science and engineering technology, ICRASET 2024

作者： Hegde, Ramakrishna Pavithra, D.R. Shivashankara, S. Prasanna Kumar, G. Soumyasri, S.M. Nagashree, S. Vidyavardaka College of Engineering Affiliated to Visvesvaraya Technological University Belagavi India Jss Science and Technology University Sri Jayachamarajendra College of Engineering Mysuru India Kr Pete Krishna Government Engineering College Krishnarajapet India The National Institute of Engineering Mysore India Dept. of Computer Applications Vidya Vikas Inst. of Engg. and Tech. Affiliated to Visvesvaraya Technological University Belagavi India Vidya Vikas Institute of Engineering and Technology Mysuru India

ISBN: (纸本)9798350388602

Drastic increase of cardiovascular disease has led to a lot of adult's death. As per 'News 18' very year, 12 million young people in India die from heart disease. In most of these cases people experience cardiac arrest, some of which are normal. First, families of patients are vulnerable because it takes only a few minutes for a person to die of a heart attack, and it is difficult to get medical help in time. Second, most victims are under the age of 45. According to Dr. Maninder Sandhu, cardiologist and cardiologist at Artemis Hospital Gurgaon, 30 percent of people who suffer from heart disease are under the age of 45. This paper proposes a method that maybe used by laboratory technicians, doctors and users to understand whether there are any chances of having cardiovascular disease or not. The proposed system takes live data of patients and their lifestyle attributes these data are processed by the trained model to produce the output. In this model we haveused random forest classifier, linear regression, k-neighbors classifier & x gradient boost classifier. This model has attained maximum accuracy and least type one error in 'X- Gradient Boost'. We have concentrated more on type one error. The developed system shows whether the patient has any chances of cardiovascular disease or not. © 2024 IEEE.

关键词： Random forests

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Advancements in Additive Manufacturing: Optimizing Mechanical Properties Through AI-Based Design 1

Advancements in Additive Manufacturing: Optimizing Mechanica...

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1st international conference on advances in Computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Prakash, Jyoti Nahar, Rajni Raj, Abhishek Joseph Silvester, K. Pandey, Sujeet Kumar Patil, Pruthviraj Devidas Engineering Institute Kamla Nehru Institute of Physical and Social Sciences Uttar Pradesh Sultanpur India Kalinga University Department of Mechanical Engineering Chhattisgarh Raipur India B.I.T. Sindri Production & Industrial Engineering Department Jharkhand Dhanbad India Bannari Amman Institute of Technology Industrial Safety Engineering Department of Mechanical Engineering Tamilnadu Erode India Rajiv Gandhi Institute of Petroleum Technology Jais Bahadurpur Uttar Pradesh Amethi India JSPM's Rajarshi Shahu College of Engineering Department of Mechanical Engineering Pune India

ISBN: (纸本)9798331518578

Modern engineering has been transformed by additive manufacturing (AM), which makes complicated geometries and customized constructions possible. Nonetheless, there are so many interconnected variables that optimising mechanical properties is still difficult. To improve mechanical performance in AM, this work investigates AI-driven design optimization strategies. Tensile strength, material toughness, and weight reduction efficiency are predicted by utilizing machine learning models to analyze critical aspects like layer height, print speed, infill density, and extrusion temperature. Metrics like MSE, MAE, and R2 scores are used to compare the predictive accuracy of various AI models, such as neural networks, random forests, and support vector machines. The findings show that while using less material, AI-based optimization greatly increases structural integrity. The connections between print parameters and performance results are shown graphically. This study demonstrates how AI may simplify AM procedures, opening the door to more intelligent, high-performance manufacturing systems with improved productivity and material use. © 2025 IEEE.

关键词： Tensile strength

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Revolutionizing Healthcare: AI-Powered Solutions, Technological Integration for Accessible and Effective Medical Service

Revolutionizing Healthcare: AI-Powered Solutions, Technologi...

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2024 international conference on advances in Computing Research on science engineering and technology, ACROSET 2024

作者： Shaikh, Almas Jafar Takbhate, Sakshi Bharat Gawai, Shailesh Limbore, Altaf Mainoddin Patil, Vaidehi Avinash JSPM's Rajarshi Shahu College of Engineering Department of Computer Science and Business System Tathawade India

ISBN: (纸本)9798350388800

The main characteristics of the healthcare platform that this study suggests for rural areas are User ID or image-based recognition, Options for consultation, Disease Prediction, Integration with Aasha Workers, Technical considerations and Project Objectives. User ID or image-based recognition are used for a secure login process. Options for Consultation is to select from medical chatbots or video consultations with physicians. Disease Prediction is to make recommendations for possible disease diagnosis, real-time image analysis (such as reviewing uploaded photos) and machine learning are used. Risk assessment: Machine learning algorithms evaluate risks for different health disorders based on user-provided data (age, medical history, etc.). Integration with Aasha Workers is a platform establishes a connection with Aasha workers, who can provide medication as required following consultations. Technical considerations are a platform can be built using web development frameworks such as Flask or Django. Strong privacy and data security protocols are necessary. Project Objectives: Expand access to healthcare in remote locations. Give patients knowledge and tools for self-evaluation to empower them. Improve interaction and cooperation between medical professionals, patients, and Asha employees. © 2024 IEEE.

关键词： Adversarial machine learning

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Increasing Emergency Response Time: A Smart GPS and IoT-Based Approach for Ambulance Optimization 2

Increasing Emergency Response Time: A Smart GPS and IoT-Base...

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2nd IEEE international conference on advances in Information technology, ICAIT 2024

作者： Prajwal, V. Rahul, I.V. Sai Adithya, A. Chayadevi, M.L. Computer Science and Engineering BNMIT Bangalore India HOD Computer Science and Engineering BNMIT Bangalore India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

This paper proposes a novel approach to enhance ambulance response times and streamline patient transportation during emergencies by leveraging a smart GPS-based system integrated with Internet of Things (IoT) technology at traffic signals. Traditional methods, relying on camera detection or manual traffic clearance, often lead to delays in granting priority to ambulances. In contrast, our system pre-empts traffic signals in real-time, ensuring swift passage for ambulances by communicating with IoT devices installed at traffic control boxes. The proposed system optimizes ambulance routes using real time traffic data and machine learning algorithms, dynamically adjusting paths to avoid congestion. Components include a smart GPS navigation system, IoT devices at traffic signals, robust communication protocols, and centralized control systems. In summary what makes this design effective is its ability to prioritize clearing intersections ahead of emergency vehicles thus reducing time spent on responding to medical calls while at same time giving hope for better outcomes among patients. © 2024 IEEE.

关键词： Traffic signals

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Real-Time Hand Gesture Recognition and Sentence Generation for Deaf and Non-Verbal Communication

Real-Time Hand Gesture Recognition and Sentence Generation f...

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2024 international conference on advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Manikanta, K. Lavanya Shyam, N. Saraswathi, S. Sathyabama Institute Of Science And Technology Department Of Computer Science And Engineering India Sathyabama Institute Of Science And Technology India

ISBN: (纸本)9798350364828

To bridge the communication gap between deaf people and non-sign language users, the research project Real-Time Hand Gesture Recognition and Sentence Generation for Deaf and Non-Verbal Communication was created. The proposed system makes use of computer vision technology for real-time hand gesture identification and natural language processing to produce coherent and understandable words. In order to recognize hand gestures in real-time, the study employs deep learning techniques like convolutional neural networks (CNNs) and recurrent neural networks (RNNs). These models are trained on a dataset of hand gestures and are able to accurately classify different hand gestures in real-time. The system can recognize a wide range of hand motions, from simple ones like pointing and waving to complex ones involving several fingers. The system creates sentences that are consistent with the hand gesture's intended meaning once it has been identified. Natural language processing techniques are utilized to convert the recognized hand signal into understandable speech. Based on established rules and grammar suggestions, the program generates appropriate sentences while taking the context of the conversation into account. The proposed device may change communication for nonverbal and deaf individuals who have trouble expressing themselves. It gives these populations and the rest of society a quick, efficient way to communicate. © 2024 IEEE.

关键词： Recurrent neural networks

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Swarm Intelligence-Based Bio Inspired Algorithm for Patch Antenna Optimization

Swarm Intelligence-Based Bio Inspired Algorithm for Patch An...

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2024 IEEE international conference on Recent advances in science and engineering technology, ICRASET 2024

作者： Mahesh, D.S. Naveen, K.B. Bgs Institute of Technology Adichunchanagiri University Electronics & communication Engineering Department Karnataka Mandya India

ISBN: (纸本)9798350388602

the design of pentagon shape oriented patch antenna then its simulation, parameter optimization later on its fabrication and at the end it's testing is presented in this study. Optimization process reduces the antenna's dimensions to 5 cm by 5cm. For antenna optimization, the ant colony optimization algorithm (ACO) was selected. An ant colony optimization algorithm (ACO) was used to vary the side length and feeding location of the pentagon-shaped antenna, which was fed by a coaxial wire with a 1.59 mm diameter. The optimization aimed to identify parameters which improve the antenna's working efficiency. We have implemented the ACO Algorithm using MATLAB software. The antenna was designed using the Ansys HFSS program. MATLAB can integrate with Ansys HFSS software to facilitate optimization. The return loss and Voltage Standing Wave Ratio (VSWR) were the metrics taken into consideration. The ACO produced a design that was 5 cm by 5 cm in size and had a S11 of -27.96 dB and a Voltage standing wave ratio which was 1.08 at operating frequency of 2.4 GHz. using a network analyser, the constructed antenna was tested in an anechoic environment. The measurements obtained from the modelling and the fabrication of the antenna showed high agreement for the VSWR parameters and good performance in return loss at the 2.4 GHz operating frequency © 2024 IEEE.

关键词： MATLAB

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