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Recognition of Intracranial Hemorrhage with its Subtypes from CT Images using Deep Learning Approach 7

Recognition of Intracranial Hemorrhage with its Subtypes fro...

引用

7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Rashid, Md. Harun Or Ahmed, Boshir Department of Computer Science & Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350397253

Intracranial Hemorrhage (ICH) or Intracranial Bleeding is a severe problem for a person who rushes towards death unless it is recognized accurately within a short period. Commonly, it occurs within the skull or brain. Since the brain is the most crucial part of the human body and consists of soft tissue, its lesion detection, and treatment are so sensitive and also challenging within a faster time. The radiologist analyzes brain CT images to diagnose diseases consuming more time that affects a severe patient negatively. Being the process slow, non-automated, and costly, an automated deep learning approach is proposed to diagnose the problems accurately within the shortest time. For our research, the Radiological Society of North America (RSNA) dataset is considered, which consists of 752,803 training images and 121,232 test images. Normalization, windowing, and augmentation are used as preprocessing techniques on the training data, and during the model training, 30% of data are treated as validation data for hyper-tuning. After the successful completion of learning, this study has applied the test data to evaluate the performance of the proposed model, and it was observed that the proposed model performs better in the recognition of ICH with 98% accuracy. © 2023 IEEE.

关键词： Deep learning

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A Novel Customized Crypto Token Based Approach on Blockchain

A Novel Customized Crypto Token Based Approach on Blockchain

引用

2023 International Conference on Sustainable Emerging Innovations in engineering and Technology, ICSEIET 2023

作者： Taneja, Shweta Suri, Bhawna Dhawan, Omansh Ruhela, Devanshu Khatri, Kunal Singh, Mritunjay Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350329186

The world is moving from Web 2.0 to Web 3.0, leading to a rise in Blockchain technology and its applications like crypto-currencies, supply chains, and IOT. Tokenization of digital assets is necessary, but standards are hard to obtain due to their diverse nature. In this work, a custom crypto token has been created on an existing Internet computer Blockchain. Each token has a unique Value, Owner, and Symbol. This token has been implemented using Motoko at backend and React at frontend. Using Motoko language, smart contract has been established to govern the behavior of our token on the IC blockchain. The decentralized application manages the tokens, serving as an important digital asset in online trading. The token's unique properties and seamless integration provide a more accessible and secure platform for transactions, making it an important advancement in the field. © 2023 IEEE.

关键词： Smart contract

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The Smart Development of the Large Scale Sensing Techniques in Intelligent Industrial Automation 2

The Smart Development of the Large Scale Sensing Techniques ...

引用

2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Geetha, S. Mahesh, T.R. Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

Intelligent industrial automation is becoming increasingly popular, as companies look to automate processes and maximize efficiency. One way they are achieving this is through the use of large scale sensing techniques. These techniques enable companies to monitor the performance of their equipment and make adjustments if necessary. Large scale sensing techniques are used in industrial automation to detect physical quantities such as temperature, pressure, flow, and vibration. By using sensors, companies can detect problems before they become serious issues. This allows them to make changes and keep their machines running as efficiently as possible. Large scale sensing techniques are also used to monitor the environment in which machines are operating. This can help companies identify potential risks, such as high temperatures or humidity, and take the necessary measures to avoid damage. It can also help companies make better decisions about how to deploy their resources to maximize efficiency. Lastly, large scale sensing techniques can be used for predictive maintenance. By monitoring the performance of machines, companies can detect potential problems early on and take preventive measures before they become serious. This helps to avoid costly repairs and downtime, and ensures that companies are able to keep their machines running smoothly. © 2023 IEEE.

关键词： Efficiency

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Lightweight Privacy Preserving Framework at Edge Layer in IoT 1

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10th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2022

作者： Agrawal, Kavita Chittineni, Suresh Reddy, P. V. G. D. Prasad Subhadra, K. Diaz, Elizabeth D. Department of Computer Science and Engineering Chaitanya Bharathi Institute of Technology Hyderabad India Department of Computer Science and Engineering GITAM Deemed to be University Vishakhapatnam India Department of Computer Science and Systems Engineering Andhra University Vishakhapatnam India Department of Computer Science and Engineering University of Texas Arlington United States

ISBN: (数字)9789811975240

ISBN: (纸本)9789811975233

The Internet of Things is helping us to make our lives comfortable. IoT devices used here are resource-constrained in terms of memory and CPU as well as they lack basic security design. When everyone is using IoT, there are high chances of their personal data getting exposed to the outside world. For real-time application, data need to be processed at the edge layer directly instead of doing the entire processing and analysis at cloud. Privacy preserving at the edge layer in the Internet of Things is a challenging task. We proposed a lightweight privacy preserving framework that uses the concept of blockchain, zero knowledge proof algorithm, principal component analysis, and digital signatures. Here for the mining process in blockchain, Trust concept is being used instead of Proof of Work (POW) and Proof of Stake (POS) as it consumes fewer resources and our edge layer is resource constrained. This could be a feasible solution and can be deployed at the edge layer in the Internet of Things for preserving privacy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Real-Time Road Lane Detection for Self-driving Cars Using computer Vision 1

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1st International Conference on Machine Intelligence for Research and Innovations, MAiTRI 2023

作者： Gupta, Meenu Kumar, Rakesh Bisht, Archana Department of Computer Science and Engineering Chandigarh University Punjab Chandigarh India

ISBN: (数字)9789819981359

ISBN: (纸本)9789819981342

The design and implementation of autonomous vehicles are a rapidly evolving field of research and development. The purpose of this paper is to provide an overview of the design and implementation of road lane line detection. This paper provides an overview of the history and evolution of road lane line detection, describes the various technologies involved in their operation, and explores the challenges and future prospects of this emerging field. In this work, the current state of road lane line detection was reviewed using autonomous vehicles, research, and development in terms of environment sensing, pedestrian detection, path planning, motion control, and vehicle cybersecurity. Our objective is to study the various techniques proposed and to compare their approaches. For fully self-driving cars, these technologies need to be precise enough to gain public trust and be pinpoint accurate in their methods of solving these problems. The paper also explores public confidence and perceptions of motor vehicles. By discussing opportunities and barriers of road lane line detection using self-driving technology, the aim was to shed light on future possibilities. A camera is a widely used sensor for lane detection. computer vision and deep learning will be used for detecting the edges and preparing the model for the same. Also, canny-edge detection that uses a multi-stage algorithm to simulate the lane line was used. Then at last, Hough Transmission was used that converted pixels to a line and process frames to detect lanes. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Autonomous vehicles

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The Smart Performance Development of Geographic Routing in a Reliable Communication 2

The Smart Performance Development of Geographic Routing in a...

引用

2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Geetha, S. Mahesh, T.R. Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

Geographic routing is a routing method used in communication networks that is effective, dependable, and resilient. It is based on the actual locations of the network nodes. The source and destination nodes' geographic coordinates are used to determine the route, taking into consideration any obstacles or other nodes along the path. Geographic routing is especially useful in sensor networks where the nodes have limited capabilities. Geographic routing works by using the geographic coordinates of the nodes to select the shortest and most reliable path. The route is then determined by taking into account any obstacles or other nodes along the path. This ensures that the route is reliable and secure. The main advantages of geographic routing are its scalability, robustness, and reliability. The geographic routing is reliable. It is able to maintain a reliable and secure connection even in the presence of environmental changes. This is because the route is determined based on the geographic coordinates of the nodes, which are not affected by the environment. The geographic routing is an efficient, reliable and robust routing technique used in communication networks. It is based on the actual locations of the network nodes and can keep a safe and stable connection even when there are barriers or other nodes in the way. Additionally, it is very trustworthy, robust, and scalable. © 2023 IEEE.

关键词： Scalability

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Parameter Tuning in Automatic Detection Algorithm for Detecting Artificial Brain Shift 24

Parameter Tuning in Automatic Detection Algorithm for Detect...

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6th International Conference on Intelligent Medicine and Image Processing, IMIP 2024

作者： Watanabe, Kaoru Noborio, Hiroshi Koeda, Masanao Department of Computer Science Osaka Electro-Communication University Japan Faculty of Computer Science and Systems Engineering Okayama Prefectural University Japan

ISBN: (纸本)9798400710032

In neurosurgery to remove brain tumors, DICOM data, a medical imaging standard, is generated preoperatively using CT and MRI. This data is used for surgical planning. However, brain deformation problems, known as brain shift, occur during surgery, resulting in discrepancies from the preoperative surgical plan. Deviations from the surgical plan due to brain shift are a life-threatening problem as they reduce the success rate of surgery. Brain shifts have not yet been elucidated, and DICOM data acquired from MRIs and CTs for surgical planning remain stored and archived in hospital information centers. Here, it is planned to extract the physical parameters of the brain model from a large amount of brain deformation data using pre- and post-operative brain DICOM pairs, which are stored in large quantities in hospitals and discarded after a particular time. This study investigates the optimization of parameters in the brain shift detection algorithm. © 2024 ACM.

关键词： Digital Imaging and Communications in Medicine (DICOM)

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Physics-Based Modeling and Characterization of Wide GaN-HEMTs

Physics-Based Modeling and Characterization of Wide GaN-HEMT...

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2025 IEEE Topical Conference on RF/Microwave Power Amplifiers for Radio and Wireless Applications, PAWR 2025

作者： Nouri, Soheil Avval, Amirreza G. El-Ghazaly, Samir M. University of Arkansas Department of Electrical Engineering and Computer Science Fayetteville United States

ISBN: (纸本)9798331507275

In this paper, we model and characterize an unconventionally wide GaN HEMT device. Each finger of the device has a width of 150 μ m which allows the modeling methodology to examine the wave propagation effects at high frequency bands. The transistor simulation employs a hybrid model, integrating a simulator for the transport equations of the semiconductor device and an electromagnetic solver for the extrinsic components. By combining these two elements, we can achieve a more comprehensive and accurate simulation of the transistor's behavior. To assess the accuracy of our modeling technique, we compare the current gain predicted by the model with experimental measurements. The results demonstrate that our model is capable of accurately tracking the reduction in gain at frequencies beyond 50 GHz, highlighting its effectiveness in predicting high-frequency performance. © 2025 IEEE.

关键词： Gallium nitride

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Sustainable Farming: Crop Prediction and Detection of Crop Deficiency using Various Algorithms 4

Sustainable Farming: Crop Prediction and Detection of Crop D...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Unnathi, C.S. Kalpana Devi, S. Rv University Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540364

Selection of crop suitable to the environment and soil of the farm land is very crucial for sustainable agriculture. According to The CropLife India and Yes Bank report India faces an annual crop yield loss of approximately INR 2 lakh crores due to various factors, including not proper usage of fertilizer use and unsuitable crop choices for specific soil and climate conditions. Traditional farming methods often depends on intuition and experience, leading to inefficient resource utilization and reduced crop yields. The existing systems are particularly designed for large farms which leaves small scale farmers in despair. So, we have developed a model that predicts the best suited crop for that particular land by considering N, P, K levels, humidity, rainfall, temperature and pH levels of the soil and experimented with various algorithms such as Naïve's Bayes algorithm, Random Forest, Decision tree, SVM, Logistic Regression, Linear Regression, k-NN out of which Naïve's Bayes achieved highest accuracy of 99.54%. The proposed model not only gives the best suited crop but also tells what nutrients are deficient and need to be improved for better yield recommended by official government website of agriculture domain thereby contributing to both agricultural productivity and sustainability. This work underscores the potential of smart systems to support sustainable farming by enabling precise, informed decision-making that optimizes resource use and minimizes environmental impact. © 2024 IEEE.

关键词： Training Support vector machines Soil Predictive models Prediction algorithms Agriculture Bayes methods Sustainable development Crop yield Farming

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Customer Segmentation using Clustering Techniques: Data-Driven Approach to Enhance Marketing Strategy 5

Customer Segmentation using Clustering Techniques: Data-Driv...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Sakthi Priya, G. Vignesh Saravanan, K. Hemalatha, B. Karthika, R. Kavya Srinidhi, B. Ramco Institute of Technology Department of Computer Science and Engineering Rajapalayam India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

Customer segmentation is a vital strategy for businesses seeking to enhance their marketing efforts and optimize resource allocation. This research focuses on segmenting customers using the Mall Customer Segmentation dataset, which includes demographic and behavioral attributes such as age, gender, annual income, and spending score. The work begins with data preprocessing, involving the handling of missing values, encoding of categorical variables, and scaling of numerical features to prepare the data for analysis. Following preprocessing, exploratory data analysis (EDA) is conducted to visualize feature distributions and relationships, providing insights into the data's structure. The core of the research involves applying various clustering algorithms-K-Means, Hierarchical Clustering, and DBSCAN-to identify distinct customer segments. The optimal number of clusters is determined through methods like the Elbow Method and Silhouette Score. The clusters are then evaluated for quality, and their characteristics are analyzed to understand different customer segments. This segmentation enables businesses to their marketing strategies, enhance customer satisfaction, and make data-driven decisions based on customer behavior insights and demographics. © 2024 IEEE.

关键词： Sales

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