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Multimodal Medical Image Registration Using Constraint Least Squares 15th

Multimodal Medical Image Registration Using Constraint Least...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Nagarathna, P. Jeelani, Azra Tirumala Vasu, G. Fiza, Samreen Kubra, Afreen Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Karnataka Bengaluru560064 India Department of Electronics and Communication Engineering Presidency University Karnataka Bangalore560064 India

ISBN: (纸本)9783031810794

Multimodal image registration finds widespread use in medical image analysis because of its usefulness in bringing together seemingly disparate sets of information from several imaging modalities. The major issue associated with image registration is that an algorithm works efficiently only for a given modality. In order to tackle this issue, we have proposed a novel multimodal image registration algorithm using Constrained Least Squares (CLS) that works for multiple medical modalities. The entire procedure of registration is based on the stipulation that the source and template images do not differ in zoom level but do differ in rotation and translation effects in the complex domain. To reduce the impact of noise and other inhomogeneities, a weight matrix is developed using prior knowledge of the local properties in the images. The least squares method is used to select the closed form solution to register the transformed image by estimating the displacement and rotation parameters. Many medical image registration algorithms have the limitation of quantitative analysis which we have addressed through Information Ratio (IR), Mutual Information Ratio (MIR), Lower bound Information Ratio (LIR), and Lower bound Mutual Information Ratio (LMIR). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image correlation

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IoT-driven Transformation of Poultry Farming: Ensuring Health, Hygiene, and Efficiency

IoT-driven Transformation of Poultry Farming: Ensuring Healt...

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

作者： Priyanka, K. Sridhara, N. Prasanna Kumar, M.J. Vijay Kumar, M.S. Nitte Meenakshi Institute of Technology Information Science & Engineering Department Bangalore India Adichunchanagiri University Bgs Institute of Technology Computer Science & Engineering Department BG Nagara India Mit Department of Ise Mysore India

ISBN: (纸本)9798350306927

This paper presents an innovative approach to poultry farming, leveraging cutting-edge technologies such as the Internet of Things (IoT), Wireless Sensor Networks (WSN), and edge computing. Unlike conventional manual monitoring systems, the proposed system deploys an array of smart sensors for continuous real-time monitoring of critical environmental parameters, including temperature, humidity, gas levels, and lighting. The data collected by these sensors are processed through edge computing, enabling autonomous control of farm actuators to optimize conditions for poultry health and production. A significant emphasis is placed on ensuring food and water hygiene within the poultry environment. To mitigate the risk of contamination and potential disease spread, the system incorporates precise food measurement mechanisms tailored to the specific age of the chicks. This meticulous approach guarantees the dispensing of the appropriate amount of fresh and uncontaminated food during each feeding cycle, thereby maintaining a high standard of hygiene at the feeder. Ultimately, this proactive system significantly contributes to safeguarding poultry health and enhancing overall farm productivity while prioritizing disease control. By implementing these health-conscious practices, the proposed system ensures the production of robust and healthy chickens, meeting the stringent standards demanded by consumers and reshaping poultry farming practices for greater sustainability and efficiency. © 2023 IEEE.

关键词： Internet of things

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Intrusion Detection System Using Improved Gloden Jackal optimization for Security and Privacy in Internet of Things 2

Intrusion Detection System Using Improved Gloden Jackal opti...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Bhagavatham, Naresh Kumar Al-Farouni, Mohammed Kumar, Komuravelly Sudheer Sundaram, S. meenakshi Puneeth, R.P. Vignana Bharathi Institute Of Technology Dept Of Computer Science And Engineering Hyderabad India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq Sr University School Of Computer Science & Artificial Intelligence Telangana Warangal India Nitte Meenakshi Institute Of Technology Department Of Ai&ds Bengaluru India Department Of Computer Science And Engineering Karnataka India

ISBN: (纸本)9798350381665

technology plays a primary role in rapid growth of service and identifying the quality of life. Recent technology such as Internet of Things (IoT) determines an impressive performance in the development of fast-forward. Intrusion Detection System (IDS) is employed as a lifeline to prevent attacks by categorizing activities as suspicious or normal. However, identifying the most relevant features from large data is challenging to effectively distinguish among suspicious or normal behavior. This research proposes the Improved Gloden Jackal optimization (IGJO) to select the appropriate features in IDS for security and privacy. GJO is improved by using tent mapping reverse learning for initializing populations, sine and cosine techniques are performed for updating prey positions which increases the ability of global exploration, and Cauchy mutation is used to acquire the optimal solutions. Initially, the CIC-IDS2017 dataset is used to evaluate the model performance and min-max normalization is utilized for normalizing the data. Then, IGJO is performed to select the appropriate features and Long Short-Term Memory (LSTM) is employed to classify IDS. The IGJO achieves a high accuracy of 99.95% compared to existing techniques respectively. © 2024 IEEE.

关键词： Internet of things

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Diabetes Mellitus Prediction and Classification Using Firefly Optimization Based Support Vector Machine 2

Diabetes Mellitus Prediction and Classification Using Firefl...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Chitradevi, B. Supriya Chandra, N. Subhash Chitradevi, T.N. Alabdeli, Haider Department of Computer Applications Trichy India Nitte Meenakshi Institute of Technology Dept of ISE Bengaluru India Cvr College of Engineering Department of Cse Telangana Hyderabad India Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode India The Islamic University Najaf Iraq

ISBN: (纸本)9798350382952

The Diabetes mellitus is the most dangerous disease all over the world and it is deadly disease determined by the rapid growth levels of the glucose in a blood. The Machine Learning (ML) approaches supports in the prediction and detection of the diabetes. This research aims to propose a Firefly Optimized-based Support Vector Machine (FSVM) for the prediction and classification of diabetes. The datasets utilized for diabetic prediction are PIMA Indian Diabetic dataset. The pre-processing is done by using the data normalization, cleaning as well as data splitting techniques. The Principal Component Analysis (PCA)-based feature selection approach is used for selecting the features in a acquired dataset. Finally, the ML-based algorithms are used for the classification of the diabetes disease. The effectiveness of proposed method is evaluated by PIMA dataset with the various performance metrices such as accuracy, precision, recall, and F1-score of values about 98.45%, 98.69%, 99.37%, and 98.25% respectively when compared to previous approaches such as Naïve Bayes, Integrated kernel as well as K-Nearest Neighbour. © 2024 IEEE.

关键词： Support vector machines

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Enhance Intrusion Detection by Analyzing the Behavior of Labeled and Unlabeled Classification to Obtain Better Accuracy 2

Enhance Intrusion Detection by Analyzing the Behavior of Lab...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Suriya Prakash, J. Srinidhi, N. Latha, A. Chaithra Kiran, S. Department of Computer Science and Engineering Karnataka Bengaluru India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Computer Science and Engineering Manipal India Sri Krishna Institute Of Technology Visvesvaraya Technological University Department of Computer Science and Engineering Karnataka Bengaluru India KSIT Department of Computer Science and Design Karnataka Bengaluru India Nitte Meenakshi Institute of Technology Department of Mathematics Karnataka Bangalore India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519056

Intrusion Detection System has accelerated globally as a result of the need to identify intrusions that occur in network data flow. The two IDS methods that are primarily used by machine learning and network pattern detection are anomaly-based and signature-based methods. A network can be made foolproof by keeping it safe by keeping an eye out for any hazardous activity. In network dataflow systems, network traffic can be produced via simulators. Simulators are a useful tool for detecting harmful behavior. Intrusions that are host-based, protocol-based, application protocol-based, network-based, and hybrid-based are some of the most prevalent kinds that have been discovered. In this paper,open source datasets ISP datasets and WIDE datasets are employed for experimentation. The crux of this paper is to carry out network classification by using minimal training sets and classifying maximal test sets for procuring accurate results. Lastly, the Labelled and Unlabelled technique is used to compare various accuracy outcomes. The suggested scheme's effectiveness is confirmed by the empirical investigation conducted on ISP and WIDE data. In this paper, the labeled and unlabeled classification proposed here identifies the intruder application with minimal labeled dataset. Most importantly automatic labeling of large test sets with small training set is accomplished. The analysis process for classification methods involving ISP and WIDE datasets by an experimental way has produced efficient classification results. The Labeled classification algorithm produces 93% accuracy and the unlabeled classification algorithm produces 84% accuracy. © 2024 IEEE.

关键词： Network intrusion

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Robust Fingerprint Minutiae Extraction and Matching using Improved AKAZE 2

Robust Fingerprint Minutiae Extraction and Matching using Im...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Reddy, R. Archana Al-Jawahry, Hassan M. Shruthi, B.S. Heswaran, K. Ma Pani, Alok Kumar Sr University School Of Computer Science And Engineering Telangana Warangal India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Department Of Civil Engineering Bengaluru India Department Of Computer Science Tiruchirappalli India Birla Global University School Of Applied Science Department Of Computer Science And Engineering Bhubaneswar India

ISBN: (纸本)9798350381665

Personal identification is important to protect data from cyber-attacks. Minutiae feature extraction and matching are key tasks for recognizing fingerprints and are essential to Automatic Fingerprint Recognition (AFR) systems. Fingerprint recognition technologies are becoming increasingly significant in preventing identity theft and ensuring accurate identification. However, the major problem is their identification sensors are vulnerable to presentation or spoofing attacks from the fake fingerprint samples. To overcome these problems, an improved AKAZE is introduced for feature extraction and feature matching in the process of fingerprint identification. Thinning and dilation methods are used for preprocessing the image and Improved AKAZE is used for feature extraction of the fingerprint images. After extracting features, the proposed Improved AKAZE is used for identifying and matching those fingerprint images. The proposed method is evaluated on publicly available FVC2004 dataset. The results show that the improved AKAZE achieved 99.95% of accuracy in identification and matching of fingerprint images. © 2024 IEEE.

关键词： Feature extraction

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Design of Software engineering Approach's for web learning applications using Cloud Computing 3

Design of Software Engineering Approach's for web learning a...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Sreenivasa, N. Naidu, P. Ramesh Naresh, E. Chandrakala, B.M. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Karnataka Bengaluru560064 India Manipal Academy of Higher Education Manipal Institute of Technology Bengaluru Department of Information Technology Manipal India Dayanandasagar College of Engineering Department of Information Science and Engineering Kumaraswamy Layout Bengaluru560078 India

ISBN: (纸本)9798350364569

Over the years, Cloud computing has significantly influenced various sectors, including Web learning, as education is considered crucial for individual and national growth. The primary objective is to develop an Application Model that supports Web learning Services, addressing the limitations of existing web learning systems in terms of infrastructure and effectiveness. Cloud technology provides a platform to host web learning applications on a service basis, accessible to end users through the internet via cloud infrastructure. This approach offers cost-effective packages for educational organizations, benefiting both trainers and learners. To achieve this objective, a combination of technologies is required. The paper further emphasizes the importance of Web learning Design features and analyzes the significance of cloud computing. It explores the implementation of a cloudbased application model for web learning systems, focusing on its working methodology, architecture design, development tools, and external interfaces. Additionally, it explores the use of software engineering approaches in this context. The paper highlights the importance of utilizing cloud environments for institutes and learners, emphasizing the potential benefits and opportunities in terms of design. © 2024 IEEE.

关键词： Cloud platforms

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Predictive Maintenance of Aircraft Engine using Empirical Mode Decomposition based Long Short Term Memory 2

Predictive Maintenance of Aircraft Engine using Empirical Mo...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Nabi, Shaik Abdul Naick, B. Raveendra Almusawi, Muntather Shruthi, B.S. Rana Veer Samara Sihman Bharattej, R. Avn Institute Of Engineering & Technology Department Of Computer Science And Engineering Telangana Hyderabad India Tirupati India The Islamic University Department Of Computers Techniques Engineering College Of Technical Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Department Of Civil Engineering Bengaluru India Saint Leo University Department Of Computer Science FL United States

ISBN: (纸本)9798350381665

In this modern era of Industry 4.0, efficiency and safety of industrial machines are a primary concern in trade sectors and prediction of Remaining Useful Life (RUL) of an engine helps to plan effectively maintenance operation. However, the Predictive Maintenance is to acquire reliable prognostics and estimation RUL in turbofan engines is a difficult task. To solve this problem, an Empirical Mode Decomposition based Long Short-Term Memory (EMD-LSTM) for prediction of RUL in aircraft engine utilizing C-MAPSS dataset. Initially, the most relevant sensor signals are selected by Correlation analysis and filtered using Moving Median Filter (MMF). After, the signals are normalized by Z-score technique to train the prediction model and signals are fed as input to proposed EMD-LSTM approach for prediction of RUL span of the engine. The evaluation results of the proposed method using performance metrics are Root Mean Square Error (RMSE), Score, Accuracy and Precision. The presented EMD-LSTM model achieved great accuracy of 99.3%, Precision of 98.9%, RMSE of 13.02 and Score of 282.0 which is greater than the previous models like Deep Convolutional Neural Network (DCNN)-LightGBM, LSTM, Graph CN (GCN), DCNN and Denoising Online Sequential Extreme Learning Machine (DOSELM). © 2024 IEEE.

关键词： Empirical mode decomposition

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Relocation Service Provider Application

Relocation Service Provider Application

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Innovation in technology (INOCON), IEEE International Conference for

作者： Shobha Shobha Ankita Choudhary Naindeep Kour Nitin Prakash P R Tejas Dept. of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India

This research aims to solve many problems people face when moving to a new place, such as housing, food, transportation, language differences and optimization. By creating recommendations, it is aimed to provide self-support to facilitate the transition process. The system will consider people's interests and consider different languages and cultures and budgets of people. This is useful in war or disaster environment.

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Utilizing Machine Learning for Sport Data Analytics in Cricket: Score Prediction and Player Categorization 3

Utilizing Machine Learning for Sport Data Analytics in Crick...

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3rd IEEE Mysore Sub Section International Conference, MysuruCon 2023

作者： Suguna, R. Praveen Kumar, Y. Suriya Prakash, J. Neethu, P.S. Kiran, S. Vel Tech Rangarajan Dr. Sagunthala RD Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vidhya Jyothi Institute of Technology Department of Computer Science and Engineering Hyderabad India Department of Computer Science and Engineering Bengaluru India School of Engineering and Technology Bengaluru India Nitte Meenakshi Institute of Technology Department of Mathematics Bangalore India

ISBN: (纸本)9798350340358

Cricket is a popular sport with complex gameplay and numerous variables that contribute to team performance. In recent years, sports analytics has gained significant attention, aiming to extract valuable insights from large volumes of cricket data. Cricket has many fans in India. With a strong fan following, many try to use their cricket intuition to predict the outcome of a match. A set of rules and a points system govern the game. The venue and the performance of each player greatly affect the outcome of the match. The game is difficult to predict accurately as the various components are closely related. The CRR (Current Run Rate) approach is used to predict the final score of the first innings of a cricket match. Total points are calculated by multiplying the average number of runs scored in each over by the total number of overs. For ODI cricket, these methods are useless as the game can change very quickly regardless of the current run rate. The game may be decided by 1 or 2 overs. For more accurate score predictions, a system is needed that can more accurately predict the outcome of an inning. This research paper explores the application of machine learning techniques to predict scores and classify players based on their roles in the squad. The study utilizes a comprehensive dataset comprising various attributes of cricket matches, including player statistics, match conditions, and historical performance. Linear Regression, Logistic Regression, Naive Bayes, Support Vector Machines (SVM), Decision Tree, and Random Forest regression models are employed to predict scores. Additionally, player categorization is performed using a classification approach. The results demonstrate the effectiveness of machine learning techniques in enhancing performance analysis and decision-making in the game of cricket. © 2023 IEEE.

关键词： Forecasting

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