Deep Learning for Arrhythmia Classification: A Comparative Study on Different Deep Learning Models
Arrhythmia; Custom CNN; Deep Learning; Electrocardiogram; Inception V3; Machine Learning; VGG19
Arrhythmias, or irregular heart rhythms, are a major global health concern. Since arrhythmias can cause fatal conditions like cardiac failure and strokes, they must be rapidly identified and treated. Traditional arrhythmia diagnostic techniques include manual electrocardiogram (ECG) image interpretation, which is time consuming and frequently required for expertise. This research automates and improves the identification of heart problems, with a focus on arrhythmias, by utilizing the capabilities of deep learning, an advanced machine learning technique that performs well at recognizing patterns in data. Specifically, we implement and compare Custom CNN, VGG19, and Inception V3 deep learning models, which classify ECG images into six categories, including normal heart rhythms and various types of arrhythmias. The VGG19 model excelled, achieving a training accuracy of 95.7% and a testing accuracy of 93.8%, showing the effectiveness of deep learning in the comprehensive diagnosis of heart diseases. 2023 IEEE.
Teja N.B.; Ajay H.K.; Kumar R.S.; Deepa S.; Jayapriya J.; Vinay M.
IEEE 1st International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023
Institute of Electrical and Electronics Engineers Inc.
Teja N.B., Christ Deemed to Be University, Department of Computer Science, Bangalore, India; Ajay H.K., Christ Deemed to Be University, Department of Computer Science, Bangalore, India; Kumar R.S., Christ Deemed to Be University, Department of Computer Science, Bangalore, India; Deepa S., Christ Deemed to Be University, Department of Computer Science, Bangalore, India; Jayapriya J., Christ Deemed to Be University, Department of Computer Science, Bangalore, India; Vinay M., Christ Deemed to Be University, Department of Computer Science, Bangalore, India
Augmented Reality; Deep Learning; Neural Networks; Object Recognition; Visualization
This study describes an artificial intelligence (AI)-based object identification system for detecting real-world items and superimposing digital information in Augmented Reality (AR) settings. The system evaluates the camera stream from an AR device for real-Time recognition using deep learning algorithms trained on a collection of real-world items and their related digital information. Object recognition applications in AR include gaming, education, and marketing, which provide immersive experiences, interactive learning, and better product presentations, respectively. However, challenges such as acquiring larger and more diverse datasets, developing robust deep learning algorithms for varying conditions, and optimizing performance on resource-constrained devices remain. The AI-based object recognition system demonstrates the potential to transform AR experiences across domains, while emphasizing the need for ongoing research and development to fully realize its capabilities. 2023 IEEE.
Tyagi A.; Rajpal D.; David A.; Singh J.; Thakur H.K.; Upreti K.
OCIT 2023 - 21st International Conference on Information Technology, Proceedings, pp. 162-167.
Tyagi A., Bennett University, School of Computer Science Engineering and Technology, Greater Noida, India; Rajpal D., Bennett University, School of Computer Science Engineering and Technology, Greater Noida, India; David A., Bennett University, School of Computer Science Engineering and Technology, Greater Noida, India; Singh J., Bennett University, School of Computer Science Engineering and Technology, Greater Noida, India; Thakur H.K., Bennett University, School of Computer Science Engineering and Technology, Greater Noida, India; Upreti K., Christ University, Department of Computer Science, Ghaziabad, India
An Empirical and Statistical Analysis of Regression Algorithms Used for Mental Fitness Prediction
bipolar disorder; Gradient Boosting; K-Nearest Neighbors (KNN); Linear Regression; Random Forest; schizophrenia; Support Vector Regression (SVR); Theil Sen Regression
In today's focus on mental well-being, technology's capability to predict and comprehend mental fitness holds substantial significance. This study delves into the relationship between mental health indicators and mental fitness levels through diverse machine learning algorithms. Drawing from a vast dataset spanning countries and years, the research unveils concealed patterns shaping mental well-being. Precise analysis of key mental health conditions reveals their prevalence and interactions across demographics. Enriched by insights into Disability-Adjusted Life Years (DALYs), the dataset offers a comprehensive view of mental health's broader impact. Through rigorous comparative analysis, algorithms like Linear Regression, Random Forest, Support Vector Regression, Gradient Boosting, K-nearest neighbors and Theil Sen Regression are assessed for predictive accuracy. Mean squared error (MSE), root mean squared error (RMSE), and Rsquared (R2) scores are used to assess the predictive accuracy of each algorithm. Results show that Mean Squared Error (MSE) ranged from 0.030 to 1.277, Root Mean Squared Error (RMSE) from 0.236 to 1.130, and R-squared (R2) scores ranged between 0.734 and 0.993, with Random Forest Regressor achieving the highest accuracy. This study offers precise prognostications regarding mental fitness and establishes the underpinnings for the creation of effective tracking tools. Amidst society's endeavor to tackle intricate issues surrounding mental health, our research facilitates well-informed interventions and individualized strategies. This underscores the noteworthy contribution of technology in shaping a more Invigorating trajectory for the future. 2023 IEEE.
Jamadagni V.; Vedavalli D.; Priyadarshi S.; Vasudev S.; Prathap B.R.
Proceedings - 4th IEEE 2023 International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023, pp. 805-810.
https://doi.org/10.1109/ICCCIS60361.2023.10425084 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186500666&doi=10.1109%2fICCCIS60361.2023.10425084&partnerID=40&md5=d6ed9ec084972e2f3f1bbb3a4563ee85
Jamadagni V., PES University, Computer Science and Engineering, Bengaluru, India; Vedavalli D., PES University, Computer Science and Engineering, Bengaluru, India; Priyadarshi S., PES University, Computer Science and Engineering, Bengaluru, India; Vasudev S., PES University, Computer Science and Engineering, Bengaluru, India; Prathap B.R., Christ University, Computer Science and Engineering, Bengaluru, India
Streamlined Deployment and Monitoring of Cloud-Native Applications on AWS with Kubernetes Prometheus Grafana
ArgoCD; AWS; EC2; EKS; Grafana; Helm; Keda; Kubernetes; Prometheus
As organizations increasingly move their applications to the cloud, it becomes essential to have an efficient and cost-effective method for deploying and managing those applications. Manual deployment can be time-consuming, error-prone, and expensive. Additionally, managing logs and monitoring resources for each deployment can lead to even greater costs. To address these challenges, we propose implementing an automation strategy for deployment in the cloud. With automation, the deployment process can be streamlined and standardized across different cloud providers, reducing the potential for errors and saving time and resources. Furthermore, a central log system can be implemented to manage logs from different deployments in one location. This provides a unified view of all logs and allows for easier troubleshooting and analysis. Automation can also be used to set up monitoring resources, such as alerts and dashboards, across different deployments. Overall, implementing an automation strategy for deployment in the cloud can help organizations save time and resources while improving their ability to manage and monitor their applications. A centralized log management system can further enhance these benefits by providing a unified view of logs from all deployments 2023 IEEE.
Abirami T.; Mapari S.; Jayadharshini P.; Krishnasamy L.; Vigneshwaran R.R.
2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023, pp. 1149-1155.
https://doi.org/10.1109/ICAICCIT60255.2023.10465818 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189916623&doi=10.1109%2fICAICCIT60255.2023.10465818&partnerID=40&md5=997a30665beb19abc75cc9300090f9d5
Abirami T., Kongu Engineering College, Department of Information Technology, Erode, Tamilnadu, Perundurai, India; Mapari S., Symbiosis Institute of Computer Studies and Research (SICSR), Symbiosis International, Pune, India; Jayadharshini P., Kongu Engineering College, Department of Artificial Intelligence, Erode, Tamilnadu, Perundurai, India; Krishnasamy L., Christ University, School of Engineering and Technology, Department of Computer Science and Engineering, Bangalore, Kengeri, India; Vigneshwaran R.R., Kongu Engineering College, Department of Information Technology, Erode, Tamilnadu, Perundurai, India
Establishing colonies in Lunar and Martian environments is the major task of our primary means to become a multi-planetary civilization. The Space Exploration Initiative (SEI), administered by President George H.W. Bush in 1989, was the first spark that ignited humanity's vision to establish space settlements beyond low Earth orbit (LEO) (Marc M. Cohen, 2015). At present, private space companies (like SpaceX and Blue Origin) are competing to be the first ones to colonise space. From the late 1980s to the present space race, many space habitat designs to suit human factors, ensure protection from space radiation, and be capable of regulating our day-to-day activities have been proposed for both lunar and martian settlements, respectively. In this paper, only Martian settlements are focused, and the reason for that follows next. While the moon is closer to Earth than Mars, Mars has several other advantages that make it an equal, if not a better candidate for colonisation. Some of the reasons why martian colonisation is preferred over lunar colonization include the presence of an atmosphere on Mars, its resource-rich nature, and its rotation period being closer to Earth's rotation period (Mars has 24.5 hours per day, while the moon has 28-day days) (Kamrun Narher Tithi, 2017). Another added advantage is its proximity to the main belt asteroids, which will further increase the potential for space mining in the future. So this paper will be a review of the various Martian habitat designs proposed over the last one and a half decades in terms of their designs, construction and challenges. To do so, it is assumed that every step associated with delivering the habitats to the Martian environment is achievable. These steps include the following: propulsion systems for long-term spaceflights; launch vehicles capable of lifting the habitats and fitting the habitat modules within them (Marc M. Cohen, 2015). Copyright 2023 by the International Astronautical Federation (IAF). All rights reserved.
Proceedings of the International Astronautical Congress, IAC, Vol-2023-October
International Astronautical Federation, IAF
Subbiah M.S., Department of Physics and Electronics, Christ University, Dharmaram College Post, Hosur Road, Bengaluru, 560029, India; Quiros B., ULACIT, Costa Rica
Resource Aware Weighted Least Connection Load Balancing Technique in Cloud Computing
Cloud analyst; Cloud computing; load balancing; standard score; weighted least connection load balancing
Cloud computing became a pivotal for the most of the real time applications. In cloud computing, the customer demands the services with the best performance even when the application is expanding rapidly. Therefore, it is essential to manage the resources effectively because the number of users and services growing proportionately. The main aim of the load balancing technique is to allocate the customers' requests with the large pool of resources efficiently. The problem is how to evenly distribute the load of requests among the compute nodes according to their capacity. Therefore, there is a need for an effective load balancing technique for smooth continuity of operations in a distributed environment with a heterogeneous server configuration. This paper presents a novel load balancing technique, namely, Resource aware weighted least connection load balancing which addresses the above said problem efficiently. The essence of this work is to assign the requests across multiple servers based on the requested resource and the status of the number of connections presently served by each server. This work used standard score technique to enumerate the weight of each node. Experiments were conducted using Cloud Analyst, a famous cloud simulator breed from CloudSim. Appropriate performance parameters were analysed to measure the effectiveness of the proposed technique. Future directions for the extension of the implemented technique also identified. 2023 IEEE.
International Conference on Sustainable Communication Networks and Application, ICSCNA 2023 - Proceedings, pp. 153-157.
https://doi.org/10.1109/ICSCNA58489.2023.10370307 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183057447&doi=10.1109%2fICSCNA58489.2023.10370307&partnerID=40&md5=afeab97fba034ab9a160d612dca65a66
Balan Y., Christ University, Department of Computer Science, Bangalore, India; Arokia Paul Rajan R., Christ University, Department of Computer Science, Bangalore, India
Machine Learning Based Crime Identification System using Data Analytics
Convolutional Neural Network; Crime Identification; Data Analytics; Data mining; Deep Learning; Machine Learning; Support Vendor Machine
Poverty is known to be the mother of all crimes, and a vast percentage of people in India live below the poverty line. In India, the crime rate is rapidly rising. The police officers must spend a significant amount of time and personnel to identify suspects and criminals using current crime investigation. In this research, the method presented for designing and implementing crime identification and criminal recognition systems for Indian metropolitans is utilizing techniques of data mining. These occurrences are represented by 35 predefined crime attributes. Access to the crime database is protected by safeguards. The pending four subjects are important for crime unmasking, identification and estimation of criminals, and crime authentication, in that order. The detection of crime is investigated with the help of K-Means clustering, which iteratively builds two crime batches based on congruent criminal features. Google Maps is to enhance the k-means visualization. K-Nearest Neighbor classification is used to examine criminal identification and forecasting. This is used for the authentication of the results. The technique benefits society by helping investigative authorities in crime solving and criminal recognition, resulting in lower crime rates. This research study describes a way for creating and deploying crime solving and criminal recognition systems for Indian metro's using data mining tools in this study. The method consists of data evulsion, data pre- processing, clustering, Google map delegation and classification. The first module, data evulsion, retrieves unformed or unrecorded crime datasets from several criminal sources online from 2000 to 2012. In the second module, Data pre-processing cleans, assimilates, and reduces the obtained criminal data into organized 5,038 crime occurrences. Several predefined criminal traits represent these instances. Safeguards are in place to prevent unauthorized access to the crime index. The remaining components are critical for detecting crimes, criminal identity and prediction, and crime verification, in that sequence. The investigation of crimes is investigated using k-means clustering, which gives results repeatedly. 2023 IEEE.
Rajesh S.M.; Chiranmai I.; Jayapandian N.
International Conference on Sustainable Communication Networks and Application, ICSCNA 2023 - Proceedings, pp. 951-956.
Rajesh S.M., Department of Cse, Christ (Deemed to Be University), India; Chiranmai I., Department of Cse, Christ (Deemed to Be University), India; Jayapandian N., Department of Cse, Christ (Deemed to Be University), India
Weather Forecasting Accuracy Enhancement Using Random Forests Algorithm
Data Analytics; Deep Learning; Intelligence; Machine Learning; Random Forest; Regression; Weather Forecasting
In today's world, weather forecasting is essential for decision-making in a variety of fields, including agriculture, transportation, and disaster preparedness. It's not simple to make weather predictions. Today, both in business and academia, data analytics is growing in importance as a tool for decision-making. The adoption of data-driven concepts is for our graduates, enhancing their marketability. Data Analytics us a study belonging to science that analyses gathered raw data, which makes conclusions about the particular information. Data analytics has been used by many sectors recently, such as hospitality, where this industry can collect data, find out where the problem is, and manage to fix the problem. Nominal, ordinal, interval, and ratio data levels are the four types of data measurement. Applications of data analytics can be found in many industries, including shipping and logistics, manufacturing, security, education, healthcare, and web development. Any business that wants to succeed in the modern digital economy should make analytics a core focus. To make such data meaningful, a transformation engine was used with types from several sources. Ironically, this has made analytics harder for businesses. As businesses employ more platforms and applications, the amount of data available has grown tremendously. This article focuses on different applications of data analytics in the modern world. Weather forecasting is a highly intricate and multifaceted process that draws upon data from various sources. It relies on a combination of scientific studies and sophisticated weather models to decipher the vast amount of information available. 2023 IEEE.
International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023 - Proceedings, pp. 765-770.
Jahnavi A., Christ (Deemed to Be University), Department of Cse, India; Tanni N., Christ (Deemed to Be University), Department of Cse, India; Jayapandian N., Christ (Deemed to Be University), Department of Cse, India
Comparative Performance Analysis of Machine Learning and Deep Learning Techniques in Pneumonia Detection: A Study
Accuracy; Deep Learning; Machine Learning; performance; Pneumonia
Pneumonia is a bacterial or viral infection that inflames the air sacs in one or both lungs. It is a severe life-threatening disease, making it increasingly necessary to develop accurate and reliable artificial intelligence diagnosis models and take early action. This paper evaluates and compares various Machine Learning and Deep Learning models for pneumonia detection using chest X-rays. Six machine learning models -Logistic Regression, KNN, Decision Tree, Random Forest, Naive Bayes, and Support Vector Machines - and three deep learning models - CNN, VGG16, and ResNet - were created and compared with each other. The results exhibit how just the model choice can significantly affect the quality and inerrancy of the final diagnostic tool. 2023 IEEE.
Shankar A.P.; Shah S.; Ghosh S.; Ismail Z.; Vinay M.; Jayapriya J.; Deepa S.
2023 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023
https://doi.org/10.1109/ICCCNT56998.2023.10306711 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179854381&doi=10.1109%2fICCCNT56998.2023.10306711&partnerID=40&md5=bd9df23d5b55d6e62c6aea602b492e2a
Shankar A.P., Christ University, Karnataka, Bengaluru, India; Shah S., Christ University, Karnataka, Bengaluru, India; Ghosh S., Christ University, Karnataka, Bengaluru, India; Ismail Z., Christ University, Karnataka, Bengaluru, India; Vinay M., Christ University, Karnataka, Bengaluru, India; Jayapriya J., Christ University, Karnataka, Bengaluru, India; Deepa S., Christ University, Karnataka, Bengaluru, India
Brain Tumor Detection and Classification Using a Hyperparameter Tuned Convolutional Neural Network
Brain Tumor detection; Convolutional neural network; Data pre-processing; MRI scans; Prediction
Brain tumor detection using MRI scans when integrated with a deep learning approach can be immensely applied in identifying the tumor at early stages, with minimum medical professional aid. This research paper aims to develop an advanced predictive model that accurately classify brain tumors as benign or malignant using MRI scans. Here, a novel convolutional neural network (CNN) model is proposed to automate tumor detection and improve diagnosis accuracy. The model used a dataset of around 7000 brain cancer data classified into 4 labels which include glioma, meningioma, pituitary, and no tumor. Data wrangling and pre-processing are then applied to unify the images into a single format and remove any inconsistencies. Further the records are segregated into train and test samples with a 70-30 split. The proposed model recorded an optimum accuracy of 94.82%, precision of 94.2%, recall value of 93.7% and f-score metric of 93.9% respectively. In conclusion, the paper concluded that the proposed model can be applied to enhance the precision of both brain tumor diagnosis and prognosis. 2023 IEEE.
Banerjee A.; Jaiswal K.; Biswas T.; Sharma V.; Bal M.; Mishra S.
Proceedings of International Conference on Contemporary Computing and Informatics, IC3I 2023, pp. 502-506.
Banerjee A., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Jaiswal K., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Biswas T., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Sharma V., CHRIST (Deemed to Be University, Computer Science Department, Delhi NCR, India; Bal M., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Mishra S., Kalinga Institute of Industrial Technology, Deemed to Be University, India
Evaluating Energy Consumption Patterns in a Smart Grid with Data Analytics Models
Accuracy; Big data analytics; Decision tree; Effective energy management; Smart grid
With the rapid pace of technological advancement, it is a well established fact that in todays era, economical and industrial development go hand in hand with the growth in technology. Today, massive amounts of data are generated everyday and are only growing exponentially. The collected data, whether structured or unstructured, could prove to be very beneficial in terms of improving operational efficiency by analyzing and extracting valuable information to find patterns to optimize asset utilization and improve asset intelligence. Big data analytics can very well contribute to the evolution of the digital electrical power industry. The objective of this paper is to explore how smart grid technology can be enhanced by leveraging big data analytics. Different predictive models are used for the purpose. Among them, decision tree model outperformed others recording a training and tetsing accuracy of 94.4% and 92.7% respectively while noting a least execution latency of only 4.3 seconds. 2023 IEEE.
Mishra S.; Sivani T.; Swain T.; Sharma V.; Alkhayyat A.
2023 2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023, pp. 1072-1077.
https://doi.org/10.1109/SmartTechCon57526.2023.10391483 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184828877&doi=10.1109%2fSmartTechCon57526.2023.10391483&partnerID=40&md5=31941dce418d1efd7ec6b557a62ea0db
Mishra S., School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India; Sivani T., School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India; Swain T., School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India; Sharma V., CHRIST(Deemed to be University), Delhi-NCR, India; Alkhayyat A., College of Technical Engineering, The Islamic University, Najaf, Iraq
Implementing Ensemble Machine Learning Techniques for Fraud Detection in Blockchain Ecosystem
Block Chain; Ensemble ML; Fraud Detection
A new era of digital innovation, notably in the area of financial transactions, has been conducted in by the rise pertaining to block-chain technology. Although the decentralized nature of blockchain technology renders it prone to fraud, it has been praised for its capacity to offer a safe and transparent platform for financial transactions. The integrity of the entire blockchain network may be compromised by fraudulent transactions, which may also damage user and stakeholder trust. This study aims to assess machine learning's efficacy in detecting fraudulent transactions within blockchain networks and identifying the most effective model. To achieve its objectives, this study used a combination of data collection, data preprocessing, and machine learning techniques. The data used in this study was dataset of blockchain transactions and pre-processed using techniques such as feature engineering and normalization. Then trained and evaluated using several machine learning models, including Logistic Regression (LR), Naive Bayes (NB), SVM, XGboost, LightGBM, Random Forest(RF), and Stacking, in order to determine their effectiveness in detecting fraudulent transactions. XGBoost demonstrated the highest accuracy of 0.944 in the stacking model, establishing it as the top-performing model, closely followed by Light GBM. The study's discoveries offer significant practical implications for advancing fraud detection methods in blockchain networks. By pinpointing the most efficient machine learning model and crucial predictive fraud features, this research provides vital insights for refining precise detection algorithms, enhancing blockchain network security, and broadening their reliability across various applications. 2023 IEEE.
Josephine H.V.L.; Kumar P.T.; Joy J.; Mansurali A.
2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023 - Proceedings, pp. 1980-1985.
Josephine H.V.L., Business analytics, School of business and Management, Christ University, Karnataka, Bangalore, India; Kumar P.T., Lean Operations and Systems., School of business and management, Christ University, Karnataka, Bangalore, India; Joy J., Lean Operations and Systems., School of business and management, Christ University, Karnataka, Bangalore, India; Mansurali A., Department of Management, Central University of Tamil Nadu, Tamil Nadu, Thiruvarur, India
Predicting the Cerebral Blood Flow Change Condition during Brain Strokes using Feature Fusion of FMRI Images and Clinical Features
deep learning; feature fusion; Imaging; prediction; strokes
By fusing clinical information with functional magnetic resonance imaging (fFMRI) pictures, this study describes a novel method for predicting changes in cerebral blood flow during brain strokes. The FMRI data and patient-specific variables, such as age, gender, and medical history, are combined via feature fusion in the proposed technique. As a result, the model developed can accurately forecast changes in cerebral blood flow that occur during brain strokes. The efficiency of the suggested strategy is shown by experimental findings. The performance of the model is greatly enhanced when FMRI data and clinical characteristics are combined as opposed to just one data source. The findings of this study have important ramifications for increasing the accuracy of stroke diagnosis and treatment and, eventually, for bettering patient outcomes. The experimental results showed that the proposed method a high level of accuracy in predicting changes in cerebral blood flow after brain strokes. The performance of the model was much enhanced by combining clinical characteristics with FMRI data as opposed to using only one of these data sources. This emphasizes the value of including pertinent clinical information in the diagnosis and management of stroke. 2023 IEEE.
Sharma V.; Sinha A.; Wiryaseputra M.; Kumar B.; Kumar T.R.; Alkhayyat A.
Sharma V., Christ University, Institute of Information Technology, Delhi Campus, India; Sinha A., Ignou, Department of Computer Science and Information Technology, New Delhi, India; Wiryaseputra M., Soegijapranata Catholic University, Semarang, Indonesia; Kumar B., Amity Univeristy, Department of Computer Science, Jharkhand, India; Kumar T.R., Iit Madras, Computational Mathematics & Data Science, India; Alkhayyat A., The Islamic University, College of Technical Engineering, Najaf, Iraq
Lung Cancer Detecting using Radiomics Features and Machine Learning Algorithm
Feature Extraction; Image Classification; Lung Cancer; Machine Learning; Radiomics; Selection
Lung Cancer Incidence across the globe is the second leading cancer type tallying to about 2,206,771 during 2020 and is estimated to rise to about 3,503,378 by 2040 for both male and female sexes and for all ages accounting to 11.4% as per Globocan 2020 [1]. It is the leading death-causing cancer. Lung Cancer [2] in broad terms encompasses Trachea, bronchus as well as lungs. Purpose: The study is aimed to understand Radiomics based approach in the identification as well as classification of CT Images with Lung Cancer when Machine Learning (ML) algorithms are applied. Method: CT Image from LIDC-IDRI [4] Dataset has been chosen. CT Image Dataset was balanced and image features by PyRadiomics library were collected. Various ML features classification algorithms are utilized to create models and matrices adopted in judging their accuracies. The models, distinctive capacity is assessed by receiver operating characteristics (ROC) analysis. Result: The Accuracy scores and ROC-AUC values obtained for various Classification Model are as follows, for Ada Boosting, the accuracy score was 0.9993 ROC-AUC was 0.9993 and followed by GBM, the accuracy score was 0.9993, was 0.9992. Conclusion: Extracting texture parameters on CT images as well as linking the Radiomics method with ML would categorize Lung Cancer commendably. 2023 IEEE.
Proceedings - International Conference on Technological Advancements in Computational Sciences, ICTACS 2023, pp. 1479-1485.
Rajesh M.N., Jain (Deemed-to Be-University), Electronics and Communication Engineering, Bengaluru, India; Tanni N., Christ (Deemed to Be University), B.Tech in Computer Science and Engineering (Data Science), Bengaluru, India; Baig Z.T., Amity University, Dept of It, Taskent, Uzbekistan
Performance Evaluation of OTFS Under Different Channel Conditions for LEO Satellite Downlink
Detection; Doppler; LMMSE; Low Earth Orbit; MRC; OTFS; Satellite
Orthogonal Time Frequency Space (OTFS) modulation scheme is being actively pursued as a viable alternative to Orthogonal Frequency Division Multiplexing (OFDM) modulation scheme in future wireless standards due to the inherent ability of OTFS to mitigate the Doppler effects in high mobility scenarios. The inclusion of Non Terrestrial Network (NTN) in Release 17 of 3GPP (3rd generation partnership project) New Radio (NR) standard signifies the vital role of Satellite Communications to achieve coverage extension, capability augmentation and seamless global connectivity. In this context, it becomes important to study the suitability of OTFS modulation scheme with respect to satellite channel scenarios. In this paper, we consider the downlink channel scenarios defined by 3GPP NR NTN for Low Earth Orbit (LEO) satellites at sub-6 GHz and millimetre wave frequencies for evaluating the performance of OTFS modulation schemes. Simulation results using LMMSE (Linear Minimum Mean Square Error) and MRC (Maximum Ratio Combining) detection algorithms confirm that OTFS modulation is highly robust against Doppler effects and performs consistently across all channel conditions. From simulation results, it is observed that the performance of iterative MRC detection is better than LMMSE for 16QAM and 64QAM modulation schemes by achieving respective gains of around 5 dB and 10 dB for corresponding Bit Error Rate (BER) values of 0.01 and 0.1. 2023 IEEE.
Proceedings - 10th International Conference on Wireless Networks and Mobile Communications, WINCOM 2023
Devarajalu S.K., Christ University, Department of Ece, Bengaluru, India; Jose D., Christ University, Department of Ece, Bengaluru, India
Wearable Leaf-Shaped Slotted Antenna Including Human Phantom for WBAN Applications
Complete ground; human phantom; ISM; Leaf pattern; Slot antenna; WBAN
A 5.8 GHz leaf-shaped slotted antenna for Wireless Body Area Network (WBAN) applications is presented in this piece of content. The leaf structure includes tri leaves, having a complete ground plane at the lowest floor and a central circle slot. The suggested antenna is 60 mm by 60 mm by 1.16 mm in total dimensions. The ISM (Industrial Science and Medical) band frequency of 5.8 GHz is covered by this antenna's radiation range of 5.5 to 6.4 GHz. The radiated pattern, efficiency, S11 magnitude and gain were the different attributes of the leaf-patterned slot antenna. The creation of a stylish leaf-shaped antenna that can be incorporated into clothing designs is the main goal of this project. This antenna may be used in difficult situations because of its flexible base and conductive fabric. The method considers the needs of wearable antennas, such as the impact of human interactions on this antenna, as well as the opposite. 2023 IEEE.
Annalakshmi T.; Neethu P.S.; Revathi G.; Vanitha L.; Sushanth S.G.; Joy S.
IEEE 9th International Conference on Smart Structures and Systems, ICSSS 2023
Annalakshmi T., S.A.Engineering College, Ece Department, Chennai, India; Neethu P.S., School of Engineering and Technology Christ (Deemed to Be University), Ece Department, Bangalore, India; Revathi G., Saveetha School of Engineering, Ece Department, Chennai, India; Vanitha L., S.A.Engineering College, Ece Department, Chennai, India; Sushanth S.G., School of Engineering and Technology Christ (Deemed to Be University), Bangalore, India; Joy S., New Horizon College of Engineering, Ece Department, Bangalore, India
Image Analysis of MRI-based Brain Tumor Classification and Segmentation using BSA and RELM Networks
Brain Tumor Segmentation; Central Nervous System (CNS); FuzzyClustering Method (FCM)
Brain tumor segmentation plays a crucial role in medical image analysis. Brain tumor patients considerably benefit from early discovery due to the increased likelihood of a successful outcome from therapy. Due to the sheer volume of MRI images generated in everyday clinical practice, manually isolating brain tumors for cancer diagnosis is a challenging task. Automatic segmentation of images of brain tumors is essential. This system aimed to synthesize previous methods for BSA-RELM-based brain tumor segmentation. The proposed methodology rests on four fundamental pillars: preprocessing, segmentation, feature extraction, and model training. Filtering, scaling, boosting contrast, and sharpening are all examples of preprocessing techniques. When doing segmentation, a clustering technique based on Fuzzy Clustering Means (FCM) is used to breakdown the overall dataset into numerous subsets. The proposed approach used the region of filling for feature extraction. After that, a BSA-RELM is used to train the models with the input features. The proposed technique outperforms BSA and RELM, two of the most common alternatives. There was a 98.61 percent success rate with the recommended method. 2023 IEEE.
Saranya R.; Pushpakumar R.; Srinivasan P.; Aparna N.; Karthika K.; Chauhan A.
2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023 - Proceedings, pp. 523-528.
Saranya R., Department of Computer science with Data Analytics, PSG College of Arts and Science, Coimbatore, India; Pushpakumar R., Department of Information Technology, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Tamil Nadu, Chennai, India; Srinivasan P., Department of ECE, Sona College of Technology, Tamilnadu, Salem, India; Aparna N., Department of Biomedical Engineering, Dhaanish Ahmed Institute of Technology, Tamil Nadu, Veerappanur, India; Karthika K., Civil Engineering, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, India; Chauhan A., Department of Life Sciences, School of Sciences, CHRIST (Deemed to be university), Karnataka, Bengaluru, India
Optimizing Kidney Ultrasound images through Pre-Processing Filters
edge detection; enhancement; Filters; Kidney images; Mean Standard Error (MSE); noise; Peak-Signal-Noise-Ratio (PSNR); Pre-processing; quality; Ultrasound (US)
Medical image processing and analysis have greatly advanced in the past decade, significantly contributing to the diagnosis of various diseases.However, It is crucial to address the need for effective data management in the medical field due to the significant rise in data generation and storage. It necessitates the exploration of compression methods as a means of achieving efficient data handling. Consideration should be given to image processing approaches to minimize redundancy. Ultrasound imaging has gained importance in recent years, but the presence of artifacts in ultrasound images has complicated diagnoses. An evaluation has been performed to identify appropriate Pre-processing techniques for kidney images before extracting kidney features. Observing the sensitivity and calculating the PSNR and MSE of the filtered image are used to assess the applied methods. The results indicate that the median filter is ideal for image quality enhancement, while the Sobel filter is highly effective in detecting kidney edges. 2023 IEEE.
2023 1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023
https://doi.org/10.1109/ICAEECI58247.2023.10370900 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183531754&doi=10.1109%2fICAEECI58247.2023.10370900&partnerID=40&md5=9b10f832d53eb781d5a2eb4faeb32225
Kausar F., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, 560029, India; Ramamurthy B., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, 560029, India
Mapping the Landscape of Business Intelligence Research: A Bibliometric Approach
Diamonds; Estimated values; Machine learning; Regression Models and Cross validation
The integration of Business Intelligence (BI) is an essential element in contemporary enterprises, facilitating the conversion of voluminous data into valuable insights to support informed decision-making. Consequently, a considerable body of literature has been devoted to investigating the utilization of Business Intelligence (BI) in enhancing company efficiency and competitiveness. The present investigation employs bibliometric methods as a means to examine the research pertaining to Business Intelligence (BI). This includes an examination of the main writers and universities, publication patterns, and the intellectual framework of the domain. This investigation centers on the timeframe spanning from 2000 to 2022 and scrutinizes a corpus of 3729 Scopus articles pertaining to business intelligence. The findings suggest that the domain of Business Intelligence (BI) has experienced a substantial expansion recently. The study's results reveal significant contributors, establishments, nations, and references in the discipline, along with developing research patterns and prospects for further investigation. In general, this research emphasizes the significance of bibliometric evaluation as a means of comprehending the present status of BI research and discovering approaches to enhance the utilization of BI in contemporary organizational decision-making procedures. This study has the potential to provide valuable insights into the present state of research within the field, pinpoint significant trends and themes, and highlight potential avenues for future research. 2023 IEEE.
Basha M.S.A.; Raju R.P.K.; Uma Devi A.; Raja S.; Maheshwari A.
7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023 - Proceedings
Basha M.S.A., Gitam School of Business, Gandhi Institute of Technology and Management, Deemed to Be University, Bengaluru, India; Raju R.P.K., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Uma Devi A., Srm Valliammai Engineering College, Department of Management Studies, Chengalpattu, India; Raja S., School of Management, Vel-Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology, Avadi, India; Maheshwari A., School of Commerce, Finance and Accountancy Christ (Deemed to Be University), Ghaziabad, India
AUTONOMOUS IOT MOVEMENT IN HOSTILE AREAS USING ROBOTICS AND DEEP FEDERATED ALGORITHMS
Autonomous IoT; Deep Federated Algorithms; Hostile Areas; Robotics
Innovative solutions are required when Internet of Things (IoT) devices are deployed in hostile or difficult locations to ensure dependable and effective operation. In order to enable autonomous IoT mobility in such challenging circumstances, this study suggests a novel approach integrating robotics and deep federated algorithms. Robotics and IoT can work together to create a system that can adapt to dangerous environments, extreme weather conditions, and unexpected terrain. Deep federated algorithms further improve system performance by facilitating dispersed device collaboration for learning while protecting data privacy. The suggested framework covers the issues of communication stability, energy optimization, and real-time decision-making. We illustrate the practicality of this strategy in strengthening the dependability and efficiency of IoT deployments in hostile situations through simulations and tests. 2023 IEEE.
Archana U.; Roslin Dayana K.; Jayadurga R.; Sahaana G.; Praveena K.; Saravanan V.
2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023
Archana U., Department of Computer Science and Design, R.M.K. Engineering College, Tamilnadu, Kavaraipettai, India; Roslin Dayana K., Department of Computer Science and Engineering, R.M.D. Engineering College, Tamilnadu, Kavaraipettai, India; Jayadurga R., Department of Computer Science, Christ University, Yeshwantpur(YPR) Campus, Karnataka, Bengaluru, India; Sahaana G., Department of ECE, Veltech Multitech Dr Rangarajan Dr Shakuntala Engineering College, Tamilnadu, Chennai, India; Praveena K., Department of ECE, Mohan Babu University, Erstwhile Sree Vidyanikethan Engineering College, Andhra Pradesh, Tirupati, India; Saravanan V., Department of Computer Science, Dambi Dollo University, Oromia Region, Dambi Dollo, Ethiopia