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Annals of Emerging Technologies in Computing 2024年第2期8卷 1-14页

作者： John, Sincy Danti, Ajit Department of Computer Science and Engineering Christ University India

In the realm of deep learning, the prevalence of models with large number of parameters poses a significant challenge for low computation device. Critical influence of model size, primarily governed by weight parameters in shaping the computational demands of the occlusion removal process. Recognizing the computational burdens associated with existing occlusion removal algorithms, characterized by their propensity for substantial computational resources and large model sizes, we advocate for a paradigm shift towards solutions conducive to low-computation environments. Existing occlusion riddance techniques typically demand substantial computational resources and storage capacity. To support real-time applications, it's imperative to deploy trained models on resource-constrained devices like handheld devices and internet of things (IoT) devices possess limited memory and computational capabilities. There arises a critical need to compress and accelerate these models for deployment on resource-constrained devices, without compromising significantly on model accuracy. Our study introduces a significant contribution in the form of a compressed model designed specifically for addressing occlusion in face images for low computation devices. We perform dynamic quantization technique by reducing the weights of the Pix2pix generator model. The trained model is then compressed, which significantly reduces its size and execution time. The proposed model, is lightweight, due to storage space requirement reduced drastically with significant improvement in the execution time. The performance of the proposed method has been compared with other state of the art methods in terms of PSNR and SSIM. Hence the proposed lightweight model is more suitable for the real time applications with less computational cost. © 2024 by the author(s).

关键词： Generative adversarial networks

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Secure approach to sharing digitized medical data in a cloud environment

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Data science and Management 2024年第2期7卷 108-118页

作者： Kukatlapalli Pradeep Kumar Boppuru Rudra Prathap Michael Moses Thiruthuvanathan Hari Murthy Vinay Jha Pillai Department of Computer Science and Engineering CHRIST(Deemed to be University)Bengaluru560074India Department of Electronics and Communication Engineering CHRIST(Deemed to be University)Bengaluru560074India

Without proper security mechanisms, medical records stored electronically can be accessed more easily than physical files. Patient health information is scattered throughout the hospital environment, including laboratories, pharmacies, and daily medical status reports. The electronic format of medical reports ensures that all information is available in a single place. However, it is difficult to store and manage large amounts of data. Dedicated servers and a data center are needed to store and manage patient data. However, self-managed data centers are expensive for hospitals. Storing data in a cloud is a cheaper alternative. The advantage of storing data in a cloud is that it can be retrieved anywhere and anytime using any device connected to the Internet. Therefore, doctors can easily access the medical history of a patient and diagnose diseases according to the context. It also helps prescribe the correct medicine to a patient in an appropriate way. The systematic storage of medical records could help reduce medical errors in hospitals. The challenge is to store medical records on a third-party cloud server while addressing privacy and security concerns. These servers are often semi-trusted. Thus, sensitive medical information must be protected. Open access to records and modifications performed on the information in those records may even cause patient fatalities. Patient-centric health-record security is a major concern. End-to-end file encryption before outsourcing data to a third-party cloud server ensures security. This paper presents a method that is a combination of the advanced encryption standard and the elliptical curve Diffie-Hellman method designed to increase the efficiency of medical record security for users. Comparisons of existing and proposed techniques are presented at the end of the article, with a focus on the analyzing the security approaches between the elliptic curve and secret-sharing methods. This study aims to provide a high level of s

关键词： Electronic medical records Cloud computing Data privacy Attribute-based encryption Authentication

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An Ensemble Approach Using ResNet and DenseNet for Cataract Detection 7th

An Ensemble Approach Using ResNet and DenseNet for Cataract ...

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7th International Conference on Soft Computing and Signal Processing, ICSCSP 2024

作者： Jenefa, J. Vinodha, D. Sambandam, Rakoth Kandan Vetriveeran, Divya Department of Computer Science and Engineering Christ University Bangalore India

ISBN: (纸本)9789819609239

Cataracts represent a widespread ocular condition that profoundly affects an individual’s vision and overall quality of life. Timely detection proves crucial for effective treatment, yet existing methodologies often entail invasive and discomforting procedures. Hence, an innovative approach is proposed for cataract detection utilizing an ensemble framework, which presents numerous significant advantages. It uses an ensemble framework amalgamating ResNet and DenseNet pre-trained learning models for cataract detection. This strategy enhances the precision and dependability of diagnosing cataracts. On the other hand, it diminishes false positives and negatives, consequently ensuring more accurate and timely diagnoses. Beyond mere accuracy, our ensemble framework brings about additional benefits. It bolsters the resilience of cataract detection by mitigating the influence of individual model biases and variances. Furthermore, it enhances the system’s adaptability, making it applicable to various patient demographics and ocular conditions. Such adaptability is significant in the global healthcare landscape, facilitating effective deployment across diverse regions and populations. Moreover, our approach alleviates the discomfort and invasiveness associated with conventional cataract detection methods, promoting early diagnosis and reducing patient apprehension. Streamlining the diagnostic process also eases the burden on healthcare providers and improves overall patient care. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Nutrition

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Enhanced Lung Cancer Detection Using Graph Convolutional Networks (GCN) Approach 4

Enhanced Lung Cancer Detection Using Graph Convolutional Net...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Moozhippurath, Bineesh Jayapandian, N. Christ University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331518981

Early detection of lung cancer is a critical factor in improving the survival rates of patients. In the meantime, current methods of diagnosis often do not cover the complete process as they bypass the disease's initial stages. According to the survey, it suggests a unique way to lung cancer prediction involvement of Graph Convolutional Networks (GCNs) in the acquisition of diagnostic data to raise the accuracy and exactness of diagnostic models. This approach converts patient data from a tabular format to a graph structure, with each node representing a patient and edges representing similarities between two separate clinical features. The proposed GCN model is based on the idea of a multi-layer architecture, enabling it to effectively access both local and global patterns in the data, which in turn ensures the elapse of the performance of prediction. The model was evaluated with thoroughness and it showed the potential to replace the traditional machine learning algorithms in identifying the lung cancer cases correctly at a higher precision. Our methodology unites the advantages of graph convolution networks, so that it can reach our aim of greater accuracies and precision, which gives a good recognition for our approach of early lung cancer detection. The outcomes of the experiments confirm the value of graph-based deep learning models in medical diagnostics especially when the case is about complex tasks like lung cancer prediction. This study reflects the ongoing efforts on reliable and accurate diagnostic tools with the use of AI, thus facilitating the early detection of lung cancer and increasing patient's survival. © 2024 IEEE.

关键词： Lung cancer

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Theoretical Framework for Integrating IoT and Explainable AI in a Smart Home Intrusion Detection System 2

Theoretical Framework for Integrating IoT and Explainable AI...

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Dixit, Mahi Siby, Swetha Maria Jenefa, J. Vetriveeran, Divya Sambandam, Rakoth Kandan Vinodha, D. CHRIST University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350383652

Using IoT devices in smart homes brings benefits and security dangers. This study extensively examines various intrusion detection methods within smart home environments. It also suggests a novel hybrid intrusion detection theoretical framework integrating IoT data with Explainable Artificial Intelligence (XAI) approaches. Using information from multiple IoT devices, including motion sensors, door/window sensors, cameras, and temperature sensors, our theoretical framework can create a comprehensive image of the home environment. By effectively detecting new threats, it offers anomaly detection utilizing unsupervised learning approaches to discover potential breaches without tagged data. © 2024 IEEE.

关键词： Smart homes

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Non-destructive silkworm pupa gender classification with X-ray images using ensemble learning

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Artificial Intelligence in Agriculture 2022年第1期6卷 100-110页

作者： Sania Thomas Jyothi Thomas Department of Computer Science and Engineering Christ(Deemed to be University)BangaloreIndia

Sericulture is the process of cultivating silkworms for the production of ***-quality production of silk without mixing with low quality is a great challenge faced in the silk production *** of the possibilities to overcome this issue is by separating male and female cocoons before extracting silk fibers from the cocoons as male cocoon silk fibers are finer than *** study proposes a method for the classification of male and female cocoons with the help of X-ray images without destructing the *** study used popular single hybrid varieties FC1 and FC2 mulberry silkworm *** shape features of the pupa are considered for the classification process and were obtained without cutting the cocoon.A novel point interpolation method is used for the computation of the width and height of the *** dimensionality reduction methods are employed to enhance the performance of the *** preprocessed features are fed to the powerful ensemble learning method AdaBoost and used logistic regression as the base *** model attained a mean accuracy of 96.3%for FC1 and FC2 in cross-validation and 95.3%in FC1 and 95.1%in FC2 for external validation.

关键词： Sericulture Gender classification Stratified k-fold cross-validation Machine learning AdaBoost

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Internet of Things Enabled Smart Hand Gesture Virtual Mouse System 14

Internet of Things Enabled Smart Hand Gesture Virtual Mouse ...

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14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Chelekkodan, Danish Muneer Jayapandian, N. Abhinav, S. Shyjan, Aaron Department of Computer Science and Engineering Christ University Kengeri Campus Bangalore India

ISBN: (纸本)9798331531935

This research is aim to focus on IoT based hand gesture model. Mouse is one of the most important input devices of a computer. It works as a pointing device and allows the user to move the pointer as needed by the user. In the early days, a wired mechanical mouse was used for this purpose. In mechanical mouse a ball is fixed underneath the mouse, which rotates as the user moves the mouse. This movement of the ball is used to move the mouse pointer on the screen. Now mostly we use optical mouse which can be wired or wireless. An optical mouse has a high-power laser below it, which takes more than thousand pictures of the surface below the mouse. An image comparator compares the images and sends the signal to move the mouse pointer as the texture of the image changes. Both the types of mouse works based on old technology. As technology leaps to greater heights, the need for simplicity also increases. With the invention of different kind of sensors, microcontrollers and other electronics, we can eliminate the mouse as an input device and instead use our hands to do the work of a mouse. This prototype is an embedded system which runs with the help of an arduino microcontroller. Flex sensors are used to capture the hand gestures. The proposed IoT based hand gesture model is providing high accuracy rate compare to the regular model. The proposed model is analyzed with accuracy level, the average accuracy level of proposed model is more than 90%. © 2025 IEEE.

关键词： Comparators (optical)

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Handwritten Telugu Character Recognition Using Machine Learning 2

Handwritten Telugu Character Recognition Using Machine Learn...

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

作者： Karapu, Bhavesh Madhusudhan Anoop, G.L. Elappila, Manu Mithun, B.N. Christ Deemed to Be University Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350382952

The Telugu language is the most prominent representative within the Dravidian language family, predominantly spoken in the southeastern regions of India. Handwritten character recognition in Telugu has significant applications across diverse fields such as healthcare, administration, education, and paleography. Despite its importance, the Telugu script differs significantly from English, presenting distinct challenges in recognizing characters due to its complexity and diverse character shapes. This study explores the application of machine learning, particularly delving into deep learning techniques, to improve the accuracy of Telugu character recognition. This paper proposes a model to recognize handwritten Telugu characters using Convolutional Neural Network (CNN). The proposed study demonstrates the accuracy in identifying diverse handwritten Telugu characters. We assess the system's performance against conventional and machine learning methodologies and preprocess an extensive dataset to guarantee strong model training. The proposed model excels in accurately predicting visually similar but distinct characters, achieving an impressive accuracy rate of 96.96%. © 2024 IEEE.

关键词： Character recognition

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Brain Image Classification Using Time Frequency Extraction with Histogram Intensity Similarity

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computer Systems science & engineering 2022年第5期41卷 645-660页

作者： Thangavel Renukadevi Kuppusamy Saraswathi P.Prabu K.Venkatachalam Department of Computer Technology-UG Kongu Engineering CollegeErodeIndia Department of Computer Science CHRIST(Deemed to be University)BengaluruIndia Department of Computer Science and Engineering CHRIST(Deemed to be University)BengaluruIndia

Brain medical image classification is an essential procedure in computer-Aided Diagnosis(CAD)*** methods depend specifically on the local or global *** fusion methods have also been developed,most of which are problem-distinct and have shown to be highly favorable in medical ***,intensity-specific images are not *** recent deep learning methods ensure an efficient means to design an end-to-end model that produces final classification accuracy with brain medical images,compromising *** solve these classification problems,in this paper,Histogram and Time-frequency Differential Deep(HTF-DD)method for medical image classification using Brain Magnetic Resonance Image(MRI)is *** construction of the proposed method involves the following ***,a deep Convolutional Neural Network(CNN)is trained as a pooled feature mapping in a supervised manner and the result that it obtains are standardized intensified pre-processed features for ***,a set of time-frequency features are extracted based on time signal and frequency signal of medical images to obtain time-frequency ***,an efficient model that is based on Differential Deep Learning is designed for obtaining different *** proposed model is evaluated using National Biomedical Imaging Archive(NBIA)images and validation of computational time,computational overhead and classification accuracy for varied Brain MRI has been done.

关键词： Histogram differential deep learning convolutional neural network

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WSETO: wild stock exchange trading optimization algorithm enabled routing for NB-IoT tracking system

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International Journal of Information Technology (Singapore) 2024年第8期16卷 4895-4905页

作者： Sivadevuni, Sreeparnesh Sharma Naveen, J. Department of Computer Science and Engineering School of Engineering and Technology Christ University Karnataka Bangalore 560001 India

The Narrowband Internet of Things (NB-IoT) communication plays a significant role in the IoT due to the capability of generating broad exploration with the usage of limited power. Over the past few years, the Low Power Wide Area Networks (LPWAN) have been efficient in the data acquisition and remote monitoring area however they failed to generate high data rates, low latency, and the consumption of low power. To solve these problems, NB-IoT technology has developed in long-term asset tracking and it replaces the Global Positioning System (GPS) with its ubiquitous coverage. In this research, the Wild Stock Exchange Trading Optimization technique (WSETO) is proposed for a routing-based NB-IoT tracking system. The WSETO is the combination of the Wild Geese Algorithm (WGA) and SETO. By employing WSETO, the routing to the relevant target location is established effectively. The existing techniques like Low Power Asset Tracking of NB-IoT (LoPATraN), Monitoring system based on NB-IoT and BeiDou System/GPS (BDS/GPS), and Narrowband Physical Uplink Shared Channel (NPUSCH) are used to compare the WSETO approach. In rounds with a value of 2000, the WSETO demonstrates a superior location error of 0.001 in comparison to existing methods such as LoPATraN, a monitoring system utilizing NB-IoT and BDS/GPS, as well as NPUSCH. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Global positioning system Low power wide area networks Narrowband internet of things Stock exchange trading optimization Wild geese algorithm

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