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1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Kottath, Anu V Malarvel, Muthukumaran Hindustan Institute of Technology and Science Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350342796

The Monkeypox virus is a species of double stranded DNA virus that causes Monkeypox disease in humans and other mammals. The emergence of the Monkeypox virus poses a fresh threat to global health at a time, when the world continues to try to heal from the damage brought in by the widespread of COVID-19. Although the Monkeypox diseases are not that harmful or contagious as COVID-19, many new cases are reported from various nations across the world. As a result, it won't be shocking if there is another pandemic, because of insufficient precautions taken. Due to its resemblance to both chickenpox and measles, Monkeypox can be challenging to clinically diagnose in its initial stages. computer aided Monkeypox lesion detection may be useful for surveillance and the quick identification of suspected cases in circumstances where there is a difficulty in performing confirmatory Polymerase Chain Reaction (PCR) tests. Deep learning (DL) methods has shown enormous promise for image-based diagnosis including lung cancer detection, skin diseases detection, COVID-19 etc. Hence, the same application may be used to detect the Monkeypox diseases too. This paper evaluates four pre trained deep learning models (Squeeznet, DenseNet201, EfficientNetB0, ResNet50v2) for Monkeypox disease detection and six established metrics (Precision, Recall, Sensitivity, Specificity, F1-score and Accuracy) are used to analyze the results. This paper helps to identify the best pretrained model for Monkeypox diseases detection. A publicly available dataset is used in this work. © 2023 IEEE.

关键词： Chickenpox Deep Learning Machine learning Monkeypox Polymerase Chain Reaction (PCR)

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Revolutionizing Restaurant Service: Harnessing Tablets for Seamless and Touch-free Order Placement 4

Revolutionizing Restaurant Service: Harnessing Tablets for S...

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4th IEEE International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023

作者： Yadavalli, Kousthubh Thomas, Joevita Jose Santhosh, Sanjana Mel, Roger Nikalje, Snehal Fr. Conceicao Rodrigues Institute Of Technology Department Of Computer Engineering India

ISBN: (纸本)9798350384628

The hospitality industry has become a significant contributor to the tourism sector, and hotels are a crucial component of its development. However, many hotels struggle to keep up with technological advancements, leading to poor service and unmet customer expectations. To address these challenges, implementing a Hotel Management System can help streamline all aspects of a customer's dining experience. The solution is designed to improve the customer experience by simplifying the ordering and reservation process while also providing real-time business statistics that may assist restaurant owners and hotel management in making informed decisions. Additionally, the system's automated features can assist restaurant owners in managing their inventory, minimizing waste, and improving cost-effectiveness. The Hotel Management System can improve communication between staff and customers by creating a direct link via a website, reducing wait times and providing a more personalized and efficient service experience. © 2023 IEEE.

关键词： Hotels

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Deep Learning Model to Detect and Classify Bone Fracture in X-Ray ImagesBOOK_conf_196895_none_none_1of1_10394986

Deep Learning Model to Detect and Classify Bone Fracture in ...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Imrankhan, M. Varalakshmi, I. Arthi, J. Pavithra, P. Reshma, J. Manakula Vinayagar Institute of Technology Department of Computer Science and Engineering Puducherry India

ISBN: (纸本)9798350315127

The skeletal system of the human body is very important and bones are vital to it. A support and facilitate body movement. To detect fractures in bones, doctors use X-ray images, but this manual technique is laborious and prone to mistakes. Therefore, an automated approach for diagnosing bone fractures is required. Power electronic device modeling is a frequent use of deep neural networks, or DNNs. The proposed system, provides a deep neural network model using ResNet 50 to distinguish between bones that are healthy and damaged. But when faced with little data, our deep learning model has a tendency to over fit. The data augmentation techniques is used to increase the data set in order to overcome this problem. This stratergy uses 5-fold cross- validation and our model is able to discriminate objects with 92.44% classification accuracy. © 2023 IEEE.

关键词： Image classification

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Design and Implementation of Students Grievance and Database 2

Design and Implementation of Students Grievance and Database

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Kuhan, Sakthi Grace, L.K.Joshila Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665456302

Tracking student data and complaints is critical to track student performance in the classroom even while studying. This research study aims to address the grievances of the students and presents the website as a portal built using Javascript, HTML, Python, and MySQL, where students may file concerns, and the department handling the case is notified. The student has complete access to the complaint's history and may see if it has been examined, investigated, sent, rejected, or handled. The proposed technique is totally transparent, and if a complaint remains unanswered for several days, the system will automatically transmit it to the person specified further up the hierarchy. Any correctional system should be concerned about language quality since it may be used to spread incorrect information by making comments about people's gender, color, or religion. Employing the state-of-the-art technologies like deep learning and machine learning helps to detect hate speech. After training 11,325 tweets and analyzing the outcomes using evaluation metrics including F1 score, recall, and precision. Bi-LSTM, LSTM, and SVM models are utilized. By reaching the metrics values of 0.884, 0.84, and 0.86, the LSTM model outscores the other models. © 2023 IEEE.

关键词： Students

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Fuzzy Logic Based Deep Learning Approach (FRNN) for Autism Spectrum Disorder Detection

Fuzzy Logic Based Deep Learning Approach (FRNN) for Autism S...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Kavitha Nair, R. Ranjana, P. Hindustan Institute of Technology and Science Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350398465

Autism Spectrum Disorder (ASD) is regarded as a spectrum of developmental problems with neurological roots that affect communication, social interaction, and behavior. As it has a detrimental effect on both psychological and physical health, the anxiety experienced by the average individual concerns the physician, especially for those with ASD. Researchers are working very hard to understand the genetic underpinnings of autism and use the genetic information to choose logical targets for efficient therapy. International consortiums are using linkage analysis to pinpoint chromosomes and their correlation with ASD in order to pinpoint potential genetic illnesses. This study applies a recurrent neural network with fuzzy logic and provides an empirical assessment of how well it can diagnose autism using data from an MRI scan of the brain. To evaluate their performances, precision, sensitivity, specificity, and classification accuracy metrics are used. The experimental results show that FRNN, a deep learning-based classifier, produces the best outcome. © 2023 IEEE.

关键词： Recurrent neural networks

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Color and texture feature fusion for breast cancer classification using histopathology images 20

Color and texture feature fusion for breast cancer classific...

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20th IEEE India Council International Conference, INDICON 2023

作者： Deb, Dipti Dash, Ratnakar Mohapatra, Durga Prasad National Institute of Technology Rourkela Department of Computer Science and Engineering Rourkela India

ISBN: (纸本)9798350305593

Recent advancements in the fields of artificial intelligence and computer vision have made it possible to create computer-aided diagnosis (CAD) systems to detect breast cancer using histopathology images. In this research, a classification framework for breast cancer has been developed using histopathology images. In this regard, the color and texture features of the images are utilized for classification using several machine learning (ML) classifiers. For texture, three handcrafted feature extraction techniques, such as Gray-Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP), and Gabor have been investigated. Similarly, for color features, HSV histogram features and H, S, and V values of the HSV color model are used to classify the images. The features extracted from each method are evaluated individually. The best derived methods are combined and feed into different ML classifiers. The performance of each classifier is evaluated using performance metrics such as accuracy, precision, specificity, sensitivity, and F1-score. It is observed that LBP and HSV histogram with XGB classifier obtained an accuracy of 87.50% and 83.62%, respectively. After fusing the LBP and HSV histogram features with the XGB classifier obtained an accuracy of 93.34%. The experiment is carried out on the BreakHis dataset. The proposed methodology is useful in classifying breast cancer histopathology images with benign and malignant. © 2023 IEEE.

关键词： Breast Cancer Handcrafted features Histopathology images Machine Learning

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A Robust Hybrid Cryptosystem based on DNA Steganography 8

A Robust Hybrid Cryptosystem based on DNA Steganography

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Chakraborty, Debadrita Bhowmik, Bishal Bhowmik, Susmita Debbarma, Pankaj Tripura Institute of Technology Computer Science and Engineering Department Tripura Agartala India

ISBN: (纸本)9798350396638

Cryptography is a major concern in the field of computer networks and security of data. By encoding a plain message using cryptographic algorithms, cryptography converts a plain message into an unintelligible format for humans. The advancement in cryptographic algorithm is a fundamental issue for data transmission in today's world. The field of deoxyribonucleic acid (DNA) based cryptography offers the possibility of resolving the issue efficiently. The work presents an innovative technique of data encoding using DNA sequence. In the proposed DNA steganography technique, the data is encoded to facilitate in hiding a secret message on a sequence of DNA strand using its bases, namely, adenine (A), guanine (G), cytosine (C) or thymine (T), very easily. This algorithm introduces two rounds of encryption within the DNA sequence. The plain text is converted to ASCII (American Standard Code for Information Interchange) code, which is a 7-bit character code with each individual bit representing a unique character. The proposed technique has been tested using random user input and found to give an index of coincidence (IC) value of 0.28, approximately. © 2023 IEEE.

关键词： Cryptography

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Detection and Classification of Multiple Plant Diseases Using CNN 1

Detection and Classification of Multiple Plant Diseases Usin...

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1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Vijaya Shetty, S. Hemantha, N.A. Devaganamath, Anand Ananth, R. Alur, Abhishek M Nitte Meenakshi Institute of Technology Department of Computer Science Engineering Bangalore India

ISBN: (纸本)9798350323795

Plant diseases can have a significant influence on agricultural productivity, quality, and profitability. Early identification and precise diagnosis of plant diseases are essential for successful disease control, which may reduce the use of pesticides and minimize crop losses. In this study, a machine learning-based approach is developed to identify the diseases of various plants using leaf images. Dataset of leaf images of 14 different crops are collected, including apple, pepper, potato, raspberry, blueberry, cherry, peach, soybean, squash, strawberry, corn, grape, orange, and tomato. The images are preprocessed using techniques such as normalization, resizing, and augmentation, to reduce noise and increase the diversity of the dataset. Convolution neural network (CNN) model is used as the back-end and Flask for the front-end to create a user-friendly web application. An interpretability analysis is conducted to understand the features learned by the model, which can provide insights into the underlying causes of the diseases. The study shows the potential of machine learning approaches for early diagnosis and identification of plant diseases, which can assist farmers in taking prompt action and minimizing crop losses. This approach can be extended to other crops and can be integrated with other technologies, such as drones and sensors, for remote and real-time monitoring of crop health. © 2023 IEEE.

关键词： Crops

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A Comprehensive analysis of 5G Communication and Its Health Care Management Models 2

A Comprehensive analysis of 5G Communication and Its Health ...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Kumar Sagar, Lalit Goel, Dipti Gupta, Sonali Saraswat, Manish Sharma, Ashish Srm Institute of Science and Technology Delhi Ncr Campus Department of Computer Applications UP Modinagar India Raj Kumar Goel Institute of Technology Department of Information Technology UP Ghaziabad India Computer Science & Engineering Graphic Era Hill University Uttarakhand Dehradun India Lloyd Institute of Engineering & Technology Department of Mechanical Engineering UP Greater Noida India Gla University UP Mathura India Gla University Department of Computer Engineering & Applications UP Mathura India

ISBN: (纸本)9798350371055

This paper provides an in-intensity examination of 5G conversation and its health care management models. 5G era, the imminent 5th technology of cellular cell communications, is expected to revolutionize the cellular enterprise with its build-up of excessive velocity, low latency, and big capability. however, it also increases new demanding situations for healthcaredelivery, in addition to data protection, privateness and tool management. This paper will are seeking for to deal with those troubles by using exploring numerous 5G fitness care management fashions. extra mainly, this paper will discover the demanding situations of 5G in healthcare, how the current healthcare infrastructure can facilitate 5G's use, and the principle elements of numerous 5G healthcare management fashions. finally, this paper will discuss the destiny implications of 5G in healthcare, and what groups want to do in order to make certain the most green and cozy healthcare shipping. by means of delving into this rely, this paper will provide a complete view of 5G conversation and its healthcare control fashions, main to progressed healthcare delivery within the close to future. This paper presents a complete analysis of fifth-technology (5G) communique applied to healthcare control fashions. To make sure functionality and efficacy., 5G networks and their related services need to adhere to a strict set of requirements and requirements, which can be evaluated in regard to latency, community useful resource requirements, and safety protocols. several important additives and capabilities of 5G-enabled healthcare models are mentioned, which include far off patient monitoring offerings, personalized healthcare services, and pervasive healthcare get right of entry to. additionally, this paper discusses the measures for successful implementation of 5G technology into healthcare, in conjunction with some potential dangers and ethical issues that have to be taken under consideration. in the end, the pap

关键词： cellular demanding models revolutionize

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Collaborative, Privacy-Preserving Federated Learning Framework for the Detection of Diabetic Eye Diseases

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SN computer Science 2024年第8期5卷 1-10页

作者： Gulati, Seema Guleria, Kalpna Goyal, Nitin Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of Computer Science and Engineering School of Engineering and Technology Central University of Haryana Haryana Mahendergarh India

Diabetes Mellitus is one of the chronic metabolic disorders that is easily identified by elevation in blood sugar levels. The high blood sugar levels over time can lead to substantial damage to the blood vessels, heart, eyes, kidneys, and nerves. Diabetic Eye Diseases (DED) such as Diabetic Macular Edema (DME), Diabetic Retinopathy (DR), and glaucoma, are serious complications of diabetes that can result in irreversible vision loss. Thus, affecting the health and quality of life of the population worldwide. The primary objective of the proposed work is to evaluate the efficacy of a privacy-preserved Federated Learning (FL) framework for detecting DEDs. To detect DEDs a collaborative and privacy-preserving Federated Deep Learning (FDL) framework using a lightweight MobileNetV2 architecture is proposed. The aggregation scheme selected for the FDL framework is FedProx and to achieve a high accuracy score the proximal term, µ is tuned with values ranging from µ= (0.01, 0.02, 0.03, 0.04 and 0.05). The impact of the variance in the proximal term on model performance is analyzed, and the results show that higher µ (0.03,0.4 and 0.5) values provide more stable and consistently high precision, recall, and F1-scores. The findings suggest that FedProx aggregation successfully stabilizes the performance of the framework with the least standard deviation of 0.035 in accuracy at µ = 0.05. The balance between accuracy and stability is a key factor in the successful application of federated learning in disease prediction. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Artificial intelligence Collaborative training Diabetic eye diseases Federated learning Privacy-protection

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