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2nd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2022

作者： Pawar, Varsha Jose, Deepa V Patil, Ashwini Department of Computer Science Bengaluru India CMR Institute of Technology Department of Computer Applications Bengaluru India

ISBN: (纸本)9781665491112

People of all ages and genders are using social media platforms to engage themselves in all sorts of activities. People create profiles on online social networks in order to communicate with one another in this virtual environment. Hundreds or thousands of friends and followers are split across many profiles. Along with the virtual communication in this social media life, cyber-crimes also creep in many distinguished forms to grab user's information and emotionally degrade them with harassment and arrogant behavior. A set of machine learning methods are proposed and used to detect such a bullying behavior. Along with the detection of such an act, the model should also provide the logical reasoning of the evidence extracted. The explain ability of the models classification will give us a view of the way towards portraying a suspect as a bullier. This paper illustrates a machine learning model that works on a twitter data set to suggest the tweets as category bullying or non-bullying. LIME a tool to predict the interpretability of the model is used to depict the performance of model and provides explainability. © 2022 IEEE.

关键词： Lime

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Image Forgery Detection Using Deep Learning 7th

Image Forgery Detection Using Deep Learning

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7th International Conference on Smart Trends in Computing and Communications, SmartCom 2023

作者： Koshy, Litty Chithu, K. Department of Computer Science and Engineering SCMS School of Engineering & Technology Ernakulam India Master of Computer Applications SCMS School of Engineering & Technology Ernakulam India

ISBN: (纸本)9789819908370

Since cameras are so widely available, taking pictures has become more and more common. In order to gain more information, it is frequently necessary to enhance photographs, which are crucial in our daily lives as memories or sources of wealth of information. There are many tools accessible to enhance the quality of photos;however, some of them are also widely used to alter images, leading to the dissemination of false information. This makes picture forgeries more severe and frequent, which is now a major cause of concern. To identify fake images, many conventional methods have been developed over time. Convolutional neural networks (CNNs) have drawn a lot of interest recently, and they have had an impact on the area of picture fraud detection as well. However, the majority of CNN-based picture forgery detection methods currently used in the literature are restricted to identifying a certain kind of fraud (either image splicing or copymove). As a result, a method that can quickly and precisely identify any hidden forgeries in a picture is needed. We present a powerful deep learning-based approach for detecting picture forgeries in this study. The RGB format images converted to error level analysis (ELA) are used to train our model. The suggested model is compact, and its effectiveness shows that it outperforms cutting-edge methods in terms of speed. The experiment's findings are promising, with a 91.37% total validation accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

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Word Segmentation and Sandhi Resolution on Ayurveda Classical Scriptures 6

Word Segmentation and Sandhi Resolution on Ayurveda Classica...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Amrita Varshini, E.R. Nair, Jayashree Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350398496

Sanskrit is one of the oldest of the Indo-Aryan languages which has an abundant amount of texts in literature. One of the main sources of these Sanskrit texts is the textbooks written about the field of Ayurveda, the ancient Indian medical system. However, proper computational systems for processing this language are bare. A very challenging part of processing the Sanskrit language is the task of Sandhi resolution, which means the splitting of phonetically merged words. This study mainly focuses on this complex task of joint compound splitting and Sandhi resolution in Sanskrit specific to Ayurveda texts. This paper also surveys and compares all the existing research on Sandhi resolution in Sanskrit. In this work, an Ayurvedic dataset is compiled from some popular Ayurvedic texts, and a performance analysis of the state-of-the-art word segmentation model is carried out using this dataset. © 2023 IEEE.

关键词： Computational linguistics

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Revolutionizing Healthcare by Accessing the Opportunities for Virtual and Augmented Reality 7

Revolutionizing Healthcare by Accessing the Opportunities fo...

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

作者： Mehta, Kapil Aayushi Singh, Chamkaur Chugh, Himani Kumar, Manibhushan Chandigarh Group of Colleges Department of Computer Science and Engineering Mohali Punjab Landran India Chandigarh Group of Colleges Department of Computer Applications Mohali Punjab Landran India

ISBN: (纸本)9798350397253

In recent years, the use of new technologies in the healthcare industry has substantially increased. In particular, this research study explores the potential benefits of virtual reality (VR) and augmented reality (AR). With the use of these technologies, difficult judgments can be made by using the information and specialized expertise. Given that maintaining the health is among the most fundamental human aspirations, the healthcare industry affects every one of us. This paper discusses how advances in artificial intelligence, ARvR and blockchain technology will spur innovation and transform the healthcare sector. This research study witnesses the unparalleled growth in tailored, intelligent healthcare that is accessible everywhere at a reasonable cost when these technologies merge. The fusion of mobile healthcare and support, AR/VR for medical training, and customized pharmaceuticals based on health data from sensors implanted in the human body can give therapies that are specifically tailored to a person's genetic makeup. The acceptance and integration of VR in the healthcare industry are also discussed in this paper. It examines the development, the benefits, and the drawbacks, and offers viewpoints on the potential expansion and effects of these technologies in the future. The use of AR and VR in healthcare services may lead to problems with huge data availability and trustworthy sharing. Some systems that use software-type algorithms to simulate human cognitive processes in healthcare decision-making handle the privacy of a patient's data. The dentist and the support staff oversee managing data, which carries a risk of data privacy and security violations. This study examines how the use of VR and AR is allowing decentralized care, personalized treatment, and a new generation of intelligent detection and prevention. © 2023 IEEE.

关键词： Blockchain

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Credit Risk Customers Categorization with Random Forest Classifier using Various Searching Techniques

Credit Risk Customers Categorization with Random Forest Clas...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Samprith, S. Rajesh, T.M. Babu, Tina Nair, Rekha R Kishore, S. Dayananda Sagar Univerity School of Engineering Department of Computer Science & Engineering Bengaluru India Dayananda Sagar University School of Engineering Department of Computer Science & Engineering Bengaluru India Alliance Univerity Department of Computer Science and Engineering Bengaluru India Nitte University Nitte Institute of Professional Education Department of Computer Applications Karnataka India

ISBN: (纸本)9798350313413

Effective credit risk assessment holds significant importance for financial institutions and businesses extending credit to customers. It is crucial to evaluate customers' creditworthiness to minimize default risks and manage financial exposure. This study focuses on enhancing credit risk customer classification through optimized feature selection and classification techniques. The dataset is preprocessed using Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance and enhance performance. Relevant features are then extracted utilizing Cfs Subset evaluation along with various search algorithms. The optimized features are subsequently employed for classification using the Random Forest (RF) classifier. Experimental results reveal that, in credit customer classification, Evolutionary, Tabu, and Scatter search algorithms combined with RF classifier demonstrate a commendable accuracy of 90.31%. The proposed framework also showcases comparable outcomes in terms of other performance metrics. This automated credit customer classification method proves to be highly effective in identifying fraudulent users. © 2023 IEEE.

关键词： classifier Credit risk Detection methods Search Techniques

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Evidence Acquisition in Social Media for Cyber Crime 4

Evidence Acquisition in Social Media for Cyber Crime

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4th International Conference on Cognitive Computing and Information Processing, CCIP 2022

作者： Pawar, Varsha Jose, Deepa V CMR Institute of Technology Department of Computer Applications Bengaluru India Department of Computer Science Bangalore India

ISBN: (纸本)9781665456487

Social Media forensics a branch of forensics involves in collecting the evidences for the cyber crime. Investigating social media is a complex process which involves the privacy issues for accessing the users, suspects and victims information on social media. Manual processing of social media data is not feasible as it contains large volumes of data. An automated process is needed to incident specification, evidence extraction and for provenance. The need for handling heterogeneity of data as users have accounts with multiple social websites is also explained. This study briefs the existing models and the challenges faced in analyzing with those models. The research goals in this field are also addressed. A pool of tools which can contribute in guarding the solution for cyber crime is also presented. © 2022 IEEE.

关键词： Cybersecurity

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Growth models for the progress test in Italian dentistry degree programs: Growth models for the progress..: G. Biscardi et al.

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Quality and Quantity 2025年第2期59卷 1445-1459页

作者： Biscardi, Giulio Grilli, Leonardo Rampichini, Carla Antonucci, Laura Crocetta, Corrado Department of Statistics Computer Science Applications University of Florence Firenze Italy Department of Clinical and Experimental Medicine University of Foggia Foggia Italy Department of Research and Humanistic Innovation University of Bari Bari Italy

In 2017, for the first time in Italy, the students enrolled in Dental Schools performed a Progress test. The results allow us to analyse the evolution of students’ abilities during the course years. Furthermore, we can evaluate the coherence of the learning pattern with the core curriculum. We analyse the results of the Progress Test with a mixed-effects growth-curve binomial model for the number of correct answers, using fixed effects for the topics and random effects for the universities. The learning trajectories for each topic are modelled via polynomials of time. Using the Empirical Bayes predictions of random effects, we obtain the trajectories for the Italian universities, which show substantial heterogeneity in the starting levels and growth rates. The results give insights into inequalities in the educational process across Italian universities to be exploited for planning interventions. © The Author(s), under exclusive licence to Springer Nature B.V. 2024.

关键词： Binomial growth curves Empirical Bayes prediction Mixed effect model Random effects University tests

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A Generalized Framework for Brain Tumor and Pneumonia Detection Using Streamlite Application 4

A Generalized Framework for Brain Tumor and Pneumonia Detect...

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4th International Conference for Emerging Technology, INCET 2023

作者： Kishore, S. Nair, Rekha R Mehra, Vishal Babu, Tina Dayananda Sagar University Department of Computer Applications School of Engineering Bengaluru India Dayananda Sagar University Department of Computer Science and Engineering School of Engineering Bengaluru India

ISBN: (纸本)9798350335750

One of the dangerous disease pneumonia, which can affect one or both lungs, is frequently brought on by viruses, fungi or bacteria. Radiotherapists with advanced training are required to evaluate chest X-rays used to diagnose pneumonia. Brain tumours are another fatal disease that can have a profound impact on a patient's quality of life and alter everything for them and their loved ones. Therefore, creating an automatic system for diagnosing pneumonia would help treat the disease quickly, especially in remote areas. The work proposes a general framework for MRI and CT images that can automatically detect diseases such as brain tumours and pneumonia with single framework using VGG16. The proposed work focusing on developing streamlit web application for deployment. The proposed work gained an accuracy of 97.87% compared to Inception, Xception and ResNet for brain tumor detection and 94.3% for Pnemonia detection model. The training and testing data are compared using a CNN-based classifier to determine the optimal outcome. © 2023 IEEE.

关键词： Tumors

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Multilingual and Cross Lingual Audio Emotion Analysis Using RNN 1

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International Conference on Advances in IoT, Security with AI, ICAISA 2023

作者： Bhattacharya, Sudipta Mishra, Brojo Kishore Borah, Samarjeet Department of Computer Science and Engineering GIET University Gunupur India Department of Computer Applications Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim Gangtok India

ISBN: (数字)9789819950881

ISBN: (纸本)9789819950874

Speech and language are necessary for a person’s development of their emotional and social skills. Reading, writing, and verbal apprehensions are vital components of the overall learning process, and hence, instruction or some linguistic representation is necessary for academic achievement. This study has been highlighted using several audio files based on various linguistic methodologies, and it has been compared using multilingual and cross-lingual methods. For our experiments with the English, German, Italian, and Canadian-English emotion datasets, we have decided to use CNN and LSTM as our models of choice. We obtained the most outstanding results in Canadian English during the trial with a single language. Here, we obtained CNN at 99.12% and LSTM at 98.96%. During the experiment with several languages, only English test data was found to be secure at 98.19% in CNN and 93.41% in LSTM. Lastly, in the cross-lingual domain of Canadian English, namely 93.25%, we found 98.19% using the CNN and LSTM models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Emotion Recognition

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DCNN and LSTM based Plant Leaf Disease Detection Model For Edge IoT Device 5

DCNN and LSTM based Plant Leaf Disease Detection Model For E...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Selvam, L. Mary, S. Roseline Manimuthu, A. Pandian, M. Faculty of Science and Humanities Srmist Department of Computer Applications chengalpattu Kattankulathur India Loyola College Department of Mathematics Chennai India Loyola College Department of Data Science Chennai India

ISBN: (纸本)9798350383522

Edge Computing is an incredibly practical and affordable alternative IoT device for cloud computing that uses a revolutionary methodology. Not only does edge processing reduce the cost of systems administration frameworks, but it also improves edge-cloud latency, which is essential for all critical applications. Plant diseases have a catastrophic impact on food production security and can lead to a sharp decline in the quantity and quality of agricultural products. In severe cases, plant diseases may continue to be the cause of partial grain failure. As a result, an automated method for diagnosing and detecting plant diseases is vitally needed for all agricultural data. In this paper, we have proposed a plant disease detection method based on transfer learning and tested the model's performance on IoT edge device. Long-Short Term Memory (LSTM) is used in the proposed system to classify plant leaf diseases based on data extracted by CNN. An internal memory within the LSTM network allows it to learn from critical experiences with long-term conditions. Therefore, the combination of CNN and LSTM layout features significantly enhances classification. It is suggested to use a Deep Convolutional Neural Network based on a pre-trained MobileNetV1 feature extractor and an LSTM classifier. The proposed model's performance is contrasted with that of other pre-trained models, including NASNetMobile and MobileNetV2. The proposed model outperformed other models, achieving a validation accuracy of 0.9892. © 2024 IEEE.

关键词： Edge Computing MobileNet Plant Disease Detection Transfer Learning

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