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Ensemble-based advancements in maternal fetal plane and brain plane classification for enhanced prenatal diagnosis

International Journal of Information Technology (Singapore) 2024年 1-17页

作者： R, Annamalai Sindhu, Kolla Gnapika Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Tamil Nadu Chennai India

In the realm of maternal healthcare, accurate fetal plane detection is of paramount importance. This paper introduces a novel approach that leverages ensemble techniques to enhance the precision and dependability of fetal plane classification. We address two pivotal classification tasks: first, the categorization of fetal planes into six distinct classes, encompassing critical regions such as the fetal abdomen, brain, femur, thorax, maternal cervix, and other areas. The second task focuses on the nuanced classification of fetal brain planes, further refined into trans-thalamic, trans-cerebellum, and trans-ventricular subtypes. To address these challenges, we harnessed the power of six distinct pre-trained models, rigorously training each for 50 epochs. Our results underscore the consistent superiority of InceptionResNetV2, DenseNet121, and Xception. Through a pioneering ensemble approach, we synergistically harnessed their capabilities, leading to enhanced classification performance. This research promises to augment the precision of maternal-fetal plane classification, with the potential to revolutionize clinical practice and healthcare research. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： DenseNet121 Ensemble learning Fetal planes Histogram equalization InseptionResNetV2 Median filter Ultrasonography

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Enhancing Healthcare AI Sensitivity through Affective computing: Analyzing Human Emotions 1

Enhancing Healthcare AI Sensitivity through Affective Comput...

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1st IEEE International Conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Singh, Rahul Kumar, Avadhesh Sagar, Shraddha Sharma, Vishnu Galgotias University School of Computing Science and Engineering Greater Noida India ITS Engineering College Department of Computer Science Engineering Greater Noida India

ISBN: (纸本)9798350356816

Affective computing (AC), also known as emotion AI, is a concept that applies modern technology to help systems to recognize and process human emotions and their feelings. Several uses of affective computing have been demonstrated, including marketing, healthcare, driving assistance, and education. Linear Support vector machines (SVM), k-nearest neighbor (k-NN), Hidden Markov models (HMMs), Gaussian mixture models (GMM), discriminate classifiers (LDC), decision tree algorithms and artificial neural networks (ANN), are the classifiers that are most frequently used in research on identifying and categorizing human emotions. Selecting the right classifier can significantly increase the system's overall efficiency, as numerous studies have conclusively demonstrated. Through use of cameras, microphones, skin sensors, or other devices, affective computing gathers signals from human users, gathering data on their facial expressions, voice tones, gestures, and other characteristics that can hint at their emotional states. The system deduces the user's emotional state from those data points. This study emphasizes on the fundamentals of computing emotions. © 2024 IEEE.

关键词： Support vector machines

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From FOMO to a Legitimate Wellbeing Concern: The Evolution and Impact of Disconnectivity Anxiety According to the Current Research 29

From FOMO to a Legitimate Wellbeing Concern: The Evolution a...

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29th Annual Americas Conference on Information Systems: Diving into Uncharted Waters, AMCIS 2023

作者： Nawaz, Saqib Department of Computing Technologies School of Science Computing and Engineering Technologies Swinburne University of Technology Australia

ISBN: (纸本)9781713893592

The increasing prevalence of smartphones and social media has sparked growing concerns about anxiety related to digital disconnection, particularly in the context of problematic smartphone use and dependence (PSUD). Fear of missing out (FOMO) is a widely recognised form of anxiety associated with frequent smartphone use, especially on social media apps, and it has been primarily observed among younger adults. However, beyond FOMO, other technological concerns, such as nomophobia (no-mobile-phone phobia) and technoference (the interference of technology in interpersonal relationships), have also become more prevalent, highlighting the broader impact of smartphone use on individuals' mental wellbeing. My ongoing PhD research into the identification and management of PSUD aimed to delve deeper into understanding the complexities of PSUD and its effects on individuals' lives. FOMO is one of the critical aspects examined within the broader framework of PSUD, as it can have significant implications for mental health and wellbeing. It also explored how FOMO may arise both from PSUD and effectual smartphone use, where individuals may feel concerned about missing important meetings, notifications, calls, or other responsibilities related to their social and professional roles. The multifaceted cause of FOMO was one of the focal points of my investigation, encompassing factors such as the need for connectedness for various reasons and constant exposure to media. I have investigated how FOMO can lead to feelings of inadequacy, loneliness, and anxiety, impacting individuals emotionally and socially. This could include further examining how FOMO may negatively impact self-esteem, increase stress, and contribute to depression and its influence on developing unhealthy coping mechanisms and impulsive behaviours. Addressing FOMO and digital disconnection anxiety within the context of PSUD requires a holistic approach that involves self-care, mindfulness practices, and boundary setti

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Customer Churn Prediction in Subscription-Based Services 5th

Customer Churn Prediction in Subscription-Based Services

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5th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Kolli, Mahi Varadharajan, Nayantara Ajith, Keerthana Dinesh Kumar, K. Department of Computer Science and Engineering Amrita School of Computing AmritaVishwaVidyapeetham Bangalore India

ISBN: (纸本)9789819788644

The rapid growth of subscription-based services in the telecom industry has led to a larger subscriber base for service vendors. However, customer churn, or the loss of clients, has become a critical issue for telecom companies. This paper addresses the problem of customer churn or attrition in the telecommunication industry and performs a churn prediction model that implements Machine Learning techniques on a big data platform. The study employs key algorithms, including Logistic Regression, Decision Tree, KNN Classifier, Random Forest and Support Vector Machine (RBF), to identify potential defectors and enable companies to implement retention techniques. This paper focuses on voluntary churn and implements engineering features and selection methods to improve predictive accuracy. As markets expand and customer acquisition costs increase, effective customer retention becomes paramount, prompting service companies to invest in prediction analytics for proactive churn management. The study leverages prior research and experiments with multiple machine-learning algorithms to build a predictive model of customer churn. The utilized model uses big data and innovative feature engineering and selection methods to predict customers who are most likely to churn. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Telecommunication industry

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From Posts to Personas: Classifying Users in the Social Media Landscape 5

From Posts to Personas: Classifying Users in the Social Medi...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Nair, Lekshmi S. Cheriyan, Jo Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Computer Science and Engineering Saintgits College of Engineering Kerala Kottayam India

Social media is a digital environment where users openly share their opinions and engage in debates and discussions on various topics. Social media has amassed an enormous quantity of accessible data due to its constantly expanding and highly active user base. This data is a valuable resource for researching a variety of topics. An example of such a research problem is identifying user similarities by analyzing their data. This article explores combining comments, textual posts, and likes for comments to create keywords or tags and group the users. These tags are extracted from user data and utilized in constructing a tag network. The tag network facilitates the formation of communities consisting of users with similar interests. User grouping is achieved based on the tags extracted from the posts. The proposed methods employ TF-IDF (term frequency-inverse document frequency) and TextRank algorithms to extract the tags. Kernel diffusion determines the similarities between tags in the tag network. Finally, an aggregation-hierarchical clustering algorithm is employed to group social media users based on these tags. © 2024 The Authors. Published by Elsevier B.V.

关键词： Complex networks

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Survey on Video Security:Examining Threats,Challenges,and Future Trends

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Computers, Materials & Continua 2024年第9期80卷 3591-3635页

作者： Ali Asghar Amna Shifa Mamoona Naveed Asghar Department of Artificial Intelligence Faculty of ComputingThe Islamia University of BahawalpurBahawalpur63100Pakistan School of Computer Science College of Science and EngineeringUniversity of GalwayGalwayH91 TK33Ireland

Videos represent the most prevailing form of digital media for communication,information dissemination,and ***,theirwidespread use has increased the risks of unauthorised access andmanipulation,posing significant *** response,various protection approaches have been developed to secure,authenticate,and ensure the integrity of digital *** study provides a comprehensive survey of the challenges associated with maintaining the confidentiality,integrity,and availability of video content,and examining how it can be *** then investigates current developments in the field of video security by exploring two critical research ***,it examine the techniques used by adversaries to compromise video data and evaluate their *** these attack methodologies is crucial for developing effective defense ***,it explores the various security approaches that can be employed to protect video data,enhancing its transparency,integrity,and *** compares the effectiveness of these approaches across different use cases,including surveillance,video on demand(VoD),and medical videos related to disease ***,it identifies potential research opportunities to enhance video data protection in response to the evolving threat *** this investigation,this study aims to contribute to the ongoing efforts in securing video data,providing insights that are vital for researchers,practitioners,and policymakers dedicated to enhancing the safety and reliability of video content in our digital world.

关键词： Attacks threats security services video manipulation video security

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Deep Fake Detection Based on Hybrid Approach Involving Residual Network Deep Learning Architecture

Deep Fake Detection Based on Hybrid Approach Involving Resid...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Shibu, N.V. Muthunagai, R. Prabu, S. Srm Institute of Science and Technology Department of Computing Technologies Chengalpattu India Manakula Vinayagar Institute of Technology Department of Electrical & Electronics Engineering Puducherry India School of Computing Srm Institute of Science and Technologies Department of Computing Technologies Chennai India

ISBN: (纸本)9798331510022

This Deep fake Technology aims at generating synthetic video content or morphed video content which is very realistic to the naked eye. It involves the use of deep learning algorithms in the domain of video processing. In most cases, such artificially generated video contents are very difficult to get differentiated from naturally captured video signals. The existing methodologies of detection involved image tamper detection and digital forensics algorithms. These algorithms are ineffective in detecting videos tampered using deep fake technology. The proposed technique involves training and building of deep learning models that works like a black box to detect the artefacts crucial in the detection of video tampering. This paper presents an overview of the technology advancements in the field of deep fake video detection techniques over recent years. This paper also performs a comparative analysis on the performance of hybrid algorithms involving residual network deep learning architecture in the detection of video signals morphed using deep fake technology. The training datasets are obtained from Face forensics to build the model. The algorithm is augmented with image quality assessment, temporal consistency analysis and facial landmarks analysis to improve the detection algorithm. The performance metrics involve the generation of confusion matrix and generation of accuracy measures. The results provide a promising direction towards the construction of better detection models to safeguard against current and future threats. © 2024 IEEE.

关键词： Contrastive Learning

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A Novel Attention-Based Method for Integrating Facial and Speech Features in Emotion Recognition 2

A Novel Attention-Based Method for Integrating Facial and Sp...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Singh, Rahul Sagar, Shraddha Kumar, Avadhesh Sharma, Vishnu Galgotias University School of Computing Science & Engineering Greater Noida India Its Engineering College Department of Computer Science Engineering Greater Noida India

ISBN: (纸本)9798350388916

Our research introduces a novel approach for recognizing multiple emotions by leveraging perspective-based methods. This technique integrates facial features and speech characteristics obtained from different encoders to pinpoint the most significant attributes. The distinct characteristics of speech and facial expressions, along with effective size management, prove to be highly beneficial. By concentrating on specific components, the system enhances overall accuracy. Images of facial features are captured from both low and high angles to ensure a comprehensive representation. These techniques are merged through a fusion mesh, producing several feature vectors that are subsequently utilized in the classification stage for sensitivity analysis. When tested with both data sets, this algorithm surpassed existing models, achieving a weighted accuracy (WA) of 90.3%. © 2024 IEEE.

关键词： Attention-Based Method Emotion Classification Facial and Speech Features Fusion Network Multimodal Emotion Recognition Weighted Accuracy

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QViLa: Quantum Infused Vision-Language Model for Enhanced Multimodal Understanding

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SN Computer science 2024年第8期5卷 1023页

作者： Mukesh, K. Jayaprakash, S.L. Kumar, R. Prasanna Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Tamilnadu Chennai 601103 India

Vision-language models have emerged as transformative tools, revolutionizing the integration of visual and textual information, forging pathways for nuanced interpretations across various applications. The evolution of these models underscores the challenge of achieving seamless modality fusion, particularly in aligning raw pixel values of images with high-level semantics of text, a bottleneck that often hinders optimal cross-modal representations. Addressing this, our research introduces a quantum-enhanced multimodal framework. The main objective of our proposed work is the integration of classical vision-language transformers with quantum-augmented layer, aimed at enhancing the fusion of extracted feature embeddings, thereby bridging the modality gap. The quantum computing techniques, offers an innovative approach to information processing, which paves the way for richer and more intricate visual-textual representations. Furthermore, the shared self-attention mechanism accentuates the model’s ability to detect complex modality interactions. The quantum-enhanced framework is empirically evaluated on the VQA v2 dataset. This evaluation not only considers accuracy across diverse question categories but also the model’s computational efficiency, emphasizing the pivotal contributions of quantum computations in achieving heightened accuracy levels. Further exploration into the influence of different quantum feature maps aided in identifying the most optimal model variant. Our findings highlight the quantum layer’s pivotal role in improving the efficacy of classical vision language models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Multimodal learning Quantum computing Vision language model Visual reasoning

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Holt-Winters Algorithm to Predict the Stock Value Using Recurrent Neural Network

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Intelligent Automation & Soft computing 2023年第1期35卷 1151-1163页

作者： M.Mohan P.C.Kishore Raja P.Velmurugan A.Kulothungan Department of Computer Science&Engineering SRM UniversitySonepatHaryanaIndia Department of Electronics&Communication Engineering SRM UniversitySonepatHaryanaIndia Department of Computing Technologies SRM Institute of Science and TechnologyChennaiIndia Department of Computer Science&Engineering SRM Institute of Science and TechnologyModinagarGhaziabadIndia

Prediction of stock market value is highly risky because it is based on the concept of Time Series forecasting system that can be used for investments in a safe environment with minimized chances of *** proposed model uses a real time dataset offifteen Stocks as input into the system and based on the data,predicts or forecast future stock prices of different companies belonging to different *** dataset includes approximatelyfifteen companies from different sectors and forecasts their results based on which the user can decide whether to invest in the particular company or not;the forecasting is done for the next *** model uses 3 main concepts for forecasting *** one is for stocks that show periodic change throughout the season,the‘Holt-Winters Triple Exponential Smoothing’.3 basic things taken into conclusion by this algorithm are Base Level,Trend Level and Seasoning *** value of all these are calculated by us and then decomposition of all these factors is done by the Holt-Winters *** second concept is‘Recurrent Neural Network’.The specific model of recurrent neural network that is being used is Long-Short Term Memory and it’s the same as the Normal Neural Network,the only difference is that each intermediate cell is a memory cell and retails its value till the next feedback *** third concept is Recommendation System whichfilters and predict the rating based on the different factors.

关键词： Stock market stock market prediction time series forecasting efficient market hypothesis National stock exchange India smoothing observation trend level seasonal factor

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