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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Krishnan, Sundara Rajulu Navaneetha Sangeetha, M. Srinivasulu, Senduru Surendran, R. Dhanalakshmi Srinivasan University Trichy Department of Computer Science and Engineering Samayapuram Campus Tiruchirappalli India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Computer Science and Engineering Avadi Chennai India Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha School of Engineering Chennai India

ISBN: (纸本)9798331534950

In recent days, Emotion recognition has garnered a lot of attention because of its important applications in human-computer interaction. Human emotion is expressed through a variety of verbal and nonverbal languages, including text, audio, and visual. Current multimodal emotion analysis techniques typically perform poorly because they don't fully utilize the link between various modalities. An advanced transformer model with fine-grained correlation fusion (AT-FGCF) is presented in this research to identify emotion in text, audio, and visual modalities. To enhance the representation of the input audio and video, dense hierarchical feature maps are captured using the Dense Swin Transformer (DSwinT) model. Also, an Emotion- Aware Cognitive BERT (EAC-BERT) is introduced to enhance text representation. Lastly, a fine-grained correlation fusion is carried out to take advantage of correlations between fine-grained features from multimodal data. Our experimental findings show that the suggested method is successful in reaching radical outcomes across two benchmark datasets. That is the datasets IEMOCAP, and MELD all have average accuracy improvements of about 88.35%, and 84.80% respectively. © 2024 IEEE.

关键词： Emotion Recognition

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A Clustering Approach on Unveiling Global Development Disparities 5

A Clustering Approach on Unveiling Global Development Dispar...

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5th International Conference on Circuits, Control, Communication and computing, I4C 2024

作者： Ankireddypalli, Okesh Arella, Mouhitha Gujjula, Saranya Jayan, Sarada Amrita School of Computing Department of Computer Science & Engineering Amrita Vishwa Vidyapeetham Bengaluru India Amrita School of Engineering Department of Mathematics Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331528539

This paper seeks to go further and use clustering, hypothesis testing as well as attribute independence analysis to delve deeper into the world development indicators such as birth rate, infant mortality, business taxes rates etc., in a bid to pinpoint key relationships in the underlying socio-economic environment of the world today. Regional classifications, based on socio- economic development indicators, show that there are groupings of countries with similar development characteristics and this makes it easier to review, and analyze regional disparities. Hypothesis testing allows us to determine if there is indeed a correlation, such as between the business tax rates and Gross domestic product [GDP] growth or spending on health care and life expectancy. Also, the interdependence and the extent of the impact of these factors may be determined by identifying how precisely each component correlates with the others. And comparative analysis of these indicators were performed using the clustering techniques such as K means, hierarchical clustering, Density-Based Spatial Clustering of Applications with Noise [DBSCAN] and Spectral. The silhouette score for the clusters using K means and Hierarchical clustering shows up better analysis of the development status of the countries based on the world development features. © 2024 IEEE.

关键词： Inflation

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NeuroDerm: Pioneering Dermatological Diagnostics with Advanced Neural Networks 7

NeuroDerm: Pioneering Dermatological Diagnostics with Advanc...

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Anjali, T. Abhishek, S. Remya, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering India

ISBN: (纸本)9798350341485

Accurate skin tumor identification and categorization are crucial for quick diagnosis and treatment in dermatological care. In our research, we developed a reliable model that successfully categorizes a wide range of skin tumors, obtaining astounding accuracy rates of 91% with DenseNet and 97% with EfficientNet. This feat highlights the advanced architectures' capacity for synergy. Our dataset includes an extensive collection of skin tumor images that includes ten different categories, providing a comprehensive view of all types of skin tumors. These exceptional accuracy rates were attained via meticulous model training and validation, demonstrating the effectiveness of our methods. Our study makes a substantial contribution to dermatological diagnostics using computers. Both DenseNet and EfficientNet recognize complex characteristics in skin tumor images, allowing accurate classification. High accuracy is acheived by both models, which also make the prediction more versatile. Our study's consequences go beyond the confines of academia. Dermatologists and other medical practitioners may use our method as a powerful diagnostic tool for correctly describing skin growths, eventually improving patient treatment and results. © 2023 IEEE.

关键词： Patient treatment

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Real-Time Classification of Defects During Automated Valve Testing and Inspection 5th

Real-Time Classification of Defects During Automated Valve T...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Singh, Shweta Agarwal, Trapty Haripriya Hemlata School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India

ISBN: (纸本)9789819780426

This paper offers a singular method for actual-time type of defects at some point of computerized valve testing and inspection. Mainly, an image processing gadget is developed to discover and classify various defects which includes cracks, dents, foreign items, and different floor irregularities. The machine includes a high-decision digital camera, LED lighting fixtures, processing unit, and defect reputation algorithm. The image processing system captures the picture and methods it to pick out the illness regions. The gadget then extracts the features from every of the detected regions and classifies them the usage of a guide Vector device. The model is demonstrated with manually received pics, and the outcomes show true overall performance in terms of accuracy and speed. Similarly, the system can alert the consumer whilst a potential disorder is identified and the defects can then be in addition inspected and repaired. This approach has the ability to lessen inspection time, enhance manufacturing efficiency, and reduce charges. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lighting fixtures

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Prediction of Fetal Brain Anomalies Using Deep Learning Models 5

Prediction of Fetal Brain Anomalies Using Deep Learning Mode...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Subburaj, Venkateshwari Pandiyarajan, Pandiselvam School of Computing Department of Computer Application Kalasalingam Academy of Research and Education TamilNadu Krishnankoil India School of Computing Department of Computer Science and Engineering Kalasalingam Academy of Research and Education TamilNadu Krishnankoil India

ISBN: (纸本)9798331518097

Fetal brain anomaly prediction is important for fetal medicine, as well as for prenatal health care. Fetal anomalies are classified into two types, anomalies in the fetus' body parts including heart, lung, and kidney anomalies in facial features. Thus, to predict the anomalies in the fetal brain, various deep-learning models are used in this paper. The LeNet, AlexNet, ResNet, VGGNet, GoogLeNet and ZFNet were used. From the results obtained, the ResNet method achieved 99% accuracy with the given 3D ultrasound images of the fetal brain compared with other models and benchmark algorithms like SVM, Logistic regression, and KNN. The results of this paper are useful to identify fetal anomalies. Hence, this approach will be useful for improving outcomes for individuals and society as much as possible. © 2024 IEEE.

关键词： Prediction models

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Improved Dynamic Johnson Sequencing Algorithm(DJS)in Cloud computing Environment for Efficient Resource Scheduling for Distributed Overloading

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Journal of Systems science and Systems engineering 2024年第4期33卷 391-424页

作者： Anurag Sinha Pallab Banerjee Sharmistha Roy Nitasha Rathore Narendra Pratap Singh Mueen Uddin Maha Abdelhaq Raed Alsaqour Department of Computer Science Engineering ICFAI Tech SchoolICFAI UniversityJharkhandRanchi834004India Department of Computer Science Engineering Amity UniversityJharkhandRanchi835303India Department of Computing and Information Technology Usha Martin UniversityRanchi835103India Department of Information Technology Bharti Vidyapeeth’s College of EngineeringPaschim ViharNew DelhiIndia Department of CSE Delhi UniversityNew DelhiIndia College of Computing and Information Technology University of Doha for Science and TechnologyDoha24449Qatar Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdurrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology College of Computing and InformaticsSaudi Electronic University93499RiyadhSaudi Arabia

This study introduces an innovative approach to optimize cloud computing job distribution using the Improved Dynamic Johnson Sequencing Algorithm(DJS).Emphasizing on-demand resource sharing,typical to Cloud Service Providers(CSPs),the research focuses on minimizing job completion delays through efficient task *** Johnson’s rule from operations research,the study addresses the challenge of resource availability post-task *** advocates for queuing models with multiple servers and finite capacity to improve job scheduling models,subsequently reducing wait times and queue *** Dynamic Johnson Sequencing Algorithm and the M/M/c/K queuing model are applied to optimize task sequences,showcasing their efficacy through comparative *** research evaluates the impact of makespan calculation on data file transfer times and assesses vital performance indicators,ultimately positioning the proposed technique as superior to existing approaches,offering a robust framework for enhanced task scheduling and resource allocation in cloud computing.

关键词： First come first served(FCFS)scheduling round robin task scheduling queuing model Johnson scheduling algorithm

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Real Time Object Detection using Markov Random Fields: Video Anomaly Detection 5

Real Time Object Detection using Markov Random Fields: Video...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Chennupati, Anjali Prahas, Bhamidipati Ghali, Bharadwaj Aaditya Murali, K. Department of Computer Science and Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Mathematics Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350361155

This research focuses on the field of video anomaly detection, using the combined strength of the Markov Random Fields (MRFs) and autoencoder systems. The image-segmentation model proposed is based on the use of MRF, which is being used its spatial-temporal abilities to, for one, improve its ability to differentiate abnormalities in video content. The use of MRFs and autoencoders for the integration of the MRFs and autoencoders forms a comprehensive representation of the video data, improving the detection of abnormal activities. A wide range of testing is performed on videos randomly selected from the AVENUE dataset in order to emphasize the effectiveness of the proposed method, which outperforms other approaches. In particular, the approach significantly reduces the computation time of the anomaly detection by 60% when compared to the conventional frame extraction method. This efficiency improvement is an indication of the feasibility of this model in detecting anomalies in practical surveillance applications and has the required attributes that can be used to detect an anomaly in the surveillance system within a short time. The results reveal the potential benefits of the proposed framework for the improvement of the anomaly detection accuracy and operational effectiveness, adding to its practical viability being deployed in surveillance settings. © 2024 IEEE.

关键词： Anomaly detection

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Exploring the Dynamics of Exchange Rates: An Empirical Analysis and Predictive Modelling 2

Exploring the Dynamics of Exchange Rates: An Empirical Analy...

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2nd International Conference on electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Anand, R.N. Pati, Peeta Basa Deepa, K. Konidhala, Jothibabu K Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering India Amrita School of Engineering Department of Electrical & Electronics Engineering Bangalore India University of Technology and Applied Sciences-Al Musannah Department of Engineering Oman

ISBN: (纸本)9798350397635

In India, globalization and reforms in the financial sector have brought about a major overhaul of the financial landscape. The connection between financial markets and the tangible economy is now highly valued in the current scenario. Exchange rate affects a country's economy in several ways. It has a huge effect on international trade, economic growth, capital flows, interest rates and also inflation. Here, the aim is to understand the underlying factors that affects the exchange rate of US dollar with respect to Indian rupee. To accomplish this, a Vector Auto Regression (VAR) model, as well as other machine learning techniques such as multi-variable linear regression, polynomial regression, and regression tree, are employed to forecast exchange rates. Our findings indicate that Trade Balance is a significant factor which cause fluctuations in exchange rate of USD and regression tree model performs better from the side of all machine learning model applied for predicting the same. © 2023 IEEE.

关键词： Multiple linear regression

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TrueAdaptTM- AI Based Maskless Patterning to Compensate for Die-Shift in Fan-Out Wafer Level Packaging 73

TrueAdaptTM- AI Based Maskless Patterning to Compensate for ...

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73rd IEEE Electronic Components and Technology Conference, ECTC 2023

作者： Sabbir, Golam Sahoo, Krutikesh Sun, Henry Iver, Subramanian Ouyang, Guangqi Wu, Yilin Ucla Department of Materials Science and Engineering United States Ucla Department of Electrical and Computing Engineering United States Ucla Department of Computer Science United States

ISBN: (纸本)9798350334982

TrueAdapt™ offers an attractive solution to fan-out-wafer-level-packaging (FOWLP) and fan-out-panel-level-packaging, which offers significant cost savings as compared to similar bridge connection technology or interposers. Due to die-placement error and shift during molding, die assemblies on FOWLP can have significant die-shift. Design rules must ensure the accommodation of die-shift and as a result fine-pitch patterning is limited. TrueAdapt™ aims to solve that by utilizing in-line characterization of the die shift, generation of a new layout using the measured offsets, and patterning with direct-write lithography for high throughput wafer-level and panel-level fabrication at fine-pitches. In this work we present the fabrication of an assembly of dies interconnected in a daisy chain network with 10 µm pitch wiring on the FlexTrate™, a bio-compatible polydimethylsiloxane (PDMS) based FOWLP architecture. We measure die shift using optical metrology techniques to generate a stitched image of the assembly. The image is then processed using artificial intelligence (AI) computer vision to identify critical features on the dies (which are used to generate die-offsets for each die). Furthermore, we generate a layout based on measured die-shift that adaptively routes the wiring in between dies. Via and metal layers are subsequently patterned using direct-write lithography using a 405 nm lase. Direct-write lithography enables fine-pitch patterning at the wafer/panel level without the need for mask fabrication, promising significant cost savings. We further demonstrate a novel exposure technique based on focal extension that can be utilized to pattern over 100 µm of topography, making this a truly adaptive patterning process. © 2023 IEEE.

关键词： Dies

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Optimizing Energy Consumption in Smart Grids Using Demand Response Techniques

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Distributed Generation and Alternative Energy Journal 2024年第1期39卷 111-136页

作者： SwornaKokila, M.L. Venkatarathinam, R. Rose Bindu, Joseph P. Manivasagam, M.A. Kishore, Kakarla Hari Department of Computing Technologies School of Computing College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur TN Chennai India Department of Information Technology Sri BalajiChockalingam Engineering College A.C.S. Nagar Irumbedu Thiruvannamalai District Tamil Nadu Arni India Department of Mathematics Dayananda Sagar College of Engineering Bangalore India Computer Science and Engineering Siddartha Institute of Science and Technology Andhra Pradesh India Department of Electronics and Communication Engineering KoneruLakshmaiah Education Foundation Guntur Andhra Pradesh Vaddeswaram India

Smart grids have developed as a potentially game-changing strategy for controlling the demand and supply of energy. Unfortunately, peak demand is a significant source of grid instability and rising energy prices, making it one of the most critical difficulties in smart grids. During times of high energy demand on the grid, demand response (DR) strategies incentivize consumers to change how they use energy. This study’s overarching goal is to learn how DR methods may be used to help smart grids make better use of their energy resources. The primary research is to develop a smart DR system that can predict times of high energy demand and proactively alter usage to reduce such periods. Machine learning strategies are utilized in the proposed system to estimate peak demand via past data, weather predictions, and other variables. The system will then alter energy use based on real-time data from smart meters along with other sensing devices to meet the projected demand. The simulation model will include several scenarios for testing the DR system’s flexibility, including a range of weather conditions, load profiles, and grid topologies. Several indicators, including peak demand reduction (80.04%), energy savings (38.09%), environmental consequences, and reaction time ( © 2024 Taylor and Francis Inc.. All rights reserved.

关键词： Smart power grids

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