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Transformer based multimodal similarity search method for E-Commerce platforms

Transformer based multimodal similarity search method for E-...

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2023 IEEE Guwahati Subsection Conference, GCON 2023

作者： Sahoo, Chandan Charchit Tomar, Deepak Singh Bharti, Jyoti Maulana Azad National Institute of Technology Dept. of Computer Science and Engineering Bhopal462003 India

ISBN: (纸本)9798350337785

Multimodal similarity search has been attracting much attention from scholars in recent times. The intensification of research into Multimodal search lies in the motive to understand the context of the multiple modes associated with the data and the interdependency of these modes. Multimodal data can be in the form of audio, video, images, texts, and any combination of them. For relevant extraction of features from them can take too much time and thus it may cause the whole process of learning the features and searching for them time-consuming. In this work, a deep neural network uses Convolutional neural network and Bidirectional Encoder Representations from Transformers to learn features from text and image data. A K nearest neighbor along with KD Tree has been used to search for similar image-text pairs. Evaluations based on several image-text multimodal retrieval procedures show the effectiveness of the proposed multimodal search method and its higher speed performance compared to existing algorithms. © 2023 IEEE.

关键词： Convolutional neural networks

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College Bus Number Plate Registration Detection Using YOLO-V8

College Bus Number Plate Registration Detection Using YOLO-V...

引用

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

作者： Naureen, Ayesha Krishna, K.Chaithanya Teja, N.Sai Mahesh, L. Vamshi, K. B v Raju Institute of Technology Dept. of Computer Science and Engineering Telangana Narsapur India

ISBN: (纸本)9798350313413

There is a need for automation of vehicle entering system and to reduce the time of verification. We propose a solution for this problem considering college buses o BVRIT as organization. College bus number plate Registration Detection is crucial part of smart BVRIT planning and BVRIT transport management. This paper presents YOLO V8 algorithm for Detecting college bus using number plate registration detection. YOLO - You Only Look Once. This YOLO-V8 algorithm is state-of-the-art (SOA) model or algorithm. It's real-time object detection algorithm that can detect objects in any image, that has many classes in it with high accuracy and speed. A dataset of College bus images is collected manually and finetuned using transfer learning. Model is trained on powerful GPU (Graphics Processing Unit), significantly reducing training time and taking less time for Character recognition. The model's accuracy is computed by average, precision, recall, and F1 score as well curves. Our results show that Yolo V8, model can accurately detect the bus with high accuracy and speed in any weather condition. The text from detected number plate is recognized using Easy-OCR. This number plate text is used to infer whether bus is of COLLEGE BUS or NON-COLLEGE BUS. Model's overall performance is remarkable. While maintaining recall of 95 percent, which improves its capacity to collect a substantial number of pertinent things, it attained a precision of 99.2 percent, suggesting a high accuracy in properly identifying objects. The model's ability to detect items with a high degree of confidence is demonstrated by its mAP50 score of 99 Percent. This is one of the optimal solutions to minimize human work and time, making automation. This research has been done with the purpose to find the registered college bus is supposed to enter the college campus and Non-college bus is not supposed to enter the campus. © 2023 IEEE.

关键词： Bounding box Character Recognition Easy-OCR License plate detection mAP(mean Average precision) Object detection Precision Recall YOLO V8

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Does Sri Lanka Have a Good Maritime Strategy to Stop Cyber-Terrorist Attacks 14

Does Sri Lanka Have a Good Maritime Strategy to Stop Cyber-T...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Samaraweera, S.A.R.P. Siriwardana, S.M.D.N. Fernando, W.D.V. Rajakarunaratne, D.N. Sri Lanka Institute of Information Technology Dept. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350335095

Maritime cyber-terrorist attacks have become a major concern to the entire world in recent decades. Everyone should be more aware of marine strategies to prevent cyberterrorist attacks, both locally and globally. This study investigates how Sri Lanka and the Sri Lanka Navy (SLN) may successfully address maritime security concerns by utilizing current resources and developing a marine strategy to counter such maritime cyber-terrorism assaults. The literature review in this thesis primarily evaluates the level of understanding in Sri Lanka concerning when marine cyber terrorism strikes may occur. Furthermore, this thesis investigates if Sri Lanka has any maritime rules and regulations. The use of such laws and regulations will concentrate on how they can increase their expertise. © 2023 IEEE.

关键词： Cybersecurity

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Analyzing Voice Quality with Multi-Dimensional Voice Program for Disease Determination 6

Analyzing Voice Quality with Multi-Dimensional Voice Program...

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6th International Symposium on computer, Consumer and Control, IS3C 2023

作者： Liza, Rahmi Tsung, Chen-Kun National Chin-Yi University of Technology dept. Computer Science and Information Engineering Taichung Taiwan

ISBN: (纸本)9798350301953

Multi-Dimensional Voice Program (MDVP) parameters are very popular among physicians/clinicians to detect vocal pathologies and analyze various diseases of the vocal cords. In this paper, voice pathologies are automatically detected using the parameters of the MDVP. However, MDVP is commercial software, so this work is trying to build MDVP using Python to extract MDVP parameters useful for various experiments, automatic detection of sound pathologies, and automatic classification of voice recognition. This study evaluates MDVP parameters and applies the XGBoost model as a classification method to analyze and classify diseases. This work considers three sample data, polyps, nodules, and Reinke edema, popular in clinical vocal cords diseases, from Saarbruecken Voice Database (SVD) for data testing and training. Test results demonstrate the excellent ability of MDVP's parameter extraction to identify healthy voices and obtain accurate classification results to discriminate between healthy voices and pathological voices. The best overall accuracy is 98% using the XGBoost classifier. © 2023 IEEE.

关键词： Classification (of information)

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Integration of BCH Encoder in SiGe Driver System

Integration of BCH Encoder in SiGe Driver System

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2023 Signal Processing in Photonic Communications, SPPCom 2023 in Advanced Photonics Congress, Part of Advanced Photonics Congress 2023

作者： Wang, Xu Mowlavi, Siavash Svensson, Lars Larsson-Edefors, Per Dept. of Computer Science and Engineering Chalmers University of Technology GothenburgSE-412 96 Sweden

We implement a 56-Gbps BCH encoder in a 130-nm SiGe technology suitable for VCSEL drivers for short-reach optical communication. We show that encoder-driver integration can reduce total power consumption in addition t... 详细信息

ISBN: (纸本)9781957171265

关键词： Signal encoding

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Dynamic Time-Series Clustering for Improving Appliances Forecasting in Smart Homes

Dynamic Time-Series Clustering for Improving Appliances Fore...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Raja, Rameez Redhu, Surender Dinh, Nga Østfold University College Dept. of Engineering Technology Halden Norway Kongsberg Digital As Oslo Norway Østfold University College Dept. of Computer Science Halden Norway

ISBN: (纸本)9789531842976

Energy consumption forecasting for smart home appliances is a crucial area of research due to its potential to optimize energy consumption and reduce costs. However, accurate energy forecasting remains challenging due to the evolving nature of energy consumption patterns. This paper addresses these challenges by proposing Dynamic Time-Series clustering techniques to enhance accuracy in energy and load forecasting for smart home appliances. The focus of this study is on the correlation between the energy consumption of different household appliances. Energy consumption data from these appliances is recorded as time-series data, offering valuable insights for analysis. Our research focuses on clustering time-series data from multiple household appliances to enhance the performance of forecasting algorithms. Specifically, we aim to improve forecasting accuracy by using clustering information. This involves organizing time-series data from various appliances into clusters, which are then used as input for the forecasting algorithm. To achieve this, we employ the k-shape algorithm for time-series clustering on household appliances data. This algorithm effectively extracts time series shapes and computes pairwise cross-correlation for clustering purposes. Moreover, our proposed Dynamic time-series clustering approach incorporates time of the day information, enabling the identification of patterns and variations in appliance behavior across various periods. © 2024 IEEE.

关键词： Smart homes

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Self-supervised Federated Learning for Anomaly Detection

Self-supervised Federated Learning for Anomaly Detection

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Eng, Tze-Qian Pao, Hsing-Kuo Liao, Chi-Chen National Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350324457

Federated learning-based strategy can confirm data privacy, as well as other related ones like split learning and differential privacy. When enjoying secured data sharing, we also worry that the data privacy preserving may harm the model's overall performance. Given the most recent progress in machine learning and AI methodology, we incorporate self-supervised learning to plug in the federated learning framework and the integrated system can guarantee the model performance and data privacy preservation simultaneously. In the integrated framework, we have different clients to keep their own data, and the data are well separated into the attribute half and the label half, for enhanced privacy, not to mention the additional privacy-preserving skill like differential privacy. Given all the aforementioned components, we can still have the privacy-preserving components equipped model performed superior to or is compatible with the performance without the privacy-preserving components. That is, one client can perform better when adding more information from other clients, without the data sharing in between, and no clients own both the attributes and the corresponding labels. The focused topic is anomaly detection and we pay attention to the imbalanced nature of the data which shows additional challenges to the problem. Given the problem, self-supervised learning is especially useful when obtaining the label information is considered non-trivial. After all, we demonstrate the overall model effectiveness when compared to methods without any federated learning components. © 2023 IEEE.

关键词： Anomaly detection differential privacy federated learning self-supervised learning split learning

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KinderEye: Kindergarten Child Performance Monitoring System 9

KinderEye: Kindergarten Child Performance Monitoring System

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9th International Conference on computer and Communication engineering, ICCCE 2023

作者： Rathnayaka, B. Rabel, W.T.S. Namasingha, C.N.W. Hettiarachchi, H.D. Hansika, Mahaadikara M.D.J.T. Wellalage, Sasini Nuwanthika Sri Lanka Institute of Information Technology Dept. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350325218

Kindergarten is the most important beginning of a child's formal education. Since the brain is rapidly developing in early childhood, a lack of brain development at kindergarten can create a weak foundation for learning, health, and behavioral problems throughout life. Digital learning and monitoring applications for young children are widely available but often lack opportunities for physical engagement, which can negatively impact executive function development. In this paper, we introduce a system for continuously monitoring mental development in terms of working memory, decision-making ability, language skills, and likelihood of having ADHD. The system consists of three physically playable games that collect performance data from digital sensors and encourage children to be involved in physical activities while increasing enthusiasm for evaluations. Continuous performance measurement allows teachers and parents to know the development of children and guide them or direct them to have suitable medical treatments. Memory development evaluation is done from the button-pressing game which allows giving inputs according to a pattern previously indicated. For decision-making ability measurement, we are using a remote controllable robot and maze in which children navigate the robot toward the winning point of the maze. Language development is measured by giving several activities through an attractive device that measures listening, speaking, reading, and writing. Furthermore, checking ADHD is done by lively tracking child behavior from a camera while playing a game and invalid game inputs. © 2023 IEEE.

关键词： Decision making

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Gene Association Disease Prediction by GEO2R Tool

Gene Association Disease Prediction by GEO2R Tool

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

作者： Kalyan, Gunturu Udith Pranathi Reddy, Dodle Chandrakanth, Gummadivelly Pooja, Boddu Anitha, V. Vivek, D. B v Raju Institute of Technology Dept. of Computer Science Engineering Telangana Medak India

ISBN: (纸本)9798350313413

Psychiatric illnesses have a profound impact on people's mental health, their ability to function, and their general quality of life. Disorders such as bipolar disorder are very complicated and influenced by several factors, including genetic variations, behavioral characteristics, and the environment in which a person lives. In this study, the evaluation is done using the gene data of humans suffering from bipolar disorder using a tool from NCBI (National Center for Biotechnology Information) called GEO2R. This is a tool that compares two or more groups of Samples from the predefined GEO datasets and identifies the genes that are differentially expressed in different conditions. Based on the comparison the results are expressed in the form of a table of the top 250 genes which are placed sequentially according to the adjusted P-values. Further, these results are presented in the form of several graphical plots. © 2023 IEEE.

关键词： Adjusted P-value histogram Boxplot DEGs Expression density GEO2R LRT MD plot Mean-variance trend PVALUE q-q plot RNA-seq UMAP Venn diagram Volcano plot

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Diabetic Type II Prediction Using Fused Machine Learning 10

Diabetic Type II Prediction Using Fused Machine Learning

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Awasthi, Satya Prakash Singh, Sanjay Kumar, Manoj Pilania, Urmila Dubey, Shirish Mohan Poornima University Faculty of Computer Engineering Jaipur India Manav Rachna University Dept. of Computer Science & Technology Faridabad India Poornima College of Engineering India

ISBN: (纸本)9798350382471

In medicine, early illness prediction is crucial for disease prevention. Diabetes is one of the world's worst illnesses. Due to the prevalence of sugar and fat in contemporary society, diabetes risk has grown. It is essential to comprehend the symptoms of the disease in order to forecast it. The use of machine learning (ML) algorithms for disease diagnosis is increasing. This article offers a prediction model for type II diabetes using fused machine learning (FML). Three kinds of models comprise the conceptual framework: Support Vector Machine (SVM) models, decision tree models (DT), and Gaussian naive bayes models (GNB). The feature vector has extracted using DT and GNB algorithm. The feature vector has classified by using SVM algorithm. These algorithms analyze the data to evaluate the positivity or negativity of a diabetes diagnosis. © 2023 IEEE.

关键词： Diabetic DT Fused machine learning GNB SVM

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