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Detection and Classification of Bipolar Disorder Syndrome using Deep learning 5

Detection and Classification of Bipolar Disorder Syndrome us...

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5th international conference on Smart Electronics and Communication, ICOSEC 2024

作者： Charaan, R.M. Dilip Vimala Ithayan, J. Sankar, M. Chithambaramani, R. Sivaprakash, P. Marichamy, D. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala Institute of Science and Technology Department of Cse Chennai India Vel Tech Rangarajan Dr. Sagunthala Institute of Science and Technology Department of Ece Chennai India

ISBN: (纸本)9798331504403

Neural networks that are inspired by the way a human brain is structured, are known as artificial neural networks represent a unique class of machine learning algorithms. these algorithms allow artificial neural networks to acquire from data, and make resolutions based on the outcome, just like humans do. Non-linear data models, which have a whole load of variables and inputs, can predict new patterns. Artificial neural networks are already leveraged in everything from medical diagnoses to speech recognition as well as the ever-growing field of machine translation. there are multiple processes involved in the deep learning methodology that the method suggests for classifying bipolar disease. First, clinical and demographic data such as the sample's age, gender, symptom severity, and medication history would be included in the dataset of bipolar disorder patient samples and control samples. the dataset would next undergo preprocessing to remove any outliers or missing values. In the meantime, standardization and normalization would be applied to the data to ensure that each variable is on a consistent scale. data scientists may also decide to use feature selection to determine which variables are most useful for overall optimization for classification goals. In this proposed system accuracy is better as compared to the remaining conventional models. this makes it easier for us to select the elements that are most crucial to making our part a real feature. the two models ANN are the final features. Findings demonstrate that deep learning models, like as artificial neural networks (ANNs), are effective in treating neuroinformatics illnesses since datasets are readily available. When it comes to public relations, moods, and the dataset, the deep learning model predicts the correct classes with more precision. A popular application of ANN involves approximating a random function, thereby providing an inexpensive way to get to various statistics characterizing its distrib

关键词： Contrastive learning

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A machine learning Approach to Visual Insight Discovery in Multidimensional Hierarchical data 5

A Machine Learning Approach to Visual Insight Discovery in M...

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5th international conference on pattern recognition and Artificial Intelligence, PRAI 2022

作者： Wang, Guijuan Tan, Boyou Wang, Zhong Wang, Jiansong Guo, Hao Wu, Yadong Southwest University of Science and Technology Information and Technology School Computer Science and Technology School Mianyang China Southwest University of Science and Technology Computer Science and Technology School Mianyang China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

Multidimensional hierarchical (mTree) data is very common in daily life and scientific research. However, the mTree data exploration is a laborious and time-consuming process due to its structural complexity and large attributes combination space. To solve this problem, we propose a novel machine learning approach to recommend the top important subspace from the large attribute combination space and predict the suitable design choice for their visual presentation. the Subspace Importance Assessment model, the Visualization Recommendation model, and the Rule-based Chart Encoding model are proposed, and seamlessly pipelined to fulfil the task. the comparative experiment demonstrates the usability and effectiveness of our method. © 2022 IEEE.

关键词： Visualization

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A new network intrusion detection system based on auto-encoding and variance analysis 5

A new network intrusion detection system based on auto-encod...

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5th IEEE international conference on Civil Aviation Safety and Information Technology, ICCASIT 2023

作者： Chen, Dequ Zhang, Ming Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fujian Fuzhou China

ISBN: (纸本)9798350310603

In today's big data era, ensuring the network security of information technology (IT) communication facilities is one of the most challenging issues. In fact, with progress in technology, hackers have developed increasingly complex and risky network attacks, making malicious access identification a very tired task. Under various threats, the existing analysis methods encounter many challenges in detecting and mitigating these illegal accesses. this work presents a new network intrusion detection system (IDS) which combines deep learning method and mathematical strategy. Indeed, our IDS makes full use of data analysis, mathematical modeling, and deep learning to select and optimize features that are more relevant for classification. We use NSL-KDD dataset to test the performance of the IDS. the experimental results show that our proposed method has better performance compared with traditional deep learning, machine learning and recently proposed advanced methods. © 2023 IEEE.

关键词： Network security

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Intelligent vehicle classification system based on deep learning and multi-sensor fusion 5

Intelligent vehicle classification system based on deep lear...

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5th international conference on Computer Vision and data mining, ICCVDM 2024

作者： Li, Xinjin Yang, Yuanzhe Yuan, Yixiao Ma, Yu Huang, Yangchen Ni, Haowei Columbia University New YorkNY United States Diablo Valley College Pleasant HillCA United States Carnegie Mellon University PittsburghPA United States

ISBN: (纸本)9781510682863

With the rapid development of Intelligent Transportation Systems (ITS), vehicle type classification, as a key link in Automatic Toll Collection systems (ATC), is of great significance in improving traffic efficiency and reducing economic losses. this study proposes an intelligent vehicle classification system based on deep learning and multi-sensor data fusion to address the accuracy issues existing in vehicle classification methods based on optical sensors (OS) and human observers. the system significantly improves the accuracy and robustness of vehicle classification by combining deep Convolutional Neural Networks (CNN), LiDAR sensors, and machine learning algorithms. We first constructed a large-scale annotated dataset containing multiple vehicle types and complex traffic scenes to improve our model's capability to identify different vehicle characteristics. Next, CNN models based on different architectures were designed to extract global and local features of the vehicle, respectively. In addition, the LiDAR sensor was used to achieve the spatial structure architectures of the vehicle and combined with the output of the CNN model to improve classification performance under occlusion and complex scenes. © 2024 SPIE.

关键词： Convolutional neural networks

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Improved Feature Extraction for Time Series data Using Sliding Window: A Case Study of Carnatic Tala 5

Improved Feature Extraction for Time Series Data Using Slidi...

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5th international conference on Advancements in Computing, ICAC 2023

作者： Puvanendran, Rukshani Dayarathna, Oshani Erunika Kartheeswaran, thangathurai University of Vavuniya Department of ICT Sri Lanka University of Peradeniya Department of Statistics and Computer Science Sri Lanka University of Vavuniya Department of Physical Science Sri Lanka

ISBN: (纸本)9798350358131

Human action patterns are typically temporal. Since the data collected is periodic, processing time series data into a feature matrix for further learning and pattern recognition is a challenging task. Periodic data must be processed in a sequential manner. Here we have tested the case study to identify the patterns related to the Carnatic Sapta Tala (the Seven Tala System) used in Carnatic music. Accelerometer, gyroscope, gravity sensor, and rotation vector are some of the sensors embedded in smartphones and used for data collection. the minimum window size to extract distinct characteristics of the selected Sapta tala was determined to be 20 through the experiment performed. this study employed a feature extraction method with a 20-second window and 99.95% overlap to create a feature matrix for identifying Sapta tala patterns in the Carnatic music system. the findings demonstrate an improved association between these rhythmic patterns of movement and can be tracked using smartphone sensors when utilizing a method that involves 20-frame overlapping windows. Future work could incorporate more advanced machine learning algorithms and data from additional sensors to further enhance the proposed methodology to categorize tala patterns. the optimal window size of 20 frames can be further enhanced during the application of classification algorithms, subject to the relevant metric. © 2023 IEEE.

关键词： Time series

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Towards Scalable and Efficient Client Selection for Federated Object Detection 26

Towards Scalable and Efficient Client Selection for Federate...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Constantinou, George You, Suya Shahabi, Cyrus Univ Southern Calif Integrated Media Syst Ctr Los Angeles CA 90089 USA Army Res Lab Adelphi MD USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Various computer vision techniques based on deep neural networks have been proposed to detect objects accurately and fast. However, due to the privacy, security and communication bandwidth restrictions of diverse participating parties, it is sometimes prohibitive to train such models on a centralized machine. Federated learning (FL) provides a promising solution to learn a model from decentralized data. Despite the advances in FL, the diversity of client regions in which they operate and the Non-IID nature of the crowdsourced datasets reduces the accuracy of object detection models significantly. In this paper, we introduce a novel FL object detection system to efficiently train models with heterogeneous client datasets. We propose lightweight client selection methods to learn object detection models faster. Our client selection methods based on the object data distribution at clients achieves up to 74% reduction in required federated rounds compared to conventional approaches. We further extend this method by leveraging the metadata of the training images (e.g., location, direction, depth), to select clients which maximize the coverage of diverse geographical regions. We report on extensive experiments with real datasets.

关键词： Training Deep learning Privacy Computer vision Federated learning Neural networks Object detection

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Novel Hybrid Feature Selection Method and Globalization Technique for Text Classification 4th

Novel Hybrid Feature Selection Method and Globalization Tech...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Mekhzoumi, Dalila Boulkaboul, Sahar Amroun, Kamal CERIST Res Ctr Algiers Ben Aknoun Algeria Abderrahmane Mira Univ Bejaia Algeria

ISBN: (纸本)9783031821523;9783031821530

Feature selection in text classification refers to the critical process of identifying and selecting the most relevant and informative features such as words, phrases, or other linguistic elements from a text dataset. this process, which has been a research topic for decades and finds applications across various fields such as bioinformatics, image recognition, image retrieval, text mining, etc., is essential for optimizing classification accuracy and efficiency. Addressing the challenge of high dimensionality in text data, stemming from the abundance of features like words or n-grams, is crucial to mitigate computational inefficiency and overfitting. Furthermore, the presence of irrelevant or redundant features in text datasets poses another significant challenge, as these features can introduce noise or irrelevant information, thereby undermining the performance of classifier. In this paper, we proposed a new approach GL-SMCHI using improved features selection method to reduce the CHI value of high-frequency words, and globalization technique to incorporate both feature and class information when evaluating the importance of each feature. We compared the results obtained from our proposal against those of existing robust alternatives, the simulation results show that the proposed method outperforms the standard CHI squared, the improved feature selection methods, and the globalization method in term of F-Score and accuracy.

关键词： Features selection CHI-Squared machine learning Globalization Text classification Support Vector machines

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Automatic Modulation Classification in Deep learning 5

Automatic Modulation Classification in Deep Learning

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5th international conference on Communications, Signal Processing, and their Applications (ICCSPA)

作者： Alnajjar, Khawla A. Ghunaim, Sara Ansari, Sam Univ Sharjah Dept Elect Engn Sharjah U Arab Emirates Univ Sharjah Dept Comp Engn Sharjah U Arab Emirates

ISBN: (纸本)9781665482370

Due to the evolution and availability of vast amounts of data for transferring, receiving, and detection, the field of signal recognition and modulation classification has become vital in various fields and applications. Additionally, the classical approaches to machine learning (ML) no more can satisfy the current needs. Hence, this urged researchers to apply deep learning (DL) algorithms that have a very strong ability to train, learn, and automatically classify types of modulation categories. this paper focuses on three vital DL network algorithms, which are deep neural networks (DNN), convolutional neural networks (CNN), and deep belief networks (DBN). the mentioned algorithms are widely used in many applications for automatic modulation classification/recognition (AMC/AMR). Additionally, an empirical study is performed in this paper to compare a large number of different methods for the performance and recognition percentage of each considered technique.

关键词： Automatic modulation classification convolutional neural networks deep belief neural networks deep learning deep neural networks machine learning

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Leveraging DQN-Based Recommender Systems for E-Commerce in Smart Cities 4th

Leveraging DQN-Based Recommender Systems for E-Commerce in S...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Abakarim, Sana Qassimi, Sara Rakrak, Said Cadi Ayyad Univ Fac Sci & Tech L2IS Lab Marrakech Morocco

ISBN: (纸本)9783031821523;9783031821530

In the age of digital urban transformation, smart cities are emerging as ecosystems where technology, infrastructure and data converge to improve quality of life, sustainability and economic prosperity. In this context, e-commerce plays a central role, providing platforms for businesses to thrive by enabling seamless transactions, personalised shopping experiences and greater market reach. However, the dynamic and evolving nature of user preferences presents a significant challenge, requiring more adaptive and smart recommendation systems. this paper presents an approach by integrating Deep Q-Network (DQN), a reinforcement learning technique, into recommendation systems for e-commerce in smart cities. By comparing the proposed DQN model based recommender system with traditional models such as MLP, DeepFM, LSTM and CNN using metrics such as MSE, RMSE and NDCG@5, we demonstrate its superior performance in predicting user preferences and dynamically adapting to changes in user behaviour. the results highlight the potential of DQN models to revolutionise e-commerce recommender systems, delivering more personalised and adaptive user experiences in the interconnected environments of smart cities.

关键词： Deep Q-Network Recommender Systems Reinforcement learning Smart Cities E-Commerce machine learning Deep Reinforcement learning

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Feature Extraction And machine learning Classifiers For Elderly Speech recognition In Comprehensive Geriatric Assessment Cga Questionnaires 5

Feature Extraction And Machine Learning Classifiers For Elde...

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5th international conference on Bio-engineering for Smart Technologies, BioSMART 2023

作者： Zeidan, Dona Elisa Bou Noun, Abir Nassereddine, Mohamad Charara, Jamal Chkeir, Aly Lebanese University Lebanon University of Technology of Troyes France

ISBN: (纸本)9798350338492

Functional decline is a serious syndrome that attacks older adults and can be delayed or prevented by its early risk detection. In this study, speech recognition is used to help in the self-assessment of the geriatric tests used for this purpose. We investigate the effect of the speaker mode, the frame length, the speaker gender, and the vocabulary size in terms of the word recognition rate (WRR) for three different classifiers: RF, KNN, and SVM. the results of our data show that all three classifiers are proper for speech recognition. However, for speaker-dependent mode, the RF gives the maximal performance when separating genders (for females 98.6% and for males 98.1%) and using a 25 ms frame-length with a 10 ms overlap. On the other hand, for the speaker-independent mode, the RF gave also the best performance for 20ms with 10ms overlap, for digits alone WRR=98.1% and binary classification alone WRR=98%. © 2023 IEEE.

关键词： Support vector machines

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