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Research on compression pruning methods based on deep learning 3

Research on compression pruning methods based on deep learni...

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3rd international conference on Signal Processing and machine learning, CONF-SPML 2023

作者： Cai, Mingzhuo Su, Yihong Wang, Binyu Zhang, Tianyu College of Integrated Circuit Science and Engineering Nanjing University of Posts and Telecommunications Nanjing China Haide College Ocean University of China Qingdao China DGUT-CNAM Institute Dongguan University of Technology Dongguan China Faculty of Liberal Arts and Social Sciences Hong Kong University of Education Hong Kong Hong Kong

In the past decade or so, deep neural networks have continuously refreshed the best performance in tasks such as computer vision and natural language processing, and have become the most concerned research directions. Although deep neural networks have significant performance, they are difficult to deploy on hardware-constrained embedded and mobile devices due to their huge number of parameters, computational and storage costs. Therefore, in recent years, the lightweight design of network structure has gradually become a cutting-edge and popular direction, improving the operation speed and optimizing the storage space under the premise of maintaining the accuracy of the neural network. In this paper, the results achieved by domestic and foreign scholars in deep learning model compression are sorted and classified, and the methods of network pruning, model quantification, knowledge distillation, and lightweight network design are summarized, and the compression effect of related methods on known public models is summarized. Finally, the possible directions, application directions and development trends of future neural network model lightweight research are prospected. © Published under licence by IOP Publishing Ltd.

关键词： Deep neural networks

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Autoencoder Feature Based Kinship Verification using machine learning Classifiers 3

Autoencoder Feature Based Kinship Verification using Machine...

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3rd IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022

作者： Jeyashaju, D. Raj, Y. Jacob Vetha Dept. of Computer Science Tamil Nadu Nagercoil629003 India Nesamony Memorial Christian College Dept. of Computer Science Tamil Nadu Marthandam629165 India

ISBN: (纸本)9781665462006

Verification of kinship in an automatic way is a new challenge in vision on the computer that intends to determine if two given individuals have any kinship relationship based on their facial features. Verification of Kinship by facial pictures is difficult since face images have a lot of intra-class variations due to age, genetic and gender difference. Videos can provide more information as they have additional sources of information as compared to single image verification. In this paper, deep learning based Supervised Mixed Norm regularization Autoencoder (SMNAE) is suggested for verification of kinship in unconfined videos. This proposed SMNAE based approach employs the spatial information to test kinship in the video frames. The Random Forest (RF), Support Vector machine (SVM), and Decision Tree (DT) classifiers are used to Classify the decision borderline among kin and non-kin classes. The efficacy of this proposed approach is evaluated on seven kin relationships using a kinship video (KIVI) face dataset of kin pairs. According to experimental results SMNAE spatial features with DT classifier produce 70.14% of f1score, which performs much better than other classifiers. It gives improvement in f1 score of +24.52%, +22.05% than SVM and RF classifiers respectively. Furthermore, when compared to existing systems, the proposed SMNAE spatial features with DT framework performs exceptionally well in kinship verification. © 2022 IEEE.

关键词： Decision trees

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Research on Sports Competition Result Prediction System Based on computer Big Data machine learning Model

Research on Sports Competition Result Prediction System Base...

引用

Electrical Engineering, Big Data and Algorithms (EEBDA), IEEE international conference on

作者： Xiaolong Lu Ming Li Chao Ye Yunnan Vocational College of Sports Kunming Yunnan China Yunnan Youth Sports Training Center Kunming Yunnan China

Using big data theory to analyze athletes' performance data, this paper finds out the changing characteristics of athletes' performance. Using the principle of big data, we find the performance characteristics of athletes in the training process. The particle swarm optimization (PSO) method is used to predict the network parameters of support vector machine (SVM) online. The validity of this method is verified by testing on college students. This method can track athletes' performance characteristics more accurately, so as to build an excellent sports performance prediction model. The model can detect future trends from athletes' performance data in data mining and machine learning. This kind of system has a wide application prospect, and can greatly improve the efficiency and quality. The method combines machine learning theory with support vector machine network, which can effectively improve the prediction accuracy. This method has a very broad application prospect.

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A Novel Computation Offloading under 6G LEO Satellite-UAV-based IoT 3

A Novel Computation Offloading under 6G LEO Satellite-UAV-ba...

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3rd international conference on Computation, Automation and Knowledge Management, ICCAKM 2022

作者： Jaiswal, Kriti Dahiya, Anshul Saxena, Surabhi Singh, Virendra Singh, Archita Kushwaha, Arun University of Lucknow Department of Computer Science Lucknow India Mangalmay Institute of Engineering and Technology Department of Computer Science and Engineering Greater Noida India PES University Department of Computer Applications Bengaluru India Babasahab Bhimrao Ambedkar University Department of Computer Engineering Lucknow India Babu Banarasi das University Department of Computer Application Lucknow India Babu Banarasi das University Department of Computer Applications Lucknow India

ISBN: (数字)9781665453196

ISBN: (纸本)9781665453196

The Internet of Things (IoT) has become a fundamental and omnipresent component of modern life, such as the infrastructure of intelligent transport systems and smart cities. All the next-generation IoT systems necessitate cognitive data processing that could be accomplished promptly through satellite communication. The edge server performs data processing in satellite communication, which is predicted to minimize service delay. However, in prevailing satellite technology, substantial transmission latency, significant route loss, and the resource, as well as energy limitations of IoT devices, all pose challenges to the rigorous service demands for latency and throughput in the upcoming 6G age. To tackle these challenges, 6G IoT will rely heavily on technologies such as space-Air-ground integrated networks (SAGINs), edge computing, deep learning, machine learning, blockchain, and energy harvesting. Combining communication technology with Unmanned Aerial Vehicle (UAV) has recently been identified as one of the potential strategies in the future network. UAVs may provide a variety of services;however, the UAV's onboard computational resources and battery backup are constrained. The Researchers in this article examine satellites as well as UAVs to deliver wireless IoT device cloud computing and edge computing services, respectively. Grounded IoT devices can conduct operations locally or offload them to UAV-based edge servers as well as distant cloud servers via satellites. The major emphasis is on the computational offloading dilemma and considering deep learning methods to optimize the task success rate considering energy dynamics and channel circumstances. The suggested AI-based offloading approach outperformed conventional methods, indicating that it can determine the most energy-efficient offloading policy. © 2022 IEEE.

关键词： Satellites

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COVID-19 Detection in Chest X-Ray Images Using Non-iterative Deterministic learning Classifier 3rd

COVID-19 Detection in Chest X-Ray Images Using Non-iterative...

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3rd international conference on Innovations in Computational Intelligence and computer Vision, ICICV 2022

作者： Husain, Arshi Vishwakarma, Virendra P. Guru Gobind Singh Indraprastha University USICT New Delhi India

ISBN: (纸本)9789819926015

In this work, a non-iterative deterministic learning-based approach, kernel extreme learning machine (KELM) which is an extension to extreme learning machine (ELM) has been utilized to detect COVID-19 pneumonia. In ELM, the input weights are determined at random, and features are mapped explicitly by utilizing activation functions. Hence, the classification process employing ELM is non-deterministic. However, in KELM, the connection weights between the input and hidden layers do not need to be assigned at random. As a result, learning is very quick, and classification is deterministic. KELM improves performance by mapping the features implicitly as the kernel matrix permanently stores the hidden layer outputs after a single calculation. The proposed approach has been experimentally verified on chest X-ray images (pneumonia) dataset. We are able to reduce the classification error rate up to 5.14% with the use of RBF kernel on this dataset using our proposed approach. Experimental results demonstrate that the proposed non-iterative deterministic learning-based technique can obtain competitive detection performance and thus, could be utilized as a better classifier for the diagnosis of COVID-19. Comparisons with a few recent state-of-the-art approaches serve to further emphasize the work's effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： COVID-19

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An Extensive Review of Deep learning Driven Remote Sensing Image Classification Models 3

An Extensive Review of Deep Learning Driven Remote Sensing I...

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3rd international conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Rathod, Vaishnavee V. Rana, Dipti P. Mehta, Rupa G. Svnit Department of Computer Science Engineering GUJARAT SURAT India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Remote sensing images (RSI) are significant data to examine and observe complete structure on the Earth's surface. RSI classification has gained significant attention in earth observation technologies, commonly employed in military and civil fields. It becomes a challenging process because of high dimensional features and small amount of labeled data. Advancements in machine learning (ML) and deep learning (DL) models are capable in effective RSI classification. Numerous research is going on in RSI detection and classification area using ML and DL models. In this view, this article focuses on the review of recently developed RSI classification models. A brief introduction to RSI, types, characteristics and challenging issues is given. By a meta-analysis, different approaches related to the RSI classification models are identified and summarized with key findings. Besides, this survey covers the recently developed ML and DL based RSI classification models with their major aim, methodology used, merits, and demerits. At last, a concluding remark related to the present state of art approaches with possible future scope is discussed. © 2022 IEEE.

关键词： Satellite imagery

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Research on proofreading methods of Chinese semantic knowledge 3

Research on proofreading methods of Chinese semantic knowled...

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3rd international conference on Artificial Intelligence, Information Processing and Cloud Computing, AIIPCC 2022

作者： Bai, Xueli Li, Jianyi Wang, Hongjun Liu, He College of Computer Science North China Institute of Aerospace Engineering Hebei Langfang China TRS Information Technology Co. Ltd. Beijing China

ISBN: (纸本)9783800759330

With the rapid increase of the trend of electronic text, it is more urgent to improve the performance of Chinese text auto-proofreading models. Due to the effective application of deep learning technology, the research on Chinese spelling proofreading and Chinese grammar proofreading has been relatively perfect, but the research on Chinese semantic knowledge proofreading methods is still less, resulting in semantic knowledge proofreading is a weak link in Chinese text proofreading. In this paper, the semantic knowledge proofreading data set is constructed by relation extraction and other techniques, aiming to make the proofreading model can further improve the knowledge reasoning ability in combination with the context. The experimental results show that the method proposed in this paper further improves the knowledge reasoning ability of the model, and can make better use of the semantic knowledge contained in the pre-trained model, which provides a new idea for the following research. © VDE VERLAG GMBH.

关键词： Deep learning

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Method for detecting and recognizing pointer meter reading based on deep learning 3

Method for detecting and recognizing pointer meter reading b...

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3rd international conference on Electronics and Communication;Network and computer Technology (ECNCT)

作者： Liu, Hang Huang, Zhenlin Tian, Lin Chen, Baohao Jiang, Haijiao Ren, Weihua China Southern Power Grid Digital Grid Res Inst Guangzhou Peoples R China China Southern Power Grid 3 ZXCLAA Technol SuZhou EHV Power Transmiss Co Suzhou Peoples R China

ISBN: (数字)9781510652118

ISBN: (纸本)9781510652118;9781510652101

As an instrument widely used in substations, pointer meters are mainly used to detect the working status of substation equipment, so regular calibration is very important. In order to solve the problems of extraction difficulty, large positioning error and poor recognition accuracy in the reading recognition of pointer meters, a method for automatic reading detection and recognition of pointer meters based on deep learning is proposed. Use the improved EAST algorithm to extract character features, and perform module detection on the results. It is concluded that the automatic detection and recognition of analog meters under complex backgrounds has good accuracy and stability, which can meet the application needs of substations.

关键词： deep learning pointer meter convolutional neural network recognition

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A Systematic Review of Artificial Intelligence and Mental Health in the Context of Social Media 3rd

A Systematic Review of Artificial Intelligence and Mental He...

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3rd international conference on Artificial Intelligence in HCI (AI-HCI) Held as Part of the 24th international conference on Human-computer Interaction (HCII)

作者： Chen, Xing Genc, Yegin Pace Univ New York NY 10038 USA

ISBN: (纸本)9783031056437;9783031056420

Objective: We conducted a systematic review on the use of Artificial Intelligence (AI) in the psychology domain within the context of social media's role on mental health. We identified the types of mental health studies that AI has supported, reviewed the machine learning methods in these studies, and reported on the different approaches for data collection. We provided a critical review of the applicability of these methods in real-world settings. Finally, we discussed the challenges faced in this area of study and provided advice for other researchers interested in solving these issues in the future. Methods: We collected our data from three outlets: ACM, JMIR, and CLPsych. We focused on the studies on the application of AI in clinical psychology in the past six years (2016 to 2021). Results: A total of 37 articles were included in our study for further review. The number of publications increased over time. While CLPsych has the highest number of articles, Reddit was the commonly used social media site for data collection. Suicide was the most mentioned mental disorder mentioned in the studies. SVM was the most frequently used approach when applying AI in mental health studies. Conclusion: Our review of the existing literature identified three issues on the topic. First, social media is underutilized for mental health care. Second, there is a lack of collaboration between seemingly disconnected research communities: i.e., machine learning experts and clinicians. Third, little attention is paid to humans when conducting the research.

关键词： Artificial intelligence Social media Mental health Psychology machine learning Clinical

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Sentiment Analysis Based on User Opinions on Twitter Using machine learning 1

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3rd international conference on Applied Technologies, ICAT 2021

作者： Cordero, Jorge Bustillos, José Universidad Técnica Particular de Loja San Cayetano Alto Loja08544 Ecuador

ISBN: (数字)9783031038846

ISBN: (纸本)9783031038839

The growing influence of users on social networks has caused positive and negative content to spread across the Internet. This paper arises from the need to have new tools that allow an analysis of sentiments based on the opinions made by users on the social network Twitter. In particular, the results of identifying the positive and negative opinions issued to a user are presented in order to develop effective communication strategies through social networks. The results obtained allow decisions to be made to obtain a competitive advantage, evaluating positive opinions or, failing that, identifying negative comments to establish strategies to overcome user dissatisfaction. Sentiment analysis can be performed in several ways;however, to obtain greater precision, naïve bayes classifier was used as a machine learning technique, obtaining an accuracy value of 86.2% in the evaluation of the model. © 2022, Springer Nature Switzerland AG.

关键词： machine learning

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