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Deep learning-based semantic segmentation of human features in bath scrubbing robots

Biomimetic intelligence & Robotics 2024年第1期4卷 70-79页

作者： Chao Zhuang Tianyi Ma Bokai Xuan Cheng Chang Baichuan An Minghuan Yin Hao Sun School of Artificial Intelligence and Data Science Hebei University of TechnologyTianjin 300401China Engineering Research Center of Intelligent Rehabilitation Device and Detection Technology Ministry of EducationTianjin 300401China

With the rise in the aging population,an increase in the number of semidisabled elderly individuals has been noted,leading to notable challenges in medical and healthcare,exacerbated by a shortage of nursing *** study aims to enhance the human feature recognition capabilities of bath scrubbing robots operating in a water fog *** investigation focuses on semantic segmentation of human features using deep learning ***,3D point cloud data of human bodies with varying sizes are gathered through light detection and ranging to establish human ***,a hybrid filtering algorithm was employed to address the impact of the water fog environment on the modeling and extraction of human ***,the network is refined by integrating the spatial feature extraction module and the channel attention module based on *** results indicate that the algorithm adeptly identifies feature information for 3D human models of diverse body sizes,achieving an overall accuracy of 95.7%.This represents a 4.5%improvement compared with the PointNet network and a 2.5%enhancement over mean intersection over *** conclusion,this study substantially augments the human feature segmentation capabilities,facilitating effective collaboration with bath scrubbing robots for caregiving tasks,thereby possessing significant engineering application value.

关键词： 3D point cloud Human model LiDAR Semantic segmentation

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COMET ■: “cone of experience” enhanced large multimodal model for mathematical problem generation

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science China(Information sciences) 2024年第12期67卷 97-98页

作者： Sannyuya LIU Jintian FENG Zongkai YANG Yawei LUO Qian WAN Xiaoxuan SHEN Jianwen SUN National Engineering Research Center of Educational Big Data Central China Normal University Faculty of Artificial Intelligence in Education Central China Normal University School of Software Technology Zhejiang University

The impact of generative artificial intelligence on education is unprecedented [1]. Researchers have been exploring possibilities of combining the large multimodal model(LMM)with the teaching process. Specifically, Luo and Yang [2]have explored large model collaborative domain models to support smart education, fostering personalized and adaptive educational experiences. However, existing studies still lack in-depth research on generating educational resources,especially in mathematical problem generation.

关键词： Personnel training

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Enhancing Healthcare: Incorporating Blockchain into Electronic Health Records 7

Enhancing Healthcare: Incorporating Blockchain into Electron...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Saratkar, Saniya Langote, Meher Chaudhari, Aarti Thute, Trupti Raut, Rohini Thakre, Gayatri Datta Meghe Institute of Higher Education And Research Faculty of Engineering And Technology Department of Artificial Intelligence And Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798350359299

Handling large amounts of medical data on a blockchain can be challenging, maintaining patient privacy while ensuring data accessibility requires careful design. The healthcare sector is constantly changing, and in order to meet the issues of data security, interoperability, and patient empowerment, it is essential to integrate innovative technology. With its reputation for being decentralized and impervious to tampering, blockchain technology has become a disruptive factor in the redesign of electronic health records (EHR). Blockchain technology has long been a fascinating field of study, and many different businesses have benefited from its applications. Similar to this, the security, privacy, secrecy, and decentralization of blockchain technology present enormous opportunities for the healthcare industry. However, issues with data management, security, and integrity plague Electronic Health Record (EHR) systems. This research study explores how blockchain technology might revolutionize electronic health record systems and perhaps address these problems. © 2024 IEEE.

关键词： Blockchain

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A Novel Approach for Anomaly Detection using Snort Integrated with Machine Learning 18

A Novel Approach for Anomaly Detection using Snort Integrate...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Preethi, T. Reddy, Ponnuru Rakshitha Likhitha, Lekkala Kumar, Pendyala Pavan Kamani, Abhinav Vnr Vignana Jyothi Institute of Engineering and Technology Data Science and Artificial Intelligence & Data Science Department of Cse - Cyber Security Hyderabad India

ISBN: (纸本)9789380544519

In today's digital world, it is crucial to keep a company's information safe from cyber threats. With new and more sophisticated network attacks emerging all the time, better security measures and ways to monitor our networks are needed. Intrusion Detection Systems (IDS) can act as digital guards to help protect our networks from these malicious threats. Snort is a widely used open-source IDS, but it can struggle with the ever-changing nature of these threats. Our research focuses on enhancing Snort's capabilities by integrating it with ML algorithms such as Random Forest, Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Naive Bayes. We also conducted subset testing using the NSL-KDD dataset. Our results indicate that combining these ML algorithms with Snort can improve our ability to detect and stop malicious network activities. This research can be helpful for anyone interested in developing smarter and more effective intrusion detection systems. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Decision Tree Intrusion Detection System K-Nearest Neighbours (KNN) Machine Learning Naive Bayes NSL-KDD dataset Random Forest Snort Support Vector Machine (SVM)

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Product Quality Enhancement in Social Media using Emotion-Enhanced Bert (EE-BERT) Model 2

Product Quality Enhancement in Social Media using Emotion-En...

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2nd International Conference on Self Sustainable artificial intelligence Systems, ICSSAS 2024

作者： Raj Kumar, V.S. Kumaresan, T. Mythily, V. Raghavendran, N. Bannari Amman Institute of Technology Department of Artificial Intelligence and Data science Sathyamangalam India Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India Nandha Engineering College Department of Computer Science and Engineering Erode India Rmk college of Engineering and Technology Department of Artificial Intelligence and Data science Chennai India

ISBN: (纸本)9798350368413

Twitter plays an important role in understanding the consumer sentiment about the products. The advanced analytics and Natural Language Processing (NLP) are used to extract actionable insights from this data and useful for a business to improve features and address issues on time for better customer satisfaction and brand loyalty. Most of the current models suffer from a lack of effective ways to handle subtle sentiment analysis because of a lack of contextual understanding and proper feature extraction. Most traditional models cannot combine and optimize features that lead to obtaining sentiments. This leads to overfitting and problems caused by local minima. The proposed Emotion-Enhanced BERT (EE-BERT) model is an extension of the standard BERT to learn contextual embeddings in user sentiment. In this work, data preprocessing includes text cleaning, tokenization, dealing with emojis and emoticons, and lemmatization with the best quality data. The proposed work is basically a dual-stream architecture extended with butterfly optimizer. First, the advanced tokenization and semantic understanding techniques is used for extracting textual features. Second, the emotion-enhanced features extracted through emotion-hash vectorization to enhance the model's contextual awareness in social media data. This provides a unified framework that combines the feature extraction methods to enhance the accuracy in sentiment analysis. It demonstrated with an impressive accuracy of 98.2% on sentiment in posts and comments with 0.15 losses. The proposed work is free from overfitting and local minima in product quality enhancement. © 2024 IEEE.

关键词： Tweets

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Enhanced Precision and Safety in Minimally Invasive Surgery: The Role of AI and Robotics Integration 4

Enhanced Precision and Safety in Minimally Invasive Surgery:...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Kedar, Rani Sunil Puri, Chetan G. Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Sawangi Meghe Maharashtra Wardha442001 India Maharashtra Wardha442001 India

ISBN: (纸本)9798331529635

A revolutionary development in surgical practice, the incorporation of robotics and artificial intelligence (AI) into minimally invasive surgery (MIS) improves patient outcomes, safety, and precision. This review article examines the development of minimally invasive surgery (MIS) and highlights its notable advantages over open surgery, including shorter recovery periods, less discomfort following surgery, and a decreased rate of complications. The article explores the function of robotic systems, in particular the da Vinci Surgical System, which has transformed surgical operations by giving doctors more dexterity and visualization using robotic arms and high-definition 3D imagery. Additionally, the application of AI technology is explored, demonstrating how it enhances real-time decision-making, intraoperative assistance, and preoperative planning, ultimately improving surgical results. The paper also discusses the ethical and regulatory issues raised by surgical robots' growing autonomy, highlighting the necessity of strong frameworks to guarantee patient safety and fair access to these cutting-edge technologies. This study highlights how artificial intelligence (AI) and robotics have the ability to change the surgical medicine landscape by combining the most recent research and clinical practices. It also calls for continued communication among stakeholders to effectively traverse the complexity of this rapidly developing sector. © 2024 IEEE.

关键词： Clinical research

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Categorisation by Leveraging CNNs and Remote Sensing Satellite Imagery for Crop Analysis in Arid Environments

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Remote Sensing in Earth Systems sciences 2024年第2期7卷 66-79页

作者： Malhan, Anu Chavan, Yogita Rahulsing Swamikan, Brinthakumari Gupta, Megha Varun Bobade, Sunil Department of Artificial Intelligence and Data Science New Horizon Institute of Technology and Management Department of Computer Engineering New Horizon Institute of Technology and Management

The classification of walnuts in the dry areas of Ganquan Township, Awati County, Xinjiang, is investigated in this research using traditional approaches, remote sensing photos taken by satellite, and convolutional neural networks (CNNs). Thorough field sampling and the collecting of Landsat-8 Level-2 data were necessary for correct analysis due to the different environmental features of the research region, which presented major hurdles. Despite some early difficulties, CNN design revisions led to significant gains in classification accuracy. These modifications included AlexNet, VGG, GoogleNet, ResNet, and EfficientNetV2. When compared to more traditional approaches, ResNet and EfficientNetV2 stood up as the most successful models. Nevertheless, challenges including computational complexity and low-resolution imaging highlight the necessity for more research to improve detection efficiency without sacrificing accuracy. The study emphasises the need to keep looking for new ways to analyse and monitor crops, and it suggests that combining remote sensing with advanced machine learning methods could help improve precision agriculture management and increase crop yields in dry areas. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Categorisation of crop Machine learning Precision agriculture Remote sensing Walnut

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A Review on the Developments in the Field of AI-Based Gait Analysis 2

A Review on the Developments in the Field of AI-Based Gait A...

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2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Wankhede, Vidhi Verma, Prateek Gahane, Shailesh Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Sciences and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

The examination of human gait is essential for comprehending motor function, the evolution of diseases, and the effectiveness of rehabilitation in different clinical and research environments. artificial intelligence (AI) advancements have recently improved gait analysis methodologies, resulting in increased accuracy, efficiency, and therapeutic effectiveness. This review article compiles the existing literature on AI-based gait analysis. The review emphasizes the role of AI in gait analysis, namely via the use of vision-based approaches, wearable sensor technologies, markerless motion capture systems, and inertial sensor-based methodologies. AI has shown potential in identifying musculoskeletal problems including osteopenia and sarcopenia, forecasting the start of Parkinson's disease, assessing the effects of post-stroke rehabilitation, and diagnosing neurological conditions. In addition, the integration of AI with new technologies such as augmented reality (AR), smart sensors, and wearable devices presents intriguing opportunities to improve gait rehabilitation, enable individualized therapies, and facilitate remote patient monitoring. Although there have been significant breakthroughs, there are still obstacles in creating AI models that can be easily understood for clinical decision-making, establishing standardized procedures for collecting data, and verifying AI algorithms across various populations. This study underlines the transformational potential of AI-based gait analysis in customized healthcare, rehabilitation techniques, and clinical results. In order to fully harness the potential of AI in enhancing human health and well-being, it is imperative to do more research and foster multidisciplinary cooperation. © 2024 IEEE.

关键词： Clinical research

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Improving Solar Efficiency via CNN-LSTM and Cloud-Integrated IoT Prediction 3

Improving Solar Efficiency via CNN-LSTM and Cloud-Integrated...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Varun, P. Sunitha, T. Nagalingam, Mythili Nithiya, P. Selvakumaran, S. Mohanaprakash, T.A. School of Computing SRM Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India St. Joseph's Institute of Technology OMR Department of Computer Science and Engineering Chennai600119 India Rajalakshmi Institue of Technology Department of Artificial Intelligence and Data Science Chennai India Department of Artificial Intelligence and Data Science Velammal Engineering College Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Institue of Technology Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350396157

Photovoltaic panel used in solar power generation is an environmentally beneficial and sustainable energy source that has been used to transform sunlight into electrical power. Arranged in large solar facilities, these panels are connected to a central inverter, which converts Direct Current (DC) to alternating current (AC) electricity with a small amount of energy loss. Clean surfaces, unhindered light exposure, and high solar irradiance are all necessary for optimal panel performance. It's critical to assess the inverter's efficiency by comparing its AC to DC power. Large-scale installations with sensor-equipped panels and inverters track performance to help with maintenance and forecasting of power generation. The Internet of Things (IoT) facilitates data accessibility and remote monitoring, which helps choose the best location for solar power generation. Smart system continuous monitoring expedites site inspections, which supports urban smart grid integration. In this research study, a hybrid machine learning model is presented by combining the attention processes, long short-term memory (LSTM) networks, and clustering approaches. This model is separated into different phases for forecasting, training, and cloud data clustering, finds pertinent historical data, builds a hybrid machine learning model, and chooses the best training model. In comparison to conventional approaches, this method significantly improves prediction accuracy, which is important for integrating photovoltaic systems into smart grids, particularly in smart cities. © 2024 IEEE.

关键词： Solar power generation

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Modified light GBM based classification of malicious users in cooperative cognitive radio networks

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Cyber-Physical Systems 2024年第1期10卷 104-122页

作者： Sekar, S. Jeyalakshmi, S. Ravikumar, S. Kavitha, D. Department of Information Technology SRM Valliammai Engineering College Chennai India Department of Artificial Intelligence and Data Science SRM Valliammai Engineering College Chennai India Department of Computer Science and Engineering SRM Valliammai Engineering College Chennai India

This paper proposes the Modified Light GBM to classify the Malicious Users (MUs) and legitimate Secondary Users (SUs) in the cognitive-radio network. The proposed method is to avoid the consequences of malicious users in Cooperative Spectrum Sensing (CSS) without the detection of MUs. The method is tested against the occurrence of Always No Malicious User (ANMU), always yes malicious user (AYMU), the Random Malicious User (RMU), and Opposite Malicious User (OMU) transmitting spectrum sensing data to the Fusion Center (FC) by normal secondary users. The efficiency of the proposed method is expressed via simulations and compared with other existing methods. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Signal to noise ratio

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