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Communications and Information technologies (GCCIT), Global Conference on

作者： Mahesh Manchanda Dharmesh Dhabliya K. Ranjith Singh Suhas Gupta Srisainath R Yashoda L Computer Science and Engineering Graphic Era Hill University Dehradun Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Information Technology Vishwakarma Institute of Technology Pune India Department of Computer Science Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai School of Management - UG Management JAIN (Deemed to be University) Bangalore Karnataka

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

The interconnection and communication capabilities of many smart gadgets are made possible by the Internet of Things. Despite the practical advantages of the Internet of Things (IoT), it is susceptible to new forms of assaults due to the introduction of new technology. Intrusion detection systems (IDSs) need a lot of resources to determine the kind of assaults due to the complicated networks and vast amounts of data generated by the Internet of Things (IoT). In contrast, most intrusion detection methods are impractical for IoT networks due to the fact that these networks need greater computational resources for attack detection, which are in short supply on IoT devices. As a result, you need an intrusion detection system (IDS) that isn't too heavy yet can still detect emerging threats. In contrast, most intrusion detection methods are impractical for IoT networks due to the fact that these networks need greater computational capacity for attack detection, which are in short supply on IoT devices. As a result, you need an intrusion detection system (IDS) that isn't too heavy yet can still detect emerging threats. This study recommends a combination of Long Short-Term Memory (LSTM) and Random Forest to optimize detection performance while simultaneously reducing network complexity via the efficient elimination of unnecessary features. Applying hold-out, Stratified k-fold cross-validation, as well as percentage split test mode on the CICIDS-2017 dataset MachineLearning CSV version, the suggested IDS is verified with testing and training data. We opted for this dataset since it contains actual data from IoT networks. The suggested hybrid IDS shows promising experimental results for performance enhancement. Recall values average out to 1.000, resulting in an accuracy rating of 99.9%.

关键词： Accuracy Intrusion detection Training data Traffic control Feature extraction Internet of Things Particle swarm optimization Long short term memory Random forests Testing

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Advancements in Crown Sheath Rot Detection using DenseNet121 and UNet in Rice Plant Leaf Analysis

Advancements in Crown Sheath Rot Detection using DenseNet121...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： R. Thilagavathy S.Senthil Kumar R. Deebalakshmi D. Deepa R. Kavitha L. Sathish Kumar Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur India School of Arts and Science AV Campus Vinayaka Missions Research Foundation (DU) Chennai Tamil Nadu India Department of Computer Science and Engineering SRM Institute of Science and Technology Tiruchirappalli Tamilnadu India Department of Computer Science and Engineering Kongu Engineering College Perundurai Tamil Nadu India Department of Computer Applications Karpagam Academy of Higher Education Coimbatore TN India School of Computing Science and Engineering VIT Bhopal University Kotrikalan MP India

This article aims to investigate the leaf spot disease in rice, a condition that has been observed to cause substantial reductions in crop yield, ranging from 10% to 50%. The disease is characterized by the appearance of circular lesions on the leaves of rice plants, which gradually expand and cover the entire leaf surface, leading to the eventual death of the affected leaves. In order to achieve automation in the detection and severity classification of the disease under investigation, a range of deep learning models were utilized. These models encompassed Faster DenseNet121 and UNet. These methods proposed in this study was trained and tested using a dataset consisting of 1120 images. The findings of the study indicate that the DenseNet121 approach that yielded the highest level of success demonstrated a noteworthy classification accuracy rate of 95.23%. The results obtained from the integration of image processing techniques with deep learning models demonstrated superior performance compared to the analysis solely based on deep learning models. The integration described in this study presents a novel approach to enhance the efficiency of diagnosing leaf spot disease on rice. By leveraging advanced technology, this integration aims to reduce the time required for diagnosis, minimize the occurrence of human errors, and facilitate the timely application of suitable pesticides to mitigate the detrimental effects of the disease.

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Optimizing Patient Flow and Resource Allocation in Hospitals using AI

Optimizing Patient Flow and Resource Allocation in Hospitals...

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Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), International Conference on

作者： Yogesh Rathore Upasana Sinha Jaysing Pandurang Haladkar Nilesh R. Mate Supriya Ashok Bhosale Santoshkumar Vaman Chobe Dept of Computer Science & Engineering Shri Shankaracharya Institute of Professional Management and Technology Raipur Chhattisgarh India Dept of Computer Science & Engineering Guru Ghasidas VishwaVidyalaya (A central University) Department of Electronics and Telecommunication Engineering Sant Gajanan Maharaj College of Engineering Mahagaon M.S. India Bharati Vidyapeeth (Deemed to be University) Centre for Distance and Online Education Pune Department of Computer Science & Engineering Sandip University Nashik Department of Computer Engineering Pimpri Chinchwad College of Engineering & Research (PCCOER) Ravet Pune

The present study investigates the incorporation of artificial intelligence (AI) into healthcare administration, with a specific focus on optimizing patient flow as well as allocating resources in various hospital environments. Using a descriptive research methodology with secondary data gathering, the study adopts an interpretivist philosophy and a deductive technique. The findings show that applying AI-driven techniques significantly improves patient wait times, the efficiency of resource utilization, and patient outcomes. The contrast between AI-enhanced practices and traditional procedures demonstrates how much better they are at optimizing patient flow. On the other hand, implementation-critical elements include initial investment expenses and ethical considerations. Investing in AI infrastructure, resolving algorithmic biases, including protecting data privacy are among the recommendations. Future research ought to concentrate on the scalability, long-term effects, and enhancements to AI systems for objective forecasts

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Context-Aware Social Media User Sentiment Analysis

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Tsinghua science and Technology 2020年第4期25卷 528-541页

作者： Bo Liu Shijiao Tang Xiangguo Sun Qiaoyun Chen Jiuxin Cao Junzhou Luo Shanshan Zhao the School of Computer Science and Engineering Southeast UniversityNanjing 211189China the Key Laboratory of Computer Network and Information of Ministry of Education of China Nanjing 211189China Microsoft Research Asia Suzhou 215000China the School of Cyber Science and Engineering Southeast UniversityNanjing 211189China the Department of Computer Science and Creative Technologies University of the West of EnglandBristolBS161QYUK

The user-generated social media messages usually contain considerable multimodal *** messages are usually short and lack explicit sentiment ***,we can understand the sentiment associated with such messages by analyzing the context,which is essential to improve the sentiment analysis ***,majority of the existing studies consider the impact of contextual information based on a single data *** this study,we propose a novel model for performing context-aware user sentiment *** model involves the semantic correlation of different modalities and the effects of tweet context *** on our experimental results obtained using the Twitter dataset,our approach is observed to outperform the other existing methods in analysing user sentiment.

关键词： social media sentiment analysis multimodal data context-aware topic model

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A Study on the Development and Learning Effectiveness Evaluation of Problem-based Learning (PBL) Virtual Reality Course Based on Intelligence Network and Situational Learning

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第1期7卷 1-20页

作者： Chang, Hui-Ying Chung, Chih-Chao Cheng, Yuh-Ming Lou, Shi-Jer Department of Information Technology and Management Fooying University 151 Jinxue Rd. Daliao Kaohsiung831 Taiwan General Research Service Center National Pingtung University of Science and Technology 1 Shuefue Rd. Neipu Pingtung912 Taiwan Department of Computer Science and Information Engineering Shu-Te University 59 Hengshan Rd. Yanchao Kaohsiung824 Taiwan Graduate Institute of Technological and Vocational Education National Pingtung University of Science and Technology 1 Shuefue Rd. Neipu Pingtung912 Taiwan

This study aims to explore the development of the PBL virtual reality course of intelligence network and situational learning and analyze its learning effectiveness. With students as the learning center, this study innovatively integrated intelligence network technology, situational learning mode, and project-based learning. It also developed a virtual reality project practical course that guides students’ peer cooperative learning and stimulates them in actively exploring and solving problems and providing real-time intelligent feedback. This study adopted the case study method to collect qualitative data, such as teachers’ observations, students’ feedback, project works, and pre-test and posttest evaluations to explore the status of students’ learning effectiveness. The subjects of this study are 48 students of the "virtual reality course" from a case university of science and technology. After 18 weeks of experimental teaching and case study, the result showed that the "PBL virtual reality course of intelligence network and situational learning" could guide students in learning virtual reality knowledge and technology through real-time feedback of intelligent networks and situational and thematic strategies. Most students performed well in the seven abilities of virtual reality projects. The post-test scores of most students in the virtual reality course learning effectiveness were significantly higher than the pre-test scores, indicating that intelligence networks, situational learning, and PBL are help students learn virtual reality knowledge and improve their practical ability. Furthermore, the research results can be used as an important reference for future development of the Metaverse concept in teaching. Moreover, specific suggestions were put forward based on the research results for students, teachers, and future research. © 2022 The authors.

关键词： Virtual reality

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The Efficiency in Controlling and Monitoring a Poultry Farm based on Internet of Things (IoT)

The Efficiency in Controlling and Monitoring a Poultry Farm ...

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IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)

作者： Md Gapar Md Johar Fatria Jumara Adha Asif Iqbal Hajamydeen Lilysuriazna Raya Mohammed Hazim Alkawaz Software Engineering and Digital Innovation Centre Management and Science University Shah Alam Selangor Malaysia School of Graduates Studies Management and Science University Shah Alam Selangor Malaysia Artificial Intelligence and Cyber Security Centre Management and Science University Shah Alam Selangor Malaysia Faculty of Information Sciences and Engineering Management and Science University Shah Alam Selangor Malaysia Department of Computer Science College of Education for Pure Science University of Mosul Mosul Nineveh Iraq

ISBN: (数字)9798350372106

ISBN: (纸本)9798350372113

Every year technology continues to develop, especially in the field of animal husbandry. As demonstrated by the 3.6% increase in chicken consumption in developing nations, it is predicted that both poultry production and consumption will rise between 2005 until 2030. As a result, the chicken business needs to be able to fulfill customer demand. Poultry, particularly chickens, are sensitive to changes in temperature, humidity, and ammonia gas inside the coop, all of which can make the chickens sick. As a result, research is being done to create a server system and develop a framework utilizing IoT technologies. By utilizing Internet of Things (IoT) technology, efficiency in monitoring and controlling chickens can increase production. There are many farmers who still use traditional methods, especially small and medium-scale farmers. This system increases the level of measurement accuracy in the chicken coop using five modules and the device used is flexible so that farmers can move it and place it anywhere. using this system, farmers can manage chicken farming more effectively and measurably, which farmers can manage simply by monitoring via a website that can be accessed anytime and anywhere.

关键词： Temperature sensors Microcontrollers System performance Production Real-time systems Manufacturing Internet of Things

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Exploring the Relationship Between Learning Achievement and Discussion Records in Remote Maker Activities 5th

Exploring the Relationship Between Learning Achievement and ...

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5th International Conference on Innovative technologies and Learning, ICITL 2022

作者： Chien, Yu-Cheng Cheng, Pei-Yu Csui, Lin-Tao Yang, Yeongwook Hooshyar, Danial Huang, Yueh-Min Department of Engineering Science National Cheng Kung University Tainan City Taiwan Department of Information Management National Chin-Yi University of Technology Taichung City Taiwan Department of Information Management Tamkang University New Taipei City Taiwan Division of Computer Engineering Hanshin University Osan-si Korea Republic of School of Digital Technologies Tallinn University Tallinn Estonia

ISBN: (纸本)9783031152726

Maker’s popularity worldwide has led to numerous studies on maker education to enhance learner competitiveness. One important research topic in educational research is how to effectively measure learning performance. Given the rapid development of science and technology, it is now possible to use learner discussion data to measure learning outcomes. We propose using natural language processing (NLP) technology to analyze learner discussion records by using speech-to-text technology to convert audio files into text files and using NLP technology exploring the resultant discussion texts. Experimental results reveal significant relationships between learner discussion and learning effectiveness, participation, and teamwork. This analysis also shows that high-achieving learners often discuss programming-related keywords. The proposed method can be used to analyze learner discussions and thus to measure their learning achievements. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Natural language processing systems

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Remote monitoring system using slow-fast deep convolution neural network model for identifying anti-social activities in surveillance applications

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Measurement: Sensors 2023年 27卷

作者： Michael Onyema, Edeh Balasubaramanian, Sundaravadivazhagn Suguna S, Kanimozhi Iwendi, Celestine Prasad, B.V.V. Siva Edeh, Chinecherem Deborah Department of Vocational and Technical Education Faculty of Education Alex Ekwueme Federal University Ndufu-Alike Abakaliki Nigeria Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Information Technology University of Technology and Applied Sciences AlMussanah Oman Department of Computer Applications ArulmiguArthanareeswarar Arts and Science College Tamil Nadu Tiruchengode India School of Creative Technologies University of Bolton United Kingdom Department of Computer Science and Engineering Anurag University Hyderabad India Faculty of Law Enugu State University of Science and Technology Nigeria

Remote monitoring is the process that monitors and observes information from a distance utilizing sensors or electronic types of equipment. Remote monitoring is used in real-time applications like traffic, forest, military, shops, and hospitals to determine abnormal activities. Earlier research has done video processing methods based on computer vision techniques, but the computational complexity regarding time and memory is high. This paper designs and implements a novel Slow-Fast Convolution Neural Network (SF–CNN) to identify, detect, and classify abnormal behaviours from a surveillance video. The proposed CNN architecture learns the video frames automatically, obtains the most appropriate properties about various objects' behaviour from a large set of videos. The learning process of SF-CNN is carried out in two ways, such as slow learning and fast learning. The slow learning process is enabled when the frame rate is less, and the rapid learning process is enabled when the frame rate is high. Both the learning processes learn spatial and temporal information from the input video. Different objects, such as humans, vehicles, and animals, are detected and recognized according to their actions. All the videos have normal and abnormal activities that vary in various contexts. The proposed SF-CNN architecture provides an end-to-end solution to dealing with multiple constraints abnormal movements. The experiment is carried out on several benchmark datasets, and the performance of the SF-CNN architecture is evaluated. The proposed approach obtained 99.6% of accuracy, which is higher than the other existing techniques. © 2023 The Authors

关键词： Object detection

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iConDet2: An Improved Conjunctivitis Detection Portable Healthcare App Powered by Artificial Intelligence 2nd

iConDet2: An Improved Conjunctivitis Detection Portable Heal...

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2nd International Conference on Applied Intelligence and Informatics, AII 2022

作者： Adak, Mainak Chatterjee, Aayushman Roy, Nilanjana Dutta Mahmud, Mufti Department of Computer Science and Engineering Institute of Engineering and Management Kolkata India Department of Computer Science Nottingham Trent University Clifton NottinghamNG11 8NS United Kingdom Computing and Informatics Research Centre Nottingham Trent University Clifton NottinghamNG11 8NS United Kingdom Medical Technologies Innovation Facility Nottingham Trent University Clifton NottinghamNG11 8NS United Kingdom

ISBN: (纸本)9783031248009

Conjunctivitis is one of the common and contagious ocular diseases which affects the conjunctiva of the human eye. Both the bacterial and viral types of it can be treated with eye drops and other medicines. It is important to diagnose the disease at its early stage to realise the connection between it and other diseases, especially COVID-19. Mobile applications like iConDet is such a solution that performs well for the initial screening of Conjunctivitis. In this work, we present with iConDet2 which provides an advanced solution than the earlier version of it. It is faster with a higher accuracy level (95%) than the previously released iConDet. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diagnosis

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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

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