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Intelligent Behavior Recognition based on Gyroscope and Accelerometer

Intelligent Behavior Recognition based on Gyroscope and Acce...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Huang, Pin-Yu Lu, Yung-Han Lin, Chun-Ting He, Bo-Xian Pai, Tun-Wen Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350324174

In an aging society, the rate of dementia would be dramatically increased. It is urgent to provide intelligent facilitative devices and appropriate medical care for those who with dementia symptoms, especially for the elders. Accordingly, the primary research target of this study is to automatically detect and monitor behavior of the elderly suffered from dementia. Although previous research has been conducted to identify various human movement patterns, the experimental data were all received from cameras. Furthermore, these related applications are not specifically designed to identify the behavior of the elderly with dementia. In order to improve previous research methods, our study adopted wearable multi-axis sensors to collect corresponding motion data with protected privacy. In addition to collecting raw data containing time series signals, frequency and spatial domain features were applied to detect human behaviors, and Random Forest algorithm was applied to construct a motion prediction model. The classification system for real-time motion recognition contained two main actions of users including holding/putting hand gestures and walking model. Finally, an additional camera on the chest was designed to be triggered and to take a shot according to previous settings. It could enhance practical applicability of the proposed intelligent system to assist the elderly with dementia to find her/his lost objects. This study developed an app for recording individual behaviors, checking the location and time of lost objects, and even recording activity information to provide healthcare professionals with decision-making suggestions for subsequent precision medical care. © 2023 IEEE.

关键词： Accelerometers

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A comparative study of different scheduling approaches for splittable latency sensitive tasks in Fog-Cloud environment 2

A comparative study of different scheduling approaches for s...

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2nd International Conference on Ambient Intelligence in Health Care, ICAIHC 2023

作者： Sandeep, Kone Sai Koundinya, Challa Aditya Prabhas, Adapa Venkata Swain, Chinmaya Kumar Mishra, Sambit Kumar SRM University Dept. of Computer Science Engineering India

ISBN: (纸本)9798350344882

IoT has revolutionized the way we live and the work we do by connecting different devices through the Internet. In the present scenario, the number of IoT devices are increasing rapidly due to the increase in technology and the increase in the comforts of life. Nowadays we can see that many of them are using IoT devices regularly, it's estimated that by the end of 2030, there will be 30 billion users who will be using IoT applications. These devices send data to the cloud for processing. Due to the distance of the cloud from the IoT devices, the application requests get delayed service responses. So to handle the latency sensitive applications we require the micro cloud service like fog servers deployed near to the data generation points. The fog layer lies between the IoT devices and Cloud which acts as an intermediate layer. This helps in reducing latency of the tasks and provide better performance. As the number of IoT applications keeps on increasing, the resources available with the fog nodes may not handle the upcoming demands. So to overcome these demands, we are using splittable methods to allocate the tasks to Fog/ Cloud nodes more compactly. If a task can be splitted before the deadline into different modules, then we split the given task and allocate those tasks to different fog nodes/ servers and then collecting back the data from the fog nodes/ servers and merging them into a single unit. With the help of this method, we can increase the performance of the system. © 2023 IEEE.

关键词： Fog computing

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Multiclass Classification of Cereals Using Artificial Neural Network 11

Multiclass Classification of Cereals Using Artificial Neural...

引用

11th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2023

作者： Thapliyal, Suhani Singh, Tejaswini Nainwal, Ankita Sharma, Garima University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350326147

A diverse variety of finer features of objects may now be detected, processed, analyzed, and displayed by machine learning and imaging systems from their digital photographs in real-time thanks to recent advancements in hardware and software. The food business must keep up with population expansion by raising output and raising product standards. Cereals, which are at the bottom of the food industry's pyramid and are produced for both human and animal use, are directly or indirectly tied to the rise in food production. The cornerstone of human civilization and an integral part of daily living are cereal grains. Food, energy, and raw materials for industry have all become insatiable human needs in recent years, and agricultural production has grown significantly as a result. Also, cereal grains are an essential component of human diets and significant raw materials for both domestic and international trade. Automatic segregation of cereal grains becomes significant with time and advent of new technologies. Manual efforts not only lead to errors but are also less cost efficient. This study shows the potential of machine learning techniques for cereal recognition using computer vision, and it also discusses some problems that need to be fixed or looked into further in order to hasten the use of learning algorithms. © 2023 IEEE.

关键词： Neural networks

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Smart Mushroom Cultivation: Enhancing Growth Conditions through Sensor-Based Monitoring

Smart Mushroom Cultivation: Enhancing Growth Conditions thro...

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2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Rana, Ishaan Gupta, Ankit Mittal, Rakshit Chaudhary, Harshita Tyagi, Shobha Chawla, Pronika Mriirs Dept. of Computer Science and Engineering Faridabad India

ISBN: (纸本)9798350344387

Smart mushroom cultivation using remote sensing involves the integration of advanced technology to optimize the growth and productivity of mushrooms. Remote sensing techniques using arduino, sensors and cloud services are employed to monitor and manage various farming and growth related parameters in mushroom farms. Using the remote sensing of mushroom fields provides a macroscopic view of environmental conditions, helping farmers identify suitable locations for mushroom cultivation based on factors like temperature, humidity, and vegetation cover. Ground-based sensors are strategically placed within the cultivation area to measure crucial variables like temperature and humidity levels. These sensors transmit data to a central control system, allowing farmers to maintain optimal conditions for mushroom growth. Automated climate control systems can adjust parameters in response to real-time data, ensuring consistent and ideal growing conditions. This enables timely intervention, reducing crop loss and the need for chemical treatments. Furthermore, remote sensing can improve resource management. Precise water and nutrient monitoring based on sensor data can lead to more efficient resource utilization, reducing waste and operating costs. In summary, smart mushroom cultivation using remote sensing technology optimizes environmental monitoring, and resource usage. By harnessing the power of remote sensing, mushroom farmers can increase yields, reduce environmental impact, and enhance the overall sustainability and profitability of their operations. © 2023 IEEE.

关键词： Fans Fuzzy logic Humidity Schedules Temperature

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Diabetes Prediction using Extreme Learning Machine: Application of Health Systems 5

Diabetes Prediction using Extreme Learning Machine: Applicat...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Reddy, S. Nava Bharath Reddy, K.V. Narasimha Rao, S.N. Tirumala Kumar, K.S.M.V. Dept. of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9781665474672

Recently, the health sector is widely adopting artificial intelligence models such as machine learning (ML), deep learning for data analysis, disease prediction, and disease classification. However, the conventional models failed to analyze the data. Therefore, this work is focused on analysis of diabetes prediction using extreme learning machine (DP-ELM) model. Initially, Pima Indian diabetes is considered, which is pre-processed for missing data symbols identification. Then, the statistical features from pre-processed dataset are extracted using principal component analysis (PCA). Then, ELM model is trained with the PCA features and forms the trained feature dataset. Then, a random test combination is applied for ELM testing, which classifies the positive and negative status of diabetes. The simulations proved that;the proposed DP-ELM outperformed in terms of accuracy as compared to existing methods. © 2023 IEEE.

关键词： Forecasting

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Detection of Predatory Publishing Using Mixed Dataset: Proposal of a Data Model 11

Detection of Predatory Publishing Using Mixed Dataset: Propo...

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11th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2023

作者： Sharma, Garima Tripathi, Vikas Singh, Vijay University Dept. Of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350326147

Research Publications typically showcases new findings and conclusions. To ensure the reliability of publication, various authentic databases have been curated such as enlisting scholarly articles, research papers, etc. One such key database is Scopus. It is one of the largest scholarly directories of peer-reviewed literature in scientific, technical, medical, and social science. In this work, data from varied authentic publishing sources such as Scopus, DOAJ, Beall's, and Open Dataset for Predatory Publishers is collected, merged, and transformed into a single schema, and validation of publishers and their titles is analyzed. The publisher and their titles being classified into three categories: green, orange, and red nodes based on their title's presence in different authentic sources. All the green nodes publishers have every single title placed under the legitimate category in all our focused data sources, orange ones have either of their titles in the suspicious category and red ones are completely illegitimate ones. The results show the change in the percentage of legitimate publishers individually and produced single schema with a significant difference. With the proposed strategy the possibility of finding the legitimation of publishers becomes more accurate for further scientific assignments. © 2023 IEEE.

关键词： Citrus fruits

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Leukemia Cancer Classification from Abnormal Cell Images Using a Deep Learning Approach: A Comprehensive Study 26

Leukemia Cancer Classification from Abnormal Cell Images Usi...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Afrin, Sadia Paul, Subrata Kumer Paul, Rakhi Rani Haque, Md. Momenul Kabir, Md Shahriar Fahim, Sultan Islam, Md. Kamrul Dept. of Computer Science and Engineering Natore6431 Bangladesh

ISBN: (纸本)9798350359015

Leukemia, a malignant blood cancer, is characterized by the uncontrolled proliferation of abnormal white blood cells in the bone marrow and blood. It is a life-threatening disease with various subtypes, making precise classification crucial for effective treatment. However, accurate classification of leukemia subtypes from abnormal cell images poses a challenging problem due to the complex and subtle variations in cell morphology. In response to this challenge, this comprehensive study employs an innovative machine learning approach, utilizing the EfficientNet B3 architecture. With this model, we achieved an impressive accuracy rate of 98.95% and a minimal loss of 0.120, demonstrating its efficacy in classifying leukemia subtypes. To conduct our experiments, we utilized the Kaggle Leukemia cancer dataset, ensuring a robust and reliable evaluation of our proposed methodology. This research contributes significantly to the development of efficient tools for early diagnosis and personalized treatment strategies for leukemia. © 2023 IEEE.

关键词： Acute Lymphoblastic Leukemia (ALL) Deep Learning EfficientNet B3 Leukemia cancer

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Preparing and Annotating a Set of Relevant Contextual Words from the Text Corpus and the Online Newspaper Archives in Bengali 4

Preparing and Annotating a Set of Relevant Contextual Words ...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Pal, Alok Ranjan Adhikari, Prithwish Dutta, Abhishek Pal, Antara Saha, Diganta College of Engg. and Mgmt. Dept. of Computer Science and Engg. Kolaghat India Atharva College of Engineering Dept. of Computer Science and Engg. Mumbai India Jadavpur University Dept. of Computer Science and Engg. Kolkata India

ISBN: (纸本)9798350343670

Context analysis is one of the obvious tasks in nearly all computational linguistics based works. All of these works pursue this task upto different extents using different techniques. Most of the techniques include the linguistic features, like - N-grams, content words, function words, noun phrase and verb phrase, theme extraction with relevance scoring, facets etc. There are lots of machine learning and deep learning based tools available for this task. The performances of these tools are variable according to different situations. But, the close observations cite that the performance of these systems mainly depends on the availability of the contextual words at that particular experiment. Except for a good collection of contextual words, no system can produce an effective result by using only some complex mathematical calculations. This scenario is the motivation behind this work. In this work, an attempt has been made to construct a structured set of most relevant contextual words according to the classification of text domains, mentioned in the Technology Development for Indian Languages (TDIL) corpus. As it is a group work, therefore different categories of people have been involved in this work for preparing, annotating, verifying and validating the data sets. © 2024 IEEE.

关键词： Collocating words Content words Context analysis Relevance Factor TDIL Corpus

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Revolutionizing Requirements Elicitation: Deep Learning-Based Classification of Functional and Non-Functional Requirements 5

Revolutionizing Requirements Elicitation: Deep Learning-Base...

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5th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2024

作者： Hanna, Grace Boyar, Nathan Garay, Nathan Maleki, Mina University of Detroit Mercy Dept. of Electrical and Computer Engineering and Computer Science DetroitMI United States

ISBN: (纸本)9798350355925

The requirements elicitation phase in the software development life cycle (SDLC) is both critical and challenging, especially in the context of big data and rapid technological advancement. Traditional approaches like workshops and proto-typing, while useful, often struggle to keep pace with the massive data volumes and rapidly changing user demands characteristic of modern technology. This paper introduces a data-driven approach that utilizes deep learning (DL) and natural language processing (NLP) to enhance the requirements elicitation process by extracting requirements and classifying them into functional and non-functional categories. Our research involves a deep neural network (DNN) trained on a large dataset of transcriptions from client/user stories. This DNN can identify whether a specific text represents a functional requirement, a non-functional requirement, or neither. Our approach shows a marked improvement over previous methods, with a 33% increase in accuracy and an 18% increase in the F1 score. These results indicate the capability for deep learning techniques to play a vital role in elicitation. © 2024 IEEE.

关键词： Software design

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Job applications Selection and Identification: Study of Resumes with Natural Language Processing and Machine Learning

Job Applications Selection and Identification: Study of Resu...

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2023 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2023

作者： Pimpalkar, Amit Lalwani, Aastha Chaudhari, Roshan Inshall, Mohd. Dalwani, Mahak Saluja, Tarandeep Jit Dept. of Computer Science Engineering Nagpur India

ISBN: (纸本)9798350398748

For each job ad, the organization received a high number of applicants. Finding the right person's application from various applications is a time-consuming task for any company these days. The categorization of a representative's resume is a time-consuming, labor-intensive, and resource-intensive procedure. Natural language processing and advanced analytics can understand and analyze unstructured written material to extract the required information. The idea is to teach the machine to study text in the same manner that people do. By reviewing applications with natural language processing and machine-learning processing, we may be able to locate people with the abilities and attributes we need more quickly. This study provides a comprehensive assessment of resume selection and identifies the existing work gaps. Different algorithms for machine learning and methodologies for analyzing and interpreting unstructured data have been researched. This study also addresses the research problems and future potential of resume analysis regarding the writing style, word choice, and grammar of unstructured written communication. © 2023 IEEE.

关键词： Machine learning

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