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

Smart Mushroom Cultivation: Enhancing Growth Conditions thro...

引用

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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Adaptive Contour Noise Generation Network for Semi-supervised Medical data Segmentation 6

Adaptive Contour Noise Generation Network for Semi-supervise...

引用

6th International Conference on Artificial Intelligence and Big data, ICAIBD 2023

作者： Ye, Shunlong Nanjing University of Science and Technology Dept. of Computer Science and Engineering Nanjing China

ISBN: (纸本)9781665491259

When training convolutional neural networks, noise distribution in data can affect segmentation accuracy. Therefore, many neural networks will strengthen the segmentation ability of the model by artificially adding noise. However, because the noise distribution of the input data is not known, the model training process can only generate random noise, e.g., gaussian noise, but cannot simulate real-world noise. In the medical imaging domain, more attention is paid to the image contours, which can also be used for noising simulation. Meanwhile, considering the existence of rich unlabeled data sets in medical images, this paper proposes a semi-supervised framework for uncertainty estimation by using Monte Carlo Dropout and generates adaptive noise using the uncertainty map. Specifically, the framework consists of three parts, including a noise generation model, a teacher model, and a student model. The noise generation part processes the data input to the other two parts and uses the contour feature identified by the posteriori uncertainty map to simulate priori noise to guide the process of training segmentation model. Meanwhile, the student model updates its parameters based on the teacher model output and the ground truth annotations by minimizing a joint loss. Segmentation experiments on left atrium 3D data show that our method has competitive results with other SOTA semi-supervised methods. © 2023 IEEE.

关键词： Medical imaging

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ML Assisted Web Application Firewall 12

ML Assisted Web Application Firewall

引用

12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Kalariya, Pranav Jethva, Manthan Alginahi, Yasser University of Windsor Electrical and Computer Engineering Dept. Windsor Canada Computer Science Dept. Adrian College MI United States

ISBN: (纸本)9798350330366

This study presents the development and implementation of a sophisticated Web Application Firewall (WAF) empowered by machine learning techniques to bolster cybersecurity measures. Traditional WAFs primarily rely on rule-based systems, which may struggle to adapt to the evolving nature of web-based threats. In contrast, our proposed solution leverages machine learning algorithms to dynamically analyze and respond to emerging cyber threats, providing a more proactive and adaptive defense mechanism. The core functionality of the system involves the continuous monitoring of incoming web traffic, extracting relevant features, and utilizing a machine learning model to classify the traffic as either benign or malicious. The model is trained on historical data to recognize patterns and behaviors indicative of various cyber threats, including SQL injection, cross-site scripting, and other common attack vectors. Through this learning process, the system becomes adept.at discerning malicious activities and adapting its defense strategies accordingly. The proposed model helps achieve higher precision in identifying the threat requests from normal requests. © 2024 IEEE.

关键词： Learning algorithms

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Authentication and Authorization in Zero Trust IoT: A Survey 35

Authentication and Authorization in Zero Trust IoT: A Survey

引用

35th Irish Systems and Signals Conference, ISSC 2024

作者： James, Meha Newe, Thomas O'Shea, Donna O'Mahony, George D. Munster Technological University Dept. Computer Science Cork Ireland University of Limerick Dept. Electronic and Computer Engineering Limerick Ireland

ISBN: (纸本)9798350352986

A critical challenge in implementing Zero trust (ZT) in Internet of Things (IoT) environments is ensuring secure authentication and authorization mechanisms. The work in this article delves into the intricate realm of ZT by critically examining the state-of-the-art authentication and authorization processes within the ZT framework for IoT. Despite the wealth of work on ZT and Zero Trust Architecture (ZTA), notable research gaps persist, specifically in identity authentication and fine-grained access control. Given the vast and diverse ecosystem of IoT devices, device identification complexities arise from a lack of unique hardware identifiers, rendering traditional methods susceptible to cyber-attacks, such as, spoofing. For dynamic IoT environments, achieving the optimal implementation in access policies poses a significant challenge. Numerous ZTA methodologies, integrating access controls, blockchain frameworks, and advancements in Artificial Intelligence (AI) and Machine Learning (ML), have been implemented to tackle these challenges. However, research gaps remain, particularly in understanding the behavioral aspects within these frameworks. As a result, this article focuses on identifying how device behavioral aspects can improve authentication and authorization in ZT for IoT. Finally, the article concludes by highlighting how these challenges can serve as future research directions. © 2024 IEEE.

关键词： Authorization

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

Diabetes Prediction using Extreme Learning Machine: Applicat...

引用

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

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

引用

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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Deep Learning Approach for Hate and Non Hate Speech Detection in Online Social Media 3

Deep Learning Approach for Hate and Non Hate Speech Detectio...

引用

3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Sharma, Parshuram Tiwari, Rakesh Kumar Technocrats Institute of Technology & Science Dept. of Computer Science Engineering Bhopal India

ISBN: (纸本)9798350342338

People's ability to freely and anonymously share their thoughts and feelings online on social media platforms is contributing to a rising issue of hate speech. Hate speech has the potential to hurt both people and groups, contribute to the polarisation of society, and even provoke acts of physical violence. Therefore, identifying and removing hate speech from online social media is an important task for the purpose of maintaining an online environment that is healthy and respectful. A method based on deep learning is proposed in this study for differentiating between hate speech and other types of communication that may be found in online social media. The solution that has been developed makes use of a natural language processing (NLP) and long term short memory (LSTM) model that is trained on a huge dataset consisting of tweets that have been annotated. Tweets that include hate speech and tweets that do not contain hate speech were both included in the dataset and were labelled by human annotators. Python software with IDE of Spyder version 3.7 is used to carry out the simulation work. The accuracy level reached overall is 91.14%. © 2023 IEEE.

关键词： Hate LSTM Machine Learning NLP Social Media Speech Twitter

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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...

引用

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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Multiclass Classification of Cereals Using Artificial Neural Network 11

Multiclass Classification of Cereals Using Artificial Neural...

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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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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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