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Streamlining Information Capture and Retrieval with AI-Enabled Systems 3

Streamlining Information Capture and Retrieval with AI-Enabl...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Sudhamsu, Gadug Agarwal, Ankita Dhivrani, Rekha Bhanu, K.S. Jeyalaxmi, M. Nawadkar, Ashwini R. Faculty of Engineering and Technology Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Atlas SkillTech University Department of UGDX Maharastra Mumbai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India

ISBN: (纸本)9798350319125

artificial intelligence (AI) is an emerging era that has proven to have a fantastic capacity to revolutionize how facts are captured and retrieved. AI-enabled systems offer an expansion of abilities to streamline the system of accumulating, managing, storing, and retrieving data. AI-enabled structures provide several benefits, such as using predictive analytics and herbal Language Processing (NLP) to extract records more effectively and correctly and automated pass-referencing competencies to simplify locating records. AI-enabled systems make indexing and saving statistics easier, allowing users to look for the relevant data they need quickly. AI-enabled structures offer higher security measures and encryption techniques for defensive touchy facts. Functions like facial recognition, gadget mastering algorithms, and get admission to hold data safe and far from malicious actors. Such systems may control and store statistics securely, correctly, and managed. AI-enabled systems provide an excellent suite of features for streamlining the manner of seizure and retrieval of statistics. AI answers, including predictive analytics, natural language processing, and automated cross-referencing, allow groups to quickly get the right of entry to the relevant information they need, even by providing higher safety features for included facts. In destiny, this technology might also grow to be even greater advanced, assisting businesses in gaining facts to electricity predictive analytics, helping in advertising efforts, and enhancing the organizational operation. © 2023 IEEE.

关键词： information organizational predictive processing security

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Deep Generative Model with Isolation Forest (DGM-IF) for Unsupervised Anomaly Detection in Wireless Sensor Network and Internet of Things 9

Deep Generative Model with Isolation Forest (DGM-IF) for Uns...

引用

9th International Conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Zerkouk, Meriem Mihoubi, Miloud Chikhaoui, Belkacem University of Sherbrooke Faculty of Electrical Engineering Mathematics and Computer Science Enschede7500 AE Netherlands University of Téluq Department of Information Technology Montréal Canada

ISBN: (纸本)9798350311402

Anomaly detection is crucial for maintaining the reliability and security of wireless sensor networks and loT systems. Conventional methods require labeled data, often unavailable in these systems, making unsupervised techniques essential. Deep learning has shown promise in unsupervised anomaly detection tasks, including wireless sensor networks and loT applications. The Isolation Forest, an unsupervised anomaly detection algorithm, isolates anomalies based on in-herent properties. Combining deep learning with the Isolation Forest can improve accuracy and effectiveness in detecting anomalies. This paper presents the Deep Generative Model with Isolation Forest (DGM-IF), a novel unsupervised anomaly detection method for wireless sensor networks and loT. DGM-IF integrates deep generative models with the Isolation Forest algorithm to learn a robust representation of normal data and identify anomalies. The model generates synthetic data based on the learned distribution and employs the Isolation Forest to separate deviating data points. The proposed technique is assessed using real-world datasets and benchmarked against cutting-edge methods, proving its efficacy in detecting anomalies. The DGM-IF approach has the potential to significantly enhance the reliability and security of wireless sensor networks and IoT systems by identifying potential threats and attacks. © 2023 IEEE.

关键词： Wireless sensor networks

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Optimizing Patient Satisfaction Surveys and Feedback with Cloud Computing and NLP Techniques 8

Optimizing Patient Satisfaction Surveys and Feedback with Cl...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Jayanthi, L.N. Shanmugam, Kannan Suma, Challapalli Sagar, B.S. Maniraj, S.P. Ganeshbabu, T.R. Saveetha College of Liberal Arts and Sciences Department of Commerce Tamil Nadu Chennai India Vit Bhopal University School of Computing Science and Engineering Department of Gaming Technology Madhapradesh Bhopal India Department of Computer Science and Engineering Andhra Pradesh Bhimavaram India Reva University Department of Electrical and Electronics Engineering Karnataka Bengaluru India Faculty of Engineering & Technology Department of Data Science and Business Systems Chennai Tamil Nadu Kattankulathur India Department of Electronics and Communication Tamil Nadu Namakkal India

ISBN: (纸本)9798350376425

Healthcare quality assessment and identification of opportunities for improvement are crucially facilitated by patient satisfaction surveys. Traditional approaches sometimes face difficulties in handling substantial amounts of written comments and obtaining real useful data. It presents a new method that combines cloud computing with Natural Language Processing (NLP) approaches to enhance the analysis of patient satisfaction data. By using cloud platforms, it can effectively manage large datasets and provide scalable, cost-efficient processing. The use of NLP methods, such as text mining and sentiment analysis, enables the conversion of unstructured input into structured insights, thereby uncovering the fundamental patient feelings and patterns. This connection enables the analysis of data in real-time also improving the accuracy of interpreting feedback and facilitating fast enhancements in healthcare services. Future research will prioritise the improvement of NLP algorithms and the investigation of supplementary cloud-based technologies to enhance the accuracy and efficiency of patient satisfaction analysis to improved patient care and experience. © 2024 IEEE.

关键词： Sentiment analysis

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Virtual Twin Analytics: Advancing IoT Security through AI-Powered Software Informatics

Virtual Twin Analytics: Advancing IoT Security through AI-Po...

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Software, Systems and Information technology (SSITCON), International Conference on

作者： S. Nanthini Upma Jain Renuka Arora Rishu Bhatia Kruti Sutaria Harshal Patil Department of Computer Science and Engineering Saveetha School of Engineering Chennai India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science and Engineering Jagannath University Haryana India Department of Electronics and Communication Engineering Ganga Institute of Technology and Management Haryana India Faculty of Engineering and Technology Parul institute of Engineering and Technology Gujarat India School of Computer Science and Engineering Uttar Pradesh India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

The Internet has grown in importance and impact over the years, causing people to become more reliant on it. The Internet has evolved into a major vector for cybercrime because to its ever-increasing user base. Over the last decade, the number of these computing systems—including desktops, laptops, smartphones, and the Internet of Things (IoT)—has skyrocketed. Among them, cell phones are practically integral to modern life. The popularity of web-based assaults has skyrocketed with the exponential growth in the number of individuals using the Internet. These web-based assaults are increasingly being combatted by security corporations. Unfortunately, new forms of these assaults are appearing all the time, making it hard for older security measures to stay up. Artificial intelligence (AI) is a source of optimism in the current cybersecurity landscape, offering a potential solution to the ever-changing digital dangers. The fast development of AI over the last decade has given rise to this optimism, because it is now impacting the expansion of every industry. With AI bringing so many advantages in every field, online security is one sector that just cannot afford to ignore it. This planned effort’s work represents an advance in that direction. Critical online security issues have been the focus of this proposed work’s study, which aims to address these issues using AI. Web security issues for desktop and mobile devices have been addressed in the proposed work. The planned work’s contributions to online security are as follows: The ‘MalCrawler’ web crawler is a targeted tool for finding and exploring the web. This crawler makes it easy to gather websites, particularly ones that are harmful. It does a better job of collecting dangerous websites than a typical crawler. Additionally, it is built to circumvent the evasion strategies used by rogue websites. The crawler’s ability to gather webpages—particularly dangerous ones—in order to provide datasets for ML-based analysis a

关键词： Analytical models Computational modeling Crawlers Vectors Digital twins Internet of Things Security Artificial intelligence Informatics Context modeling

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Development of a Closed Chamber System for Cultivating Lion's Mane Mushrooms using an Internet of Things System to Increase Yield and Economic Value

Development of a Closed Chamber System for Cultivating Lion'...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

作者： Pakorn Suntornmeth Kanitha Homjun Worawit Fankam-Ai Charnyut Karnjanapiboon Atigorn Sanguansri Athiphong Deenoi Sirilux Kaewsirirung Department of Computer Science Faculty of Science and Agricultural Technology Rajamangala University of Technology Lanna Nan Nan Thailand Department of Electrical Engineering Faculty of Engineering Rajamangala University of Technology Lanna Nan Nan Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

This research investigates the development and implementation of an Internet of Things (IoT)-based closedsystem prototype for cultivating Yamabushitake (Lion's Mane) mushrooms, addressing the challenges of achieving optimal yields in Thailand's climate. Conducted at Saengkaew Mushroom Farm located in Nan Province, the study focuses on precise environmental control within the prototype chamber. The system employs an ESP32-IOXESP32U microcontroller and an AM2315 sensor to monitor and manage temperature, humidity, and airflow. A comparative analysis with a traditional evaporative cooling system reveals the closed system's superior performance in achieving higher yields and improved energy efficiency. The assessment of economic feasibility is conducted via a Cost-Benefit Analysis (CBA), demonstrating the potential for increased profitability and sustainable mushroom cultivation. This research offers significant findings regarding the utilization of IoT technology to enhance mushroom production and advance sustainable agricultural practices in Thailand.

关键词： Economics Temperature sensors Temperature measurement Scalability Prototypes Robustness Energy efficiency Maintenance Internet of Things Cost benefit analysis

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Intrution detection system Using IoT device for safety and security 2

Intrution detection system Using IoT device for safety and s...

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2nd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2021

作者： Shreenidhi, H.S. Prabakar, Sachin Kumar, P Ashish Faculty of Engineering Technology Bengaluru India Computer Science and Engineering in Cloud Technology and Information Securiy Faculty of Engineering Technology India

ISBN: (纸本)9781665429214

The internet of things widely used nowadays. All things are connected to internet. It involves the creation of vulnerability scanner tool for IoT device which can be run in the system to detect vulnerability. With help of software and system configuration threats are detected. Which can lead to possible vulnerability and we also wrote attack script which performs an attack on the system to detect if the attack can be done on the system by using software tool configuration error identified. The proposed system identify the vulnerability of the architecture leads to Bruteforce attack and with the help of attacks script are able to find out if there are any open ports which could be used to exploit the system and also perform Dos DDos attack to check if the system is vulnerable for it along with it we also check if the data stored in the IoT device is encrypted or not. Once the scan is complete a detailed report is sent to the user Email. © 2021 IEEE.

关键词： Internet of things

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Surface Sampling, Vertex Manipulation and Surface Generation Based on WebGL Technologies 7

Surface Sampling, Vertex Manipulation and Surface Generation...

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7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023

作者： Runchevski, Danail Rechkoska-Shikoska, Ustijana Salkoski, Rasim University for Information Science and Technology 'St. Paul the Apostle' Faculty of Communication Networks and Security Resen Macedonia University for Information Science and Technology 'St. Paul the Apostle' Faculty of Computer Science and Engineering Ohrid Macedonia University for Information Science and Technology 'St. Paul the Apostle' Faculty of Information Systems Visualisation Multimedia and Animation Ohrid Macedonia

ISBN: (纸本)9798350342154

Surface sampling is a powerful technique used in computer graphics and visualization to gather data from 3D surfaces. Surface sampling is a process of collecting visual and tactile information from real world surfaces to serve as references for artistic creations. It allows artists to study and analyze the intricate details of surfaces, such as the roughness of tree bark, the smoothness of a pebble, or the subtle variations in fabric patterns. By examining surfaces up close, artists gain insights into the nuances that make each surface visually and texturally distinct. The solution we've developed operates seamlessly on both desktop and mobile devices, utilizing the functionalities of widely used web browsers. It eliminates the need for any software installations. Our achievement is founded on fully open-source solutions, allowing users to explore 3D models without any barriers. © 2023 IEEE.

关键词： Web browsers

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Arabic Sentiment Analysis of Food Delivery Services Reviews

Arabic Sentiment Analysis of Food Delivery Services Reviews

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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Mustafa, Dheya Khabour, Safaa M. Shatnawi, Ahmed S. Taqieddin, Eyad The Hashemite University Faculty of Engineering Dept. of Computer Engineering Zarqa13133 Jordan Yarmouk University Faculty of Information Technology and Computer Sciences Dept. of Information Systems Irbid21163 Jordan Jordan University of Science & Technology Dept. of Software Engineering Irbid21110 Jordan School of Computing and Informatics Dept. of Cyber Security Al-Hussein Technical University Amman Jordan

ISBN: (纸本)9798350335590

Customer reviews on online platforms have grown to become an important source of insight into a company's performance. Food delivery services (FDS) companies aim to effectively use customers' feedback to identify areas for improvement of customer satisfaction. Although Arabic is becoming one of the most widely used languages on the Internet, only a few studies have focused on Arabic sentiment analysis to date. The present study conducts an extensive emotion mining and sentiment analysis on FDS-related reviews in Arabic, exploiting natural language processing, and machine learning techniques to extract information that reflects customers' subjective viewpoints, recognize their feelings, and determine polarity in the FDS domain. This work begins with collecting the FDS dataset (Talabat), and then extracting and creating a dialects lexicon for Arabic dialects, followed by walking the reader through detailed steps of cleaning and pre-processing a manually annotated dataset. Finally, we examined classification algorithms including Decision Tree (DT), and Support Vector Machine (SVM). We achieved a maximum accuracy of about 82% using the SVM classifier. © 2023 IEEE.

关键词： Sentiment analysis

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Development of a Closed Chamber System for Cultivating Lion's Mane Mushrooms using an Internet of Things System to Increase Yield and Economic Value 10

Development of a Closed Chamber System for Cultivating Lion'...

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10th International Conference on Digital Arts, Media and technology and 8th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Suntornmeth, Pakorn Homjun, Kanitha Fankam-Ai, Worawit Karnjanapiboon, Charnyut Sanguansri, Atigorn Deenoi, Athiphong Kaewsirirung, Sirilux Rajamangala University of Technology Lanna Nan Faculty of Science and Agricultural Technology Department of Computer Science Nan Thailand Rajamangala University of Technology Lanna Nan Faculty of Engineering Department of Electrical Engineering Nan Thailand

ISBN: (纸本)9798331543273

关键词： Sustainable development

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Integrating IoT and Machine Learning for Real-Time Patient Health Monitoring with Sensor Networks 4

Integrating IoT and Machine Learning for Real-Time Patient H...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Vimal, S.P. Vadivel, M. Baskar, V. Vijaya Sivakumar, V.G. Srinivasan, C. Sri Ramakrishna Engineering College Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Vidya Jyothi Institute of Technology Department of Electronics and Communication Engineering Telangana Hyderabad India School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350300888

An innovative approach for continuous health monitoring in medical applications is presented in this research. The proposed system is composed of Raspberry Pi, cloud storage, machine learning, and IoT sensor. The IoT sensor monitors patients' vitals in real time and quickly identifies any anomalies. The patient wearing the sensors transmit the real-time data with Raspberry Pi processors. The Raspberry Pi collects the real time data from sensors such temperature, blood pressure, heart rate, and pulse oximeter. Then the IoT transmits the data collected to a cloud server. K-Nearest Neighbors (KNN) is a data processing and analysis method used in the cloud server. The KNN algorithm categorizes and analyzes the data collected, discovers the trend and anomalies present in the patient's vital signs. The proposed system has a simple user interface that can be accessed via a web or mobile application, allowing doctors and nurses to remotely look at the patient's data and generate real-time alerts in case of severe health situations. While cloud technology ensures scalability, data storage, and advanced analytics, the integration of Raspberry Pi devices makes it possible to process data locally and reduce latency. © 2023 IEEE.

关键词： Internet of things

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