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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Kant, Ravi Sharma, Shashank Vikas, Vikas Chaudhary, Sachin Jain, Animesh Kumar Sharma, Kewal Krishan Iimt Engineering College Computer Science and Engineering Department Meerut India Iimt University School of Computer Science & Applications Meerut India

ISBN: (纸本)9798350338027

Implementation of IoT domain invites tremendous attacking opportunities which demands end to - end security mechanism. Applications related to the domain of IoT varies from critical applications to normal business-oriented applications like Intelligent Transportation Systems, Smart Grid, Video Surveillance, Banking, Logistics, Insurance etc. A special support in terms of security must be required for critical applications as well as normal business applications. Large amount of security mechanisms implemented time to time. Blockchain technology proves to be useful in providing security to several applications based on IoT by following defense - in - depth or castle approach. Blockchain can be defined as a database for storing processed data in a sequential manner. Such data has been shared among participating users. Information can be stored on a public ledger which can't be updated. Every device in the corresponding network must retain the same ledger. Through the medium of this paper, we try to highlight importance of Blockchain Mechanism in IoT environment and also point out that the concept of Blockchain mechanism is only concerned with security. © 2023 IEEE.

关键词： Blockchain

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Cyber Threat Intelligence (CTI): An Analysis on the Use of Artificial Intelligence and Machine Learning to Identify Cyber Hazards

Cyber Threat Intelligence (CTI): An Analysis on the Use of A...

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International Conference on Recent Developments in Cyber Security, ReDCySec 2023

作者： Kant, Neelima Amrita Department of Computer Science and Engineering School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida201306 India Department of Computer Science and Engineering School of Engineering and Technology Center for Cyber Security and Cryptology Sharda University Uttar Pradesh Greater Noida201306 India

ISBN: (纸本)9789819998104

Data transport volume and scope on networks are growing daily due to the quick advancements in network technology. It is challenging for cybersecurity specialists to keep track of every action taking place on the network because of the constantly growing density of networks. This circumstance has led to an increase in the complexity and intensity of threats and attacks. It is harder to detect and identify irregularities in network activities because of frequent and sophisticated cyberattacks. A well-crafted cybersecurity strategy now includes cyber threat intelligence (CTI), which is a crucial foundation. Automating the detection of cyberattacks as well as speedy attack type analysis and predication are all made possible by machine learning (ML), which offers a number of tools and techniques. The strategies for using machine learning (ML) to identify assaults are discussed in this article. Threat intelligence can help security teams defend against a constantly evolving threat environment before, during, and after an attack if used properly. By analyzing attackers and comprehending their tactics and goals, groups may create cyber defenses that are more effective, delicate, and resilient. However, due to two significant flaws, its usefulness is still in question. First, current methods are unable to detect unknown Indicator of Compromise (IoC), and second, they are unable to automatically produce categorized CTIs which renders CTI sharing. As a result, the objective of this paper is to present a complete analysis of cyber threat identification using intelligent techniques. Additionally, we covered the issues and solutions related to machine learning applications utilized in network assaults. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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Lettuce Crop Yield Prediction Analysis using Random Forest Regression Machine Learning Model in Aeroponics System 2

Lettuce Crop Yield Prediction Analysis using Random Forest R...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Rajendiran, Gowtham Rethnaraj, Jebakumar Srm Institute of Science and Technology School of Computing College of Engineering and Technology Department of Computing Technologies Tamil Nadu Chengalpattu India

ISBN: (纸本)9798350325799

Aeroponics is a type of soilless agriculture where plants are grown in a misted environment without the use of the soil. The process of yield prediction in aeroponics system for the lettuce crop is one of the key challenges because the crop yield is affected by variety of growth factors such as temperature, humidity, light and nutrient levels. Hence, to predict the lettuce crop yield in aeroponics, the machine learning models were utilized and these models can be trained on the historical data on crop yields and environmental variables. Here, in this work, the Random Forest (RF) regression is chosen as the machine learning model for yield prediction. The data used in this work were collected from the aeroponic lettuce farming tower. The purpose of the research work is to investigate the potential of the random forest model for efficient prediction of the lettuce crop yield in aeroponics system. The RF model can produce accurate prediction results of 92% which is better than the other regression algorithms in terms of forecasting lettuce crop production. Comparing this to the MAE of other regression models, which may be anywhere from 0.098 to 3.76 of the average reported yield, and it is observed that RF has a much more acceptable MAE. Hence, this work provides valuable information for farmers who are interested in utilizing this type of indoor farming to produce the high-yield of lettuce crops. © 2023 IEEE.

关键词： Lettuce

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IoT-Based Water Quality Monitoring and Detection System 1

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2nd International Conference on Emerging Trends in Expert Applications and Security, ICETEAS 2023

作者： Kanchana, M. Gopirajan, P.V. Sureshkumar, K. Sudharsanan, R. Suganthi, N. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai603203 India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai603203 India Department of Information Technology Saveetha Engineering College Chennai602105 India Department of Information Technology SRM Institute of Science and Technology Ramapuram Chennai600089 India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai600089 India

ISBN: (数字)9789819919093

ISBN: (纸本)9789819919086

This paper suggests Internet of things (IoT) and Arduino-based tele-water quality monitoring systems integrated with the web application. The most critical component of the Wireless Sensor Network includes a microcontroller for processing the circuit's sensor data, a communication system between and within node communication, and a few other sensors such as pH, Microbial Fuel Cell (MFC), humidity, and temperature. All data is collected at the Arduino (data which is sensed by the sensors) by using remote monitoring and Internet of things technology (ESP8622MOD). The real-time data is transmitted, and that data is displayed by sending through the cloud in a human interface format on a server device (pc/mobile) by the web or mobile applications. The main objective is to get a water monitoring system with high mobility (the ability to check water more freely, the ability to check wherever required), high frequency, and low power (as it is compatible, we can take and check quickly). Therefore, our main objective is that our system will immensely help people with small water reservoirs, water mines, and villagers/ framers with fields to become conscious/aware of polluted water and stop contaminating the water. The deciding parameters of water, whether toxic or drinkable, are decided by the potential level of hydrogen value. If the hydrogen value is less than five, it is acidic, and if the hydrogen value is above 7.5, then it is primary. The MFC sensor, if the analogy sensor, senses any electricity from the cathode and anode ends (0–200 indicates acidic water, 200–300 is normal water, and above 300 is essential to water);by this water, we decide whether water toxic in nature and temperature and humidity also plays a role in determining the quality, it can result in an effect on the level of O2. Also, the capacity of organisms to resist certain pollutants, so different conditions should have different water temperatures. © 2023, The Author(s), under exclusive license to Spr

关键词： Internet of things

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The Low-Attenuation Endfire Leaky-Wave State on an Optically Transparent Lossy Film

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engineering 2024年第12期43卷 72-80页

作者： Ziheng Zhou Yongjian Zhang Yilin Zheng Ke Chen Yueming Gao Yuehe Ge Yue Li Yijun Feng Zhizhang(David)Chen College of Physics and Information Engineering Fuzhou UniversityFuzhou 350108China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China School of Electronic and Information Engineering Soochow UniversitySoochow 215006China School of Electronic Science and Engineering Nanjing UniversityNanjing 210023China Department of Electrical and Computer Engineering Dalhousie UniversityHalifaxNS B3H 4R2Canada

The development of high-performance optically transparent radio frequency(RF)radiators is limited by the intrinsic loss issue of transparent conductive films(TCFs).Instead of pursuing expensive endeavors to improve the TCFs'electrical properties,this study introduces an innovative approach that leverages leaky-wave mode manipulation to mitigate the TCFs'attenuating effect and maximize the RF *** finding reveals that the precise control of the mode confinement on glass-coated TCFs can create a low-attenuation window for leaky-wave propagation,where the total attenuation caused by TCF dissipation and wave leakage is effectively *** observed low-attenuation leaky-wave state on lossy TCFs originates from the delicate balance between wave leakage and TCF dissipation,attained at a particular glass cladding *** leveraging the substantially extended radiation aperture achieved under suppressed wave attenuation,this study develops an optically transparent antenna with an enhanced endfire realized gain exceeding 15 dBi and a radiation efficiency of 66%,which is validated to offer competitive transmission performance for advancing ubiquitous wireless communication and sensing applications.

关键词： Antennas Endfire radiation Low attenuation Material losses Transparent conductive films

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Lip Synchronization Model For Sinhala Language Using Machine Learning 4

Lip Synchronization Model For Sinhala Language Using Machine...

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4th International Conference on Advanced Research in computing, ICARC 2024

作者： Ranaweera, Dilani Weerasinghe, Ruwan Dinalankara, Randima Univeristy of Colombo School of Computing University of Colombo Colombo Sri Lanka University of Sri Jayewardenepura Faculty of Engineering Department of Computer Engineering Colombo Sri Lanka

ISBN: (纸本)9798350384864

Realistic lip-synchronized animations can be produced by the appropriately timed voice and lip motions of the cartoon character. This process is called as 'lip synchronization'. Building a talking face for languages such as English, Korean, and Portuguese has been subject of numerous studies. Compared to other languages, Sinhala is a low-resource language due to less contribution in these researches. This research study focuses to build a frontal view of a synthetic mouth part with smooth lip movements rather than opening and closing while speaking Sinhala sentences. The most difficult challenge is to match the basic sounds, 'phonemes' with the lip movement called 'visemes'. This study has been used static viseme approach by deriving twenty three (23) Sinhala viseme classes and a deep learning model has been developed to map visemes with Sinhala letters. In system implementation, text input is provided first, after which the system produces audio and the deep learning model generates a collection of visemes based on the given text. The system interface then offers three options for playing the visemes at various speeds, including fast, normal, and slow. The user interface was created in Python, and the deep learning model is integrated into the system. This model will be very helpful to use in cartoon industry to build Sinhala speaking cartoon characters and also be used to train deaf people to read lips. © 2024 IEEE.

关键词： Python

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Security Challenges in Natural Language Processing Models

Security Challenges in Natural Language Processing Models

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2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023

作者： Xu, Qiongkai He, Xuanli School of Computing and Information System The University of Melbourne Australia Department of Computer Science University College London United Kingdom School of Computing FSE Macquarie University Australia

Large-scale natural language processing models have been developed and integrated into numerous applications, given the advantage of their remarkable performance. Nonetheless, the security concerns associated with these models prevent the widespread adoption of these black-box machine learning models. In this tutorial, we will dive into three emerging security issues in NLP research, i.e., backdoor attacks, private data leakage, and imitation attacks. These threats will be introduced in accordance with their threatening usage scenarios, attack methodologies, and defense technologies. © 2023 Association for Computational Linguistics.

关键词： Natural language processing systems

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Groundwater Level Forecasting using Spatio-temporal Graph Convolutional Networks (GCNs) and XGBoost 6

Groundwater Level Forecasting using Spatio-temporal Graph Co...

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6th International Conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Naga Satish, G. Jadala, Vijaya Chandra Sravani, Kolisetti Giridhara Allam, Sangeetha Shanmuga Sundari, M. Thada, Jyothirmai BVRIT HYDERABAD College of Engineering for Women Hyderabad India Department of Computer Science and Artificial Intelligence School of Computer Science and Artificial Intelligence SR University Warangal506371 India Department of AIML Gokaraju Rangaraju Institute of Engineering And Technology Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

ISBN: (纸本)9798331522667

This research analyzes groundwater levels across multiple districts using data from over 100 observation wells in each district. To capture seasonal variations and predict groundwater behavior, this research has developed three models: periodic, polynomial, and rainfall-based. The periodic and polynomial models describe groundwater level fluctuations based on historical well data, while the rainfall model assesses the influence of precipitation on water levels in the wells. In addition, this research explores advanced predictive techniques by incorporating Spatio-temporal Graph Convolutional Networks (GCNs) and XGBoost. These methods enable a more nuanced understanding of spatio-temporal dependencies and improve predictive accuracy by leveraging both spatial relationships among wells and the temporal evolution of groundwater levels. The integration of traditional models with machine learning techniques aims to enhance groundwater management and inform decision-making. © 2025 IEEE.

关键词： Rain

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Animal Collision Detection in Real Time to Prevent Accident using KNN Classifier in comparison with Stochastic Gradient Descent 6

Animal Collision Detection in Real Time to Prevent Accident ...

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6th International Conference on Contemporary computing and Informatics, IC3I 2023

作者： Asritha, K Renu Soundari, A. Gnana Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350304480

This project aims to assess and improve the detection of animal collisions in real-world situations. Materials and Methods: To forecast animal collision detection in real time and avert accidents, KNN and stochastic gradient descent are used with different training and testing splits. Both algorithms which are used in this study underwent 10 iterations, resulting in a total 20 iterations. The sample size for the study was 1347 and was sourced from the kaggle website, with the G Power as 80% (g power setting parameters: α=0.05 and power=0.80). Results: K-Nearest Neighbor (79.82%) has the increased accuracy over Stochastic gradient descent (75.19%) with a significance value of 0.039 (p © 2023 IEEE.

关键词： Animal Migration Animals Collision Machine Learning Model Parameters Novel K-Nearest Neighbor Process Stochastic Gradient Descent

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Securing IoT Systems with AI-Infused Software and Virtual Replica Models 2

Securing IoT Systems with AI-Infused Software and Virtual Re...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Supriya, K. Sangeetha Jeno Lovesum, S.P. Arora, Renuka Bhatia, Rishu Yadwad, Sunita Natrayan, L. Department of Computer Science & Engineering Christ University Bengaluru India Department of Computer Science & Engineering Jagannath University NCR Bahadurgarh India Department of Electronics and Communication Engineering Ganga Institute of Technology and Management Jhajjar India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Vadodara India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai India

ISBN: (纸本)9798331504960

The proliferation of Internet of Things (IoT) devices has introduced significant security challenges due to the increased attack surface and the inherent vulnerabilities of interconnected systems. This paper proposes a novel security framework that integrates Artificial Intelligence (AI)-infused software and Virtual Replica Models, or Digital Twins, to enhance the robustness of IoT systems. The framework leverages AI-driven anomaly detection and predictive analysis to identify potential threats in real-time, while Digital Twins provide a virtual replica of the IoT ecosystem for continuous monitoring and simulation-based threat assessment. By fusing these technologies, the proposed solution enables proactive security measures, ensuring rapid response to cyberattacks and continuous system optimization. Extensive simulations and experimental results demonstrate the system’s ability to reduce attack detection times by 30%, while improving overall system resilience and minimizing false positive rates. This approach provides a scalable and adaptable solution for securing IoT infrastructures in various industries. © 2024 IEEE.

关键词： Risk assessment

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