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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Alghamdi, Mubark Kostanic, Ivica Florida Institute of Technology Department of Electrical Engineering and Computer Science Melbourne United States

ISBN: (纸本)9798350364910

C band spectrum 5030-5091 MHz is allocated for command-and-control communication services with unmanned aircraft systems. This paper evaluates the possibility of using 3GPP 5G standards for provisioning of such services. Channelization of the spectrum and major system parameters are proposed. The performance of the proposed system in channel fading environment is evaluated using MATLAB based simulations. The evaluations examine SINR, and average throughput of the proposed system at different altitudes of the unmanned aircraft system. © 2024 IEEE.

关键词： MATLAB

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Modelling Smart Grid Instability Against Cyber Attacks in SCADA System Networks

Lecture Notes on Data Engineering and Communications Technol...

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Lecture Notes on Data engineering and Communications Technologies 2023年 180卷 239-250页

作者： Efiong, John E. Akinyemi, Bodunde O. Olajubu, Emmanuel A. Ibrahim, Isa A. Aderounmu, Ganiyu A. Degila, Jules Department of Computer Science and Engineering Obafemi Awolowo University Ile-Ife Nigeria Department of Cybersecurity Federal University of Technology Owerri Nigeria Department of Computer Science Institute of Mathematics and Physics University of Abomey Calavi Benin

The SCADA systems in the Smart Grid Network (SGN) are increasingly facing cyber threats and divers attacks due to their known proprietary vulnerabilities, most often leading to power instability and cascading failures in the Grid. This paper associates the SGN’s instability with cyberattacks and models intrusions into SCADA systems using the Decentral Smart Grid Control (DSGC) model on the Stability dataset. The Time-Synchronization Attack was modelled with its effects on Transmission Line Fault Detection, Voltage Stability Monitoring and Event Locating. Classification and prediction were done on WEKA using a number of Machine Learning Algorithms and performances evaluated. Random Forest (RF) classifier was found to be more promising in all the benefit metrics yielding 92.0%, 91.9%, 92.0%, and 91.9% for Accuracy, Precision, Recall and F-measure, respectively. An ensemble classifier – Bagging, performed significantly well after the RF with 90.0%, 89.9%, 90.0% and 89.9%. The ROC Area under Curve also showed RF having 97% and Bagging 96.7%. 100 trees were constructed for the RF while considering 4 random features, and the out-of-bag error was 0.0084. RF had a TPR of 92.0%, which is the highest and an FPR of 0.11. These results provide insights into identifying appropriate ML techniques and models for the Grid’s instability problem vis-à-vis modelling attacks. This is expected to help in effective security planning, threats detection, attack mitigation and risk management. © The Author(s).

关键词： Smart power grids

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Deep learning-based classification,detection,and segmentation of tomato leaf diseases:A state-of-the-art review

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Artificial Intelligence in Agriculture 2025年第2期15卷 192-220页

作者： Aritra Das Fahad Pathan Jamin Rahman Jim Md Mohsin Kabir M.F.Mridha Department of Computer Science and Engineering American International University-BangladeshDhaka 1229Bangladesh Institute of Information and Communication Technology Shahjalal University of Science and TechnologySylhet 3114Bangladesh Advanced Machine Intelligence Research Lab Dhaka 1207Bangladesh Bangladesh University of Business and Technology DhakaBangladesh

The early identification and treatment of tomato leaf diseases are crucial for optimizing plant productivity,efficiency and *** by the farmers poses the risk of inadequate treatments,harming both tomato plants and *** of disease diagnosis is essential,necessitating a swift and accurate response to misdiagnosis for early *** regions are ideal for tomato plants,but there are inherent concerns,such as weather-related *** diseases largely cause financial losses in crop *** slow detection periods of conventional approaches are insufficient for the timely detection of tomato *** learning has emerged as a promising avenue for early disease *** study comprehensively analyzed techniques for classifying and detecting tomato leaf diseases and evaluating their strengths and *** study delves into various diagnostic procedures,including image pre-processing,localization and *** conclusion,applying deep learning algorithms holds great promise for enhancing the accuracy and efficiency of tomato leaf disease diagnosis by offering faster and more effective results.

关键词： Deep learning Tomato leaf disease Classification Detection Segmentation Imaging

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Video Anomaly Detection Based on HSOE-FAST Modified Deep Neural Network

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SN computer science 2024年第5期5卷 1-11页

作者： Gupta, Anil Kumar Sharma, Rupak Ojha, Rudra Pratap Department of Computer Science and Engineering SRM Institute of Science and Technology NCR Campus Modinagar Uttar Pradesh Ghaziabad India Department of Computer Applications SRM Institute of Science and Technology NCR Campus Modinagar Uttar Pradesh Ghaziabad India Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and Management (GLBITM) (Affiliated to AKTU) Uttar Pradesh Greater Noida India

In recent times, video surveillance has become indispensable for public security, leveraging computer vision advancements to analyze and comprehend lengthy video feeds. Anomaly detection and classification stand out as crucial components of this technology. Anomaly detection's primary objective is to swiftly identify irregularities within a given timeframe. It is promising to use Deep Neural Network (DNN) approaches for anomaly detection because they combine the ideas of deep learning and reinforcement learning, enabling artificial agents to learn from and gain insights from real-world data. A modified DNN (Deep Neural Network) technique known as HSOE-FAST (Histo sigmoid of Orientation and Enthalpy with Fast Accelerated Segment Test) was proposed in this study. From the dataset, the input is obtained. Initially, the input is pre-processed using a Gaussian filter followed by the feature extraction using the HSOE-FAST method and finally classification is done using the modified DNN approach. Compared to other approaches, our suggested solution achieves an accuracy of almost 99% while overcoming the shortcomings of the existing methodologies. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： DNN Enthalpy Fast Accelerated Segment Test Histo sigmoid of Orientation Video anomaly detection

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Smart Farming System Using IoT for Efficient Crop Growth 3

Smart Farming System Using IoT for Efficient Crop Growth

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Ganesh, Jetti Venkata Bisht, Saksham Reddy, Shashidhar Babu, A. Mohan SRM Institute of Science and Technology Ramapuram Chennai Department of Electronics and Computer Engineering India

ISBN: (纸本)9798350354171

The Shrewd Cultivating Framework utilizes a combination of sensors and IoT innovation to make a robotized checking and control framework for agrarian purposes. The utilization of sensors like ultrasonic, soil dampness, DHT11, and LDR, interfaces with a microcontroller, empowers real-time information collection on plant development, soil dampness substance, temperature, and light escalated. The system's integration with IoT encourages farther get to screen and oversee these parameters. In case of unfavorable conditions such as moo soil dampness, the framework triggers caution through a buzzer, guaranteeing incite consideration and activity to keep up ideal edit development conditions. This framework presents a arrangement to improve edit surrender by empowering productive and opportune intercession based on exact natural data. © 2024 IEEE.

关键词： Microcontrollers

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IoT-Based Pesticide Detection in Fruits and Vegetables Using Hyperspectral Imaging and Deep Learning 4th

IoT-Based Pesticide Detection in Fruits and Vegetables Usi...

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4th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

作者： Augustin, Anju Kiliroor, Cinu C. Department of Computer Science and Engineering Indian Institute of Information Technology Kottayam Kottayam India

ISBN: (纸本)9783031488870

Fruits and vegetables contain rich nutrients and vitamins. So that they are part of our daily diet. For proper cell growth and health, we need these nutrients. Today in most crops during their growth and post-harvesting preservation different kinds of pesticides were used. Normal usage of such pesticides not that much affects health. But the actual situation is beyond our control. From soil preparation to the post-harvesting stage, pesticides are being added at alarming rates. It affects our health in a harmful way and leads to major health issues. Various studies exist to detect the pesticide levels in fruits and vegetables. This article analyses different existing methods of pesticide detection and examines their features and problems. Through this study, it is understood that Hyperspectral Imaging (HSI) is a very good method, and with it, more accurate results can be obtained by Transfer Learning (few-shot learning). This paper proposes an architecture and algorithm based on HSI and few-shot learning. Future studies are needed in this area to convert an RGB image to a spectral image because the HSI device is very expensive. © 2024, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Nutrients

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Secured Data Storage Using OAuth and Encryption of Data Using AES with Patterns and Hidden Layer

Secured Data Storage Using OAuth and Encryption of Data Usin...

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2024 International Conference on Smart Systems for Electrical, Electronics, Communication and computer engineering, ICSSEEC 2024

作者： Vilasini, V. Hariharan, K. Chezhian, Nethra Kaviyarasu, D. PSG Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350378177

In today's digital era, the use of data is becoming more day by day. Data is serving as a main source for all the applications we rely on. As the volume of data rapidly increases, the need for security also increases in the same phase. This brings the need for securing the data in a more effective manner. The need to protect sensitive data is underscored by the increasing intricacy of cyberattacks and the expansion of virtual commerce. It is imperative to rely on several legal and regulatory requirements for the need for data security. The failure to do so may result in severe penalties. In order to maintain trust in digital interactions, data security during transmission and storage is becoming more important as the world gets smaller. If the data is not secured in an effective manner, the growth of the companies and several other technologies may collapse and result in higher threats to the world. Both firms and governments are required to preserve intellectual property and information related to national security. The availability, confidentiality, and integrity of data are critical for sustaining innovation, trust, and business continuity. This project focusses on the two-level authentication of the system where the users are allowed to store the sensitive data. The first level authentication is provided using OAuth where it is a single sign on mechanism which is advantageous to the users to access it easily. The second level authentication is provided using the encryption technique where the Advanced Encryption Standard is used along with the creative usage of user-generated patterns as a hidden layer in the encryption process. Here a symmetric key is used for the encryption to make it more secure. The two-level authentication brings high level security for the stored data. © 2024 IEEE.

关键词： Data integrity

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Decentralized Noncooperative Games with Coupled Decision-Dependent Distributions 38

Decentralized Noncooperative Games with Coupled Decision-Dep...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yan, Wenjing Cao, Xuanyu Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Distribution variations in machine learning, driven by the dynamic nature of deployment environments, significantly impact the performance of learning models. This paper explores endogenous distribution shifts in learning systems, where deployed models influence environments, which in turn alters the data distributions that the learning models rely on. This phenomenon is formulated by a decision-dependent distribution mapping within the recently introduced framework of performative prediction (PP) (Perdomo et al., 2020). Our study investigates the performative effect in a decentralized noncooperative game, where players aim to minimize private cost functions while simultaneously managing coupled inequality constraints. In this context, we examine two equilibrium concepts for the studied game: performative stable equilibrium (PSE) and Nash equilibrium (NE), and establish sufficient conditions for their existence and uniqueness. Notably, we provide the first upper bound on the distance between the PSE and NE in the literature, which is challenging to evaluate due to the absence of strong convexity on the joint cost function. Furthermore, we develop a decentralized stochastic primal-dual algorithm for efficiently computing the PSE point. By rigorously bounding the performative effect, we prove that the proposed algorithm achieves sublinear convergence rates for both performative regret and constraint violations and maintains the same order of convergence rate as the case without performativity. Numerical experiments further confirm the effectiveness of our algorithm and theoretical results. © 2024 Neural information processing systems foundation. All rights reserved.

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Offline Signature Forgery Detection Based on Geometric Measures Using Tensorflow Model 2

Offline Signature Forgery Detection Based on Geometric Measu...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Lakshmi, A. Anagha Reddy, G. Siddarth Reddy, M. Sowmya Kathirisetty, Nikhila Vardhaman College of Engineering Department of Information Technology Hyderabad India Vardhaman College of Engineering Department of Computer Science and Data Science Hyderabad India

ISBN: (纸本)9798350370188

The most popular method for identifying people from past signatures is through signatures. By using a TensorFlow model which is a deep learning algorithm, we created a new system to verify signatures on bank checks and other important documents. The main point is that a signature plays a crucial role in banking because it is necessary for money withdrawals. In banks there are no efficient systems to check if a signature is real or fake as it is the most frequently used bio-metric method to verify a person's identity. This can lead to bank fraud. The Project will make it easier to tell whether a signature is genuine or not. Online and offline verification are the two methods of verification. We're going to use various geometric measurements to accomplish offline verification. Python libraries were employed in this case. In the testing phase the model's performance is evaluated. To protect the integrity and legitimacy of handwritten signatures, signature forgery detection is an essential responsi-bility. The capacity to recognize forged signatures with accuracy has grown more important as a result of the rise in digital transactions and the growing reliance on electronic documents. The act of duplicating or copying another person's signature with the purpose to deceive or gain illegal access is known as signature forging. Manually spotting these forgeries can be difficult because expert forgers are adept at closely imitating the visual style and traits of real signatures. Therefore, there has been a lot of interest in the development of automated methods and algorithms to identify fake signatures. © 2024 IEEE.

关键词： Data mining

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A Framework on Positioning of Medical Sensors in Healthcare Domain

A Framework on Positioning of Medical Sensors in Healthcare ...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Rajput, Anuj Singh Prabha, S. Khare, Mayank Deep Tula's Institute Department Of Computer Science And Engineering Dehradun India Department Of Computer Science And Information Technology Bangalore India Noida Institute Of Engineering And Technology Department Of Internet Of Things Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350366846

In the last several years, the incorporation of sensor technology into our everyday routines has resulted in improved comfort, convenience, and overall wellbeing. The size and cost of medical sensors have decreased as a consequence of technical improvements that have improved their use. Medical sensors make 'remote health monitoring, surgical procedures, therapy, and rehabilitation' more efficient and practical. We provide an in-depth historical overview of medical sensors during the last fifty years, emphasising their use in clinical settings. The evaluation also examines the impact of IoT on the advancement of sensor technologies and the uses of such technologies in healthcare. We also examine the benefits and drawbacks of several integrated architectures that have been recently proposed for the smooth integration of different medical sensors with novel paradigms and technologies, including those that are located at the edge, in the cloud, in the middle (mobile cloud computing), and in the sky (fog computing) of the network. We conclude by outlining the next challenges that must be overcome in order to fully actualize the best potential and benefits of medical sensor technologies and, in the end, build a robust, scalable, reliable, and affordable healthcare system. © 2024 IEEE.

关键词： Mobile cloud computing

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