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Vulnerability Scanning and Analysis Tool (VSAT) 9

Vulnerability Scanning and Analysis Tool (VSAT)

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9th International Conference on Smart Computing and Communications, ICSCC 2023

作者： Nair, Aarya Anil, Abhinav Pallivathuckal, Anna Rose Daiju, Dijo John, Jomina Rajagiri School of Engineering and Technology Dept. of Computer Science and Engineering Kochi India

ISBN: (纸本)9798350314090

Due to exponential technological advancement, the number of cyber attacks has been steadily increasing and leading to substantial loss of profits, productivity losses, reputational damage as well as judicial liability. To ensure that cyber attacks do not occur, it is important to identify vulnerabilities in web applications. This paper presents a vulnerability scanning tool that involves continuous monitoring and testing of Web applications which analyses the various vulnerabilities that are present in a system. Different types of scans that are performed to detect these vulnerabilities are also explained. © 2023 IEEE.

关键词： Network security

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Beamforming and Phased Arrays: Elevating MIMO-OFDM Systems for Next-Generation Wireless Networks

Beamforming and Phased Arrays: Elevating MIMO-OFDM Systems f...

引用

2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Yadav, Satya Prakash Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350378092

This study explores the optimisation of Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems through the use of phased arrays and advanced beamforming methods. With a focus on overcoming the challenges that come with 5G wireless communication, like high data rate needs, interference resistance, and efficient spectrum usage, this work presents a novel approach that combines dynamic beam steering and adaptive precoding to significantly enhance system performance. Through thorough modelling, we demonstrate considerable improvements in bit error rates (BER), throughput, and spectrum efficiency compared to traditional MIMO-OFDM configurations. The results demonstrate a 25% improvement in throughput and a 40% reduction in BER in densely populated metropolitan environments, underscoring the potential of phased arrays and beamforming to further improve 5G network performance. This work not only validates the efficacy of the proposed enhancements but also establishes a foundation for future advancements in wireless communication systems by highlighting the critical roles that phased arrays and beamforming will play in achieving the maximum potential of 5G technology. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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Sign Language Identification using Skeletal Point-based Spatio-Temporal Recurrent Neural Network 9

Sign Language Identification using Skeletal Point-based Spat...

引用

9th International Conference on Smart Computing and Communications, ICSCC 2023

作者： Johnson, Jans Joseph, Jisha Reji, Maris George, Megha Elisa Sajanraj, T.D. Rajagiri School of Engineering and Technology Dept. of Computer Science and Engineering Kochi India

ISBN: (纸本)9798350314090

Sign language is a visual language that serves as the primary means of communication in the non-hearing community. It is essential to research and develop sign language translation methods to enable smooth communication between the non-hearing and the hearing communities. The non-hearing community faces several challenges that hinder their interaction with the general public, such as the lack of knowledge of sign language and the availability of interpreters. A Sign Language Converter would thus be an important tool in breaking the communication barrier between the non-hearing and hearing communities. The paper aims to build a model that converts Indian Sign Language into corresponding words. We plan on using a skeletal-point feature extraction framework to identify hand landmarks from sequences containing distinct signs and use these landmarks to build a model for recognizing hand gestures using various Long Short-Term Memory (LSTM) Networks. [1] This approach can produce an accurate result compared to the traditional approach. The dataset is made by the co-authors of this paper due to the insufficiency of pre-existing ISL datasets. The user will be monitored and using the machine learning techniques discussed above, which will perform the real-time translation to display the final result. © 2023 IEEE.

关键词： Audition

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Leveraging XAI and Breakthrough Machine Learning Techniques for Trigeminal Neuralgia Severity Classification

Leveraging XAI and Breakthrough Machine Learning Techniques ...

引用

2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Abhijna, M.B. Akul, P. Kodipalli, Ashwini Global Academy of Technology Dept. of Information Science and Engineering India Global Academy of Technology Dept. of Computer Science and Engineering India Global Academy of Technology Dept. of Artificial Intelligence and Data Science India

ISBN: (纸本)9798350364866

Trigeminal Neuralgia (TN) is a debilitating chronic pain disorder that significantly diminishes overall well-being, making diagnosis and therapy more challenging. The quick and precise categorization of TN severity is critical to therapy effectiveness. To assess the severity of TN, this study makes use of advanced machine learning techniques and explainable AI (XAI) approaches. We use a range of bagging and boosting strategies, including AdaBoost, Random Forest, Decision Tree, K-Nearest Neighbours (KNN), Gradient Boosting, and Logistic Regression. Random Forest was 95.2% accurate, whereas AdaBoost and Gradient Boosting were 92% and 97.2% in terms of their accuracy. The Decision Tree classifier obtained an accuracy of 84.62%, while K-Nearest Neighbours and Logistic Regression also achieved 92.31% accuracy. F1- score, precision, and recall metrics were used to assess performance on a dataset that included patient demographics and medical histories. XAI techniques, such as LIME and SHAP values, were used to improve the models' readability and transparency, which helped to progress the creation of cutting-edge diagnostic tools and increase clinician and researcher confidence. © 2024 IEEE.

关键词： Logistic regression

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An Efficient Lightweight Cryptosystem Using Substitution-Shuffling Based Key Generation 26

An Efficient Lightweight Cryptosystem Using Substitution-Shu...

引用

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

作者： Provat, Al-Anher Haque, Md. Mokammel Chittagong University of Engineering and Technology Dept. of Computer Science and Engineering Chattogram Bangladesh

ISBN: (纸本)9798350359015

In recent years, there has been a meteoric rise in the use of small computing devices, including RFID (Radio Frequency Identification) tags, wireless sensors, embedded electronics, and IoT (Internet of Things) gadgets. Given that these gadgets handle highly sensitive data, their protection should be a top priority. Therefore, it is crucial to implement a secure encryption system on susceptible devices. Standard encryption algorithms like RSA and AES are resource-intensive, require substantial memory, and can slow down devices. In this work, we introduce a lightweight cryptosystem that is suitable for low-powered devices without compromising security. This system utilizes Substitution-Permutation network, incorporating a variation of the Feistel design, to establish a secure symmetric key block cipher. As an enhancement over the standard technique, the proposed algorithm requires less run-time while still providing sufficient protection to maintain an avalanche effect of nearly 50%. © 2023 IEEE.

关键词： Block ciphers GFN IoT Knuth shuffle MATLAB S-box SPN

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Wildfire Spread Prediction using Machine Learning Algorithms 14

Wildfire Spread Prediction using Machine Learning Algorithms

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Singh, Aman Yadav, Rakesh Sudhamshu, Gadug Basnet, Aryan Ali, Rahmat University Faculty of Engineering and Technology Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335095

Wildfires are one of the most destructive natural disasters that cause significant harm to both humans and the environment. Predicting their spread is critical for disaster management and preparedness. In this study, we have utilized machine learning algorithms, including Decision Tree Regression, XG Boost Regression, and Artificial Neural Networks, to predict the spread of wildfires using the Next Day Wildfire dataset. The dataset includes satellite images, weather, and geography conditions aggregated across the United States from 2012 to 2020. We preprocessed and engineered the dataset which includes the features such as elevation, wind direction and speed, temperature, humidity, precipitation, drought index, vegetation index, energy release component, and population density. We evaluated the models using the Root Mean Squared Error (RMSE) metric and found that the Decision Tree Regression algorithm performed the best with the lowest RMSE score. Our study highlights the potential of machine learning algorithms in predicting the spread of wildfires, which can aid in better disaster management and preparedness efforts. © 2023 IEEE.

关键词： Forecasting

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Emotion Classification to Augment the Emotional Intelligence of Autistic Individuals 5

Emotion Classification to Augment the Emotional Intelligence...

引用

5th IEEE International Conference on Communication, Computing and Industry 6.0, C2I6 2024

作者： Guruvammal, S. Kesavamoorthy, R. Anju, M.I. Chellatamilan, T. Jegatha Deborah, L. Jain University Dept. of Cse Kanakapura India Cmr Institute of Technology Dept. of Cse Bengaluru India Srm Institute of Science and Technology Dept. of Ece Chennai India Vellore Institute of Technology School of Computer Science and Engineering Vellore India University College of Engineering Computer Science and Engineering Tindivanam India

ISBN: (数字)9798350379884

ISBN: (纸本)9798350379884

The collaboration of machine learning (ML) algorithms and cognitive psychological frameworks supports the enhancement of emotional intelligence. In light of Plutchik's emotional wheel, this study investigates how to identify human emotions. By utilizing 24 different emotions, 8 high-intensive (secondary) emotions are identified. We employed 9 different classification algorithms K-nearest neighbors (KNN), decision trees (DT), naïve bayes (NB), support vector machine (SVM), Logistic regression, random forest (RF), artificial neural network (ANN), gradient boosting machines (GBM), and bagging classifier (BC) to identify the emotions accurately and the performance of them are compared. This research uses the labelled dataset consisting of 11 columns as similar word 1, similar word 2, typical sensation 1, typical sensation 2, opposite intensity low, opposite intensity medium, opposite intensity high, tells, helps, emotion and cluster. © 2024 IEEE.

关键词： Random forests

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Comparative Analysis of Feature Selection Techniques and Machine Learning Classifiers for Accurate Classification of Pumpkin Seeds 2

Comparative Analysis of Feature Selection Techniques and Mac...

引用

2nd International Conference on Information and Communication technology for Sustainable Development, ICICT4SD 2023

作者： Siddique, Md Abu Ismail Haque, Fariha Shojol, Md. Shojeb Hossain Rajshahi University of Engineering and Technology Dept. of Electrical and Computer Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350358667

Pumpkin seeds are widely consumed due to their nutritional content and potential health advantages. Accurately categorizing pumpkin seeds based on their diversity and distinct physical properties, such as size, compactness, roundness, and others, is crucial in agriculture, food processing, and research. In this work, two approaches were used to categorize a dataset of pumpkin seeds and establish their relative performance. The first method used a typical machine learning methodology, whereas the second used a filter-based feature selection technique. The filter-based technique used principal component analysis (PCA) and recursive feature elimination (RFE) to extract the most significant features for identifying two varieties of pumpkin seeds. Machine learning classifiers were then used to assess the performance of each strategy. Machine learning classifiers were then applied to evaluate the performance of the approach where Random Forest performed best among all others and achieved 91.80% validation accuracy. On the other hand for the conventional machine learning classifier method, six classifiers were used to evaluate the performance of this approach where the XgBoost classifier performed the best and achieved 95% as training accuracy and 94.87% as validation accuracy. © 2023 IEEE.

关键词： Feature Selection

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Optimal Switching Oscillation Damper of SiC-MOSFET 4

Optimal Switching Oscillation Damper of SiC-MOSFET

引用

4th IEEE Global Conference for Advancement in technology, GCAT 2023

作者： Sajedul Islam, Md. Tabassum, Fariya Dept. of Electrical & Computer Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (纸本)9798350305258

Wide bandgap (WBG) semiconductor devices namely SiC-MOSFET enable high-frequency power converters, which have benefits over many other switching devices in terms of power density and dynamic performance. However, switching oscillation is a common problem for this class of devices, and it has a lot of unfavorable effects. This work examines the underlying causes of the switching oscillation, its effects on the system, and modeling analysis to derive control mechanisms to prevent undesirable outcomes. This work discusses the underlying causes of the switching oscillation, which are: (1) di/dt or/and dv/dt -induced damped oscillation in the presence of parasitic factors;(2) undamped oscillation brought on by the switch's negative resistance;(3) effect of the negative capacitance phenomena brought on by the semiconductor devices' physical orientation. An effective control approach is proposed in this research work to inhibit the switching oscillation of SiC-MOSFET. The proposed integrator-based damper takes into account the turn on switching oscillation. The performance of the proposed damper is compared with the integral and proportional-integral-derivative (PID) controller for justifying its effectiveness. © 2023 IEEE.

关键词： Control System Power Converter Semiconductor WBG Device

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DMDA: A Computational Resource Allocation Approach for IoT Devices in Fog Computing 4

DMDA: A Computational Resource Allocation Approach for IoT D...

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4th International Conference on Intelligent Cybernetics technology and Applications, ICICyTA 2024

作者： Pramono, Luthfan Hadi Shen, Shan-Hsiang National Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798331506490

Fog Computing enables the efficient offloading of computational tasks from IoT Devices to Fog nodes, enhancing processing efficiency and reducing latency. This paper introduces the Dynamic Matching with Deferred Acceptance (DMDA) algorithm, designed to optimize the allocation of GPU and CPU resources within Fog Computing environments, specifically for smart city applications. The DMDA algorithm allocates resources to IoT Devices based on parameters such as data type, computational load, priority, and responsiveness. Devices processing image or video data are given priority for GPU allocation, while other devices are assigned to CPUs based on their cumulative performance scores. Devices with lower priority are reassigned to CPU resources or excluded when resource availability is exhausted, significantly enhancing performance under high-demand scenarios. The algorithm employs a Mixed-Integer Linear Programming (MILP) model to maximize the aggregate score of allocated devices while adhering to resource constraints. Experiments conducted with 50 to 1,000 devices demonstrate that both the total Device Score rate and Allocated Device rate reach 100%, indicating optimal allocation. Empirical findings show that the DMDA algorithm improves resource utilization, reduces processing times, and ensures effective workload distribution across large-scale IoT deployments. © 2024 IEEE.

关键词： Fog computing

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