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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mishra, Abhinav Sahoo, Girija Shankar Sekaran, Ramesh Umamaheswari, B. Futane, Pravin R. Choudhary, Sanjay Chitkara University Institute of Engineering and technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi University of Information Technology Maharishi School of Engineering and Technology UttarPradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Ramnagar District562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Technology Department of Information Technology Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India

ISBN: (纸本)9798350370249

Comfy Shell is an open-source protocol used for many years to comfy community logins and communication over the net. It is primarily based on public key cryptography and is well known for being one of the maximum cozy strategies of authentication and communication. It is now being explored as a Wi-Fi network security device for defensive information transmitted over WiFi networks, including those typically determined in cafes, airports, and other public locations. Its safety capabilities, which include robust authentication and encryption, make it an excellent desire to securely access assets, shift information, and connect to far-off structures. Moreover, it can be used as a digital private network (VPN) to safely establish connections between two or extra computers in a public community. At the same time as SSH is properly-appropriate for relaxed far-off connections, it can be configured to provide additional ranges of authentication and encryption to ensure the WiFi network's safety from malicious assaults. This paper will explore the capability of SSH as a Wi-Fi network safety tool, discuss diverse implementations, and focus on the particular components of using SSH as a security device for WiFi networks. © 2024 IEEE.

关键词： Wi-Fi

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Multicast Routing Technique for Augmenting Routing Efficiency in Mobile WSN 7

Multicast Routing Technique for Augmenting Routing Efficienc...

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Anand, K. Rajkumar, M. Srikanth, M.S. Radhika, S. Department of Computer Science and Engineering Rajalakshmi Engineering College Tamil Nadu Chennai India School of Information Technology and Engineering VIT University Tamil Nadu Vellore India TNS India Foundation Karnataka Bangalore India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

ISBN: (纸本)9781665464086

Mobile Wireless Sensor Network (MWSN) is an active network completely self-governing from a fixed structure. Also, MWSN has small channel bandwidth, great node movement, and inadequate battery energy. In WSN, flooding raises the reliability regarding successful communication of a packet with greater overhead. The flooding consumes the network's resources quickly, especially in terms of the lifetime of the node and energy, etc. Unicast routing creates an extra overhead and increases energy consumption because unicast routing mainly focuses on only minimum distance. But, multicasting is used when the desired destination is at maximum distance and increases the network's reliability in terms of throughput. This paper's objectives are to develop an efficient and reliable protocol using multicasting and unicasting to overcome the issue of higher overhead due to flooding. Multicast Routing Technique for Augmenting (MRTA) routing efficiency in MWSN is introduced. This method aims to enhance the link stability of the route for raising the route lifetime and diminishing the routing load. This method discovers the stable path from several ways to decline the overhead and delay between the sender and receiver. Simulation results demonstrate that this approach improves the network lifetime increases the throughput, and minimizes the routing load in the MWSN. © 2023 IEEE.

关键词： Multicasting

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Flood Prediction Using Ensemble Machine Learning Model 5

Flood Prediction Using Ensemble Machine Learning Model

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5th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2023

作者： Rahman, Tanvir Asif Syeed, Miah Mohammad Farzana, Maisha Namir, Ishadie Ishrar, Ipshita Nushra, Meherin Hossain Khan, Bhoktear Mahbub University of Delaware Newark Department of Computer and Information Sciences DE United States Brac University Department of Computer Science and Engineering Dhaka Bangladesh University of Delaware Department of Department of Geography & Spatial Sciences Newark United States

ISBN: (纸本)9798350337525

India experiences recurrent natural disasters in the form of floods, which result in substantial destruction of both human life and property. Accurately predicting the onset and progression of floods in real-time is crucial for minimizing their impact. This research paper focuses on a comparative study of various machine learning models for flood prediction in India. The evaluated models include K-Nearest Neighbor (KNN), Support Vector Classifier (SVC), Decision tree Classifier, Binary Logistic Regression, and Stacked Generalization (Stacking). We used a dataset of rainfall to train and test the models. Our results indicate that the stacked generalization model outperforms the other models, achieving an accuracy of 93.3% and Standard Deviation of 0.098. Our findings suggest that machine learning models can provide accurate and timely flood predictions, enabling disaster management authorities to take appropriate measures to minimize damage and save lives. © 2023 IEEE.

关键词： Decision trees

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Image cyberbullying detection and recognition using transfer deep machine learning

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 14-26页

作者： Almomani, Ammar Nahar, Khalid Alauthman, Mohammad Al-Betar, Mohammed Azmi Yaseen, Qussai Gupta, Brij B. School of Computing Skyline University College University City of Sharjah P.O. Box 1797 Sharjah United Arab Emirates I.T. department Al-Huson University College AlBalqa Applied University Irbid Jordan Computer Science Department Faculty of I.T. Yarmouk University Irbid Jordan Department of Information Security Faculty of Information Technology University of Petra Amman Jordan Ajman University Ajman United Arab Emirates Department of Computer Science and Information Engineering Asia University Taichung413 Taiwan Symbiosis International University Pune India Department of Electrical and Computer Engineering Lebanese American University Beirut1102 Lebanon Dehradun India

Cyberbullying detection on social media platforms is increasingly important, necessitating robust computational methods. Current approaches, while promising, have not fully leveraged the combined strengths of deep learning and traditional machine learning for enhanced performance. Moreover, online content complexity requires models that can capture nuanced contexts beyond text, which many current methods lack. This research proposes a novel hybrid approach using deep learning models as feature extractors and machine learning classifiers to improve cyberbullying detection. Extracting features using pre-trained deep learning models like InceptionV3, ResNet50, and VGG16, then feeding them into classifiers like Logistic Regression and Support Vector Machines, enhances understanding of the complex contexts where cyberbullying occurs. Experiments on an image dataset showed that combining deep learning and machine learning achieved higher accuracy than using either approach alone. This novel framework bridges the gap in existing literature and contributes to broader efforts to combat cyberbullying through more nuanced, context-aware detection methods. The hybrid technique demonstrates the potential of blending deep learning's representation learning strengths with machine learning's sample efficiency and interpretability. © 2023

关键词： Learning systems

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A System for Real-Time Shoulder Angle Measurement Using computer Vision 4

A System for Real-Time Shoulder Angle Measurement Using Comp...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Langote, Meher Saratkar, Saniya Puri, Chetan Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India Datta Meghe Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India Datta Meghe Higher Education and Research Faculty of Engineering and Technology Department of Computer Science Design Maharashtra Wardha India

ISBN: (纸本)9798331529635

Recently, angle measurements have been performed using a goniometer, but the complex motion of shoulder movement has made these measurements intricate. The angle of rotation of the shoulder is particularly difficult to measure from an upright position because of the complicated base and moving axes. This article presents a real-time shoulder angle measurement system utilizing computer vision and machine learning techniques, specifically designed for the rehabilitation of frozen shoulder and related conditions. The system leverages MediaPipe, an open-source framework, to capture and analyze shoulder movements accurately. With its modular architecture, the system processes video feeds in real time, detecting key body points and calculating shoulder angles, thereby providing immediate feedback to patients and therapists. The study evaluates the system's effectiveness in clinical and home-based environments, highlighting its potential to enhance physiotherapy by enabling precise, data-driven treatment plans. This research study also identifies current research gaps, proposes solutions, and explores future enhancements, including environmental adaptation, mobile integration, and the extension to other joints. This study has attempted to estimate the shoulder joint internal/external rotation angle using the combination of pose estimation artificial intelligence (AI), computer vision, and a machine learning mode. © 2024 IEEE.

关键词： Goniometers

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Multi-Context Recommendation Systems (CARS) in Autonomous Driving and Other Applications

Multi-Context Recommendation Systems (CARS) in Autonomous Dr...

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2022 International Telecommunications Conference, ITC-Egypt 2022

作者： Ali, Abdullahi Kashef, Rasha Ibrahim, Ahmed Fakhry Toronto Metropolitan University Electrical Computer and Biomedical Engineering Faculty of Engineering and Architectural Science TorontoON Canada Boston University Computer Science Department United States

ISBN: (数字)9781665488082

ISBN: (纸本)9781665488082

Recommendation systems (RS) are important for their instantaneous ability to suggest to users their desired items and for ensuring a smooth and worthwhile user experience. To optimize the user's experience with the RS, it can be programmed to be aware of the various contexts the user has while interacting with the system. Context-aware recommendation systems (CARS) aim to add more nuance to the process of predicting items by considering different factors such as location and time. This paper presents the foundations of recommendation systems, such as categories, evaluation metrics, datasets, and challenges. In this paper, we show the effectiveness of multi-contexts in autonomous driving. Three CARS models are developed and tested. Experimental results show that adding multi-contexts provides more personalized options for drivers, allowing intelligent decisions. © 2022 IEEE.

关键词： Recommender systems

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Exploring the Impact of Machine Learning on Real-Time Data Analysis 3

Exploring the Impact of Machine Learning on Real-Time Data A...

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

作者： Singh, Swati Nanda, R.T. Sathya, S. Banchhor, Chitrakant O. Pandey, Pratik Rampal, Sourav Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Basic Science Karnataka Ramnagar India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350319125

The emergence of system mastering has enabled actual-time information analysis to come to be extra accurate, efficient, and comfy. ML can rapidly examine and classify excessive-volume streams of raw records, allowing companies to reply to quickly changing environments. This technology has had a considerable impact on the way groups and developers manner complicated datasets and make decisions in realtime. It has revolutionized how agencies use and combine information to gain new insights and understand trends. ML performs a vital position in helping businesses efficiently technique large quantities of data while reducing human errors. At the same time, it provides security and integrity of the data, lowering the capacity for change and loss. In this paper, we discover the effect of ML on actual-time information evaluation and talk about its diverse applications. We additionally offer an overview of different methods of ML and their advantages and disadvantages. ultimately, we speak of future traits for mixing ML with actual-time records analytics and the consequences of this integration. © 2023 IEEE.

关键词： disadvantages environments organizations revolutionized understand

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BlazePose-Based Action Recognition with Feature Selection Using Stochastic Fractal Search Guided Whale Optimization

BlazePose-Based Action Recognition with Feature Selection Us...

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2023 International Conference on Control, Automation and Diagnosis, ICCAD 2023

作者： Alsawadi, Motasem S. Sandoval-Gastelum, Marcial Danish, Irfan Rio, Miguel University College London Department of Electronic and Electrical Engineering London United Kingdom Laxenburg Austria FAST - National University of Computer and Emerging Sciences Department of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350347074

The BlazePose, which models human body skeletons as spatiotemporal graphs, has achieved fantastic performance in skeleton-based action identification. A Spatial-Temporal Graph Convolutional Network can then be used to forecast the actions. This architecture performance can be improved by simply replacing the skeleton input data with a different set of joints that provide more information about the activity of interest. On the other hand, existing approaches require the user to manually set the graph's topology and then fix it across all input layers and samples. This research shows how to use Stochastic Fractal Search - Guided Whale Optimization Algorithm in conjunction with the BlazePose skeletal data to construct a novel implementation of this topology for action recognition. We utilized the NTU-RGB+D and the Kinetics datasets as benchmarks in our experiments. © 2023 IEEE.

关键词： Fractals

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Betta Fish Image Identification using Feature Extraction GLCM and K-Nearest Neighbour Classification

Betta Fish Image Identification using Feature Extraction GLC...

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2022 International Conference on Information Technology Research and Innovation, ICITRI 2022

作者： Abidin, Zaenal Rusliyawati Permata Ariany, Fenty Solehudin, Ilham Junaidi, Akmal Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandarlampung Indonesia Universitas Lampung Department of Computer Science Bandarlampung Indonesia

ISBN: (数字)9781665461849

ISBN: (纸本)9781665461849

Freshwater fish known as bettas have their natural habitats in a number of Southeast Asian nations, including Thailand, Malaysia, Brunei Darussalam, Singapore, Vietnam, and Indonesia. In addition to having a distinctive appearance and personality, this fish is aggressive while defending its area. The Betta Fish is distinctive in its size, pattern, and body color. It is known that there are 73 species of Betta Fish. The main reasons for carrying out this study were due to the limitations in recognizing Betta Fish with the human eye and to build an initial model for the Betta Fish pattern recognition application. The fish data set consists of 300 with limited species: 60 Halfmoon Fancy, 60 Hell boy, 60 Red Koi Galaxy, 60 Solid Blue, and 60 Yellow Koi Galaxy. This study uses three schemes between training data and testing data: scheme 1 consist of training data 75% and testing data 25%, scheme 2 training data 80% and testing data 20%, scheme 3 training data 85% and testing data 15%. In the pre-processing stage, the scaling, segmentation and gray scale methods are carried out. Characteristics of fish features were obtained through the gray level co-occurence matrix (GLCM) method with angular direction and the K-NN method was used for classification with values of k = 1, 2, 3, 4, 5, 6, 7. The results of this study show in Scheme 1 the highest accuracy is 92% with k = 5 and angular direction is 135 and error rate = 8%, scheme 2 has the highest accuracy 95% with k = 7 and angular direction is 135 and error rate = 5%, scheme 3 has the highest accuracy 100% with k = 2 and the angular direction is 180 and error rate is 0%. © 2022 IEEE.

关键词： Fish

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FSD: Fully-Specialized Detector via Neural Architecture Search 5

FSD: Fully-Specialized Detector via Neural Architecture Sear...

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5th International Conference on computer Communication and the Internet, ICCCI 2023

作者： Huang, Zhe Li, Yudian University of Wisconsin-Madison Department of Computer Sciences MadisonWI United States University of California Computer Science & Engineering Department La Jolla San DiegoCA United States

ISBN: (纸本)9798350326956

Most generic object detectors are mainly built for 'standard' object detection tasks such as COCO [1] and PAS-CAL VOC [2]. They might not work well and/or efficiently on tasks of other domains consisting of images that are visually different from standard datasets. To this end, many advances have been focused on adapting a general-purposed object detector with limited domain-specific designs. However, designing a successful task-specific detector requires extraneous manual experiments and parameter tuning through trial and error. In this paper, we first propose and examine a fully-automatic pipeline to design a fully-specialized detector (FSD) which mainly incorporates a neural-architectural-searched model by exploring ideal network structures over the backbone and task-specific head. On the DeepLesion dataset, extensive results show that FSD can achieve 3.1 mAP gain while using approximately 40% fewer parameters on binary lesion detection task and improved the mAP by around 10% on multi-type lesion detection task via our region-aware graph modeling compared with existing general-purposed medical lesion detection networks. © 2023 IEEE.

关键词： Object detection

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