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3rd IEEE Asian Conference on Innovation in technology, ASIANCON 2023

作者： More, Priyanka Rathi, Snehal Thatte, Harshvardhan Bhalchandra Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350302288

NS2 (Network Simulator 2) is a Linux software which is used to create wired and wireless network simulations. Using NS2, we have created wired and wireless virtual networks with up to 100 nodes and simulated them by sending packets through its nodes. For generating network simulations in TCL language, we have used NSG (Network Scenario Generator). To analyse the different characteristics of the network like-Average throughput, Instant throughput, Residual energy and Packet Delivery Ratio;we have used AWK scripts. Lastly we have implemented different routing protocols on the same wireless network and compared their key characteristics. © 2023 IEEE.

关键词： Routing protocols

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Federated Learning in the Era of Privacy Preservation: A Comprehensive Review and Future Directions 2

Federated Learning in the Era of Privacy Preservation: A Com...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Kulshreshtha, Kushagra Goel, Bhawna Kanyal, Hoshiyar Singh Singhal, Vikas Gla University Department of Computer Engineering & Applications UP Mathura India UP Ghaziabad India Abes Institute of Technology Department of Computer Science & Engineering UP Ghaziabad India Greater Noida Institute of Technology Department of Information Technology UP Greater Noida India

ISBN: (纸本)9798350371055

Federated studying is a disbursed system learning method that lets a set of dispensed users, or clients, collaboratively learn a shared predictive model without sharing any statistics. Through taking part in the model education method, every purchaser contributes to the development of a worldwide, predictive model without sacrificing its very own information privacy. This privateness renovation may be achieved through various techniques, which include differential privateness, cozy multi-celebration computation, and information obfuscation. In this way, model schooling isn't always a task exclusively for centralized facts creditors, but as an alternative, a large-scale collaboration amongst multiple entities with disparate hobbies and facts silos. In the technology of privacy protection, federated studying represents a powerful tool that enables the construction of predictive fashions that might be knowledgeable by means of various information resources at the same time as retaining the integrity and privateness of the individual statistics units. © 2024 IEEE.

关键词： Collaboratively Construction Federated Privateness technology

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Forecasting with Fuzzy Time Series and Variation 2nd

Forecasting with Fuzzy Time Series and Variation

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2nd International Conference on Signal and Data Processing, ICSDP 2022

作者： Biswas, Tamal Bhattacharya, Diptendu Dutta, Kumardeep Mandal, Gouranga Department of Computer Science and Engineering National Institute of Technology Agartala India

ISBN: (纸本)9789819914098

In this article, a new technique has been introduced to predict a single stock value which is taken from the Stock Exchange of Taiwan with the concept of fuzzy time series to stabilize the root mean square error value. In the proposed method discussed in this paper, closing price indices from the Stock Exchange of Taiwan had been taken as input. First, the preserved data set of the stock exchange has been fuzzified. Secondly, the variation for daily input data had been computed by taking the midpoint of the fuzzified interval of the same trading day (input data and fuzzified interval should be of the same trading day). Then, each trading day variation is fuzzified by defining its Universe of discourse and intervals. Finally, by taking the fuzzified closing price Index table and fuzzified variation table we calculated the forecasted value of the next day’s stock exchange closing price Index. For financial forecasting, dealing with portfolios is a major issue, especially for a single stock. Here the proposed technique is tested with a single stock taken from the Taiwan stock exchange and the results are observed. Predicting the future value with high accuracy is very important for managing single stock. This proposed method gives the constant RMSE and better-fore-casted value than other methods. Here, RMSE is constant and will not fluctuate much and due to less fluctuation in RMSE, it becomes more important for future prediction. It has also been perceived that the RMSE value for the big data set is also giving improved results for all other prevailing models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Time series

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Revolutionizing Urban Waste Management: Cutting-Edge Image Processing for Precise Solid Waste Classification 5

Revolutionizing Urban Waste Management: Cutting-Edge Image P...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Rama Krishna, S. Patan, Muzeeb Khan Sudarsan, J.S. Padmapriya, R. Bhagavathiperumal, S. Balaram, Allam Department of Computer Science & Engineering Hyderabad502329 India College of Engineering and Technology University of Technology and Applied Sciences Department of EEE Muscat Oman India Sathyabama Institute of Science and Technology Department of Civil Engineering Chennai India Sri Krishna College of Engineering and Technology Coimbatore India MLR Institute of Technology Department of Computer Science and Engineering Hyderabad500043 India

ISBN: (纸本)9798331530013

Due to rapid urbanization and population growth, urban waste management has become one of the major challenges faced by cities around the world. Solid waste classification and sorting are necessary for intensifying recycling, safeguarding environment and manage additional landfilling of materials that are reusable or recyclable, and are getting in landfill of solid waste in certain extent. In this study, an original ensemble model using the VGG19 and EfficientNet architectures is proposed to classify solid waste in urban area. This proposed model was applied on a dataset consists of 2,527 number of images in to six classes to increase the accuracy of classification as well as to maximize the efficiency of waste. It preprocesses the incoming data using various techniques such as image resizing, normalization and augmentation to make the dataset more diverse and robust. The ensemble mechanism performed better combining the detailed feature extraction skills of VGG19 and versatile and scalable architecture of EfficientNet. On test set, it outperformed individual models and the accuracy for the proposed model was 98.75% which closely followed, previously obtain the best result. © 2024 IEEE.

关键词： Population statistics

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Augmented Reality Indoor Navigation with Smart Appliance control

Augmented Reality Indoor Navigation with Smart Appliance con...

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2023 IEEE International Conference on Modelling, Simulation and Intelligent Computing, MoSICom 2023

作者： Bide, Pramod Sharma, Nishtha Sheikh, Danish Baranwal, Amaresh Sardar Patel Institute of Technology Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350393415

This proposed work explores the application of augmented reality (AR) for indoor navigation in emergency situations and large spaces using Unity NavMesh and the A∗ algorithm. It provides real-Time information about college class-rooms and enhances accessibility for people with disabilities. The proposed system utilizes Vuforia and Unity to construct the AR environment, integrating QR code scanning using the ZXING SDK, as well as the smart control of light bulbs, fans, and other appliances through ESP32, relay modules, and the Blynk API. Experimental results demonstrate the effectiveness of our approach in improving navigation accuracy and accessibility for users. Furthermore, the system's real-Time updates, user-friendly interface, and remote appliance control offer a versatile solution for various indoor environments. The proposed work presents a detailed explanation of the literature review, the operating architecture, discusses its social impact, limitations, and future scope, highlighting its potential to revolutionize indoor navigation and accessibility for all. © 2023 IEEE.

关键词： Augmented reality

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Street Smart: An Intelligent Traffic Management System 10

Street Smart: An Intelligent Traffic Management System

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Hegde, Adwait Patil, Ojas Belurgikar, Soham Godbole, Anand Sardar Patel Institute of Technology Department of Computer Engineering Mumbai India

ISBN: (纸本)9789380544472

Traffic congestion is a critical issue not only in all major cities but also in many rural areas of India. One of the most dangerous side effects of traffic congestion is the unnecessary delay of emergency vehicles. There are several approaches to managing traffic, including wireless sensor networks, inductive loop detection, video data processing, and infrared sensors which are successful strategies for intelligent traffic management. However, the issue with these systems is that they are incredibly expensive to install, maintain, and take a long time to implement. In this paper, we propose a novel method of traffic management along with pedestrian crossing that uses Fuzzy Logic based on lane density which is sensitive to emergency vehicle in traffic. This method, when used in conjunction with the current signalling system, can be the answer to effective real-time traffic control. When compared to current techniques of reducing traffic congestion, this new plan will be easier to implement and cost less. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Fuzzy logic

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Mandala as computational art: Vectorization and beyond 14

Mandala as computational art: Vectorization and beyond

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14th Indian Conference on computer Vision, Graphics and Image Processing, ICVGIP 2023

作者： Sarkar, Tusita Bhowmick, Partha Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India

ISBN: (纸本)9798400716256

This paper introduces a novel technique of computational art with mandala-an iconic heritage of Indian folk art. Its novelty lies in several fundamental steps. The first one is fixing the asymmetries and the imperfections in a hand-drawn piece of art based on the notion of a primitive map. The primitive map is described using a novel concept of geometric salience-a set of well-defined salient points on the frontier polygon of a primitive-characterizing the concavities and convexities present in the primitive. The primitive map is also used for the vectorization of a mandala and its succinct representation as a mandala sector graph (MSG), which eventually results in efficient graph operations on an existing artwork to create a new piece of art. The use of frontier polygons in different steps of the algorithm makes it robust and efficient. Experimental results on various datasets demonstrate the potential and versatility of the proposed technique. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Arts computing

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A Clustering-Based 3D Localization in Wireless Sensor Networks Using RSSI and AoA

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SN computer Science 2023年第5期4卷 1-23页

作者： Wajgi, Dipak W. Tembhurne, Jitendra V. Department of Computer Science and Engineering Indian Institute of Information Technology Maharashtra Nagpur 441108 India Department of Computer Engineering St. Vincent Pallotti College of Engineering and Technology Maharashtra Nagpur 441108 India

Localization in wireless sensor network (WSN) is an important issue since it helps to find the origin of the event. Many localization algorithms have been proposed and are efficient up to certain extent. Energy efficient and low cost 3D localization algorithm in wireless sensor networks is still a big challenge. Accuracy of location estimation with minimum computational complexity is the main objective of the localization algorithm. In this paper, we propose the clustering-based localization algorithm for 3D environment which is energy efficient and has less computational complexity. The sensor nodes are grouped in a cluster which is based on the received signal strength at the respective anchor nodes. The nearest neighbor clustering algorithm is formulated for forming the clusters. The anchor nodes act as the cluster heads. The distance information from received signal strength indicator (RSSI) along with the angle of arrival (AoA) information is combined to estimate the location of the cluster members creating the local map. The energy dissipation in the proposed approach can be reduced by adopting the density control strategy. The simulation results show that the proposed approach performs better as compared to the PSO, BBO, and FA in terms of localization error. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Angle of arrival Clustering Density control Energy efficiency Localization RSSI WSN

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Ensemble Machine Learning for Better Crime Detection and Prevention 3

Ensemble Machine Learning for Better Crime Detection and Pre...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Dhanwanth, Batini Roshan, R Allen Bhargavi, C.H. Vidhya Shri, G. Raja, S. Panimalar Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343632

Ensuring public safety is a significant concern that necessitates the need to develop effective strategies to identify and apprehend criminals. Machine learning has gained increased research attention in the field of law enforcement due to its unique ability to improve crime detection. This study investigates crime detection through ensemble machine learning, emphasizing the benefits of integrating multiple models. Techniques such as Random Forest, Gradient Boosting, and Ada-Boost are employed to develop precise crime detection and high-crime prediction models. Utilizing historical crime statistics, socioeconomic characteristics, geographical information, and temporal patterns, this study has constructed a comprehensive dataset. Further, the Ensemble models are trained and evaluated on this dataset to enhance the capability of recognizing and categorizing illegal actions. Moreover, this study addresses feature engineering and model hyperparameter tuning by optimizing crime detection ensemble models. Finally, the results indicate that ensemble machine learning algorithms consistently outperform other existing models, providing more reliable insights into crime detection. This research study illustrates how ensemble models can enhance public safety and optimize resource allocation in real-world crime prevention scenarios, assessing their practical effects. © 2023 IEEE.

关键词： Ada-Boost Crime Detection Crime Hotspots ensemble learning Gradient Boosting Random Forest Socio-economic Indicators

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Diabetic Foot Ulcer Detection and Classification using ConvMixer Model 5

Diabetic Foot Ulcer Detection and Classification using ConvM...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Anusha, N. Vidhya, R. Jani Anbarasi, L. Dhanya, D. Pushparani, S. Mohan, Geetha Vellore Institute of Technology School of Computer Science and Engineering Department of IoT Vellore Campus Vellore India Agni College of Technology Department of Information Technology Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India Mar Ephraem College of Engineering and Technology Department of Artificial Intelligence and Data Science Elavuvilai India Meenakshi College of Engineering Department of Cse Chennai India Institute of Applied Technology Computer Science Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331504403

Diabetic foot ulcer (DFUs) are a common and significant consequence of diabetes, frequently resulting in severe illness and amputations if not promptly identified and treated. The growing occurrence of DFUs is a substantial obstacle to healthcare globally. Precise and prompt categorization of DFUs is essential for efficient treatment and avoidance of consequences. This research analyzed ConvMixer model for comprehensive classification designed to accurately evaluate DFU. The DFU images are preprocessed using the watershed algorithm that helps in separating different regions of an image, such as the ulcer and surrounding tissues, to improve the neural network's ability to extract high-quality information. The framework utilizes depthwise and pointwise convolutions to mix spatial and channel-wise information in an efficient manner to effectively extract pertinent information from various clinical DFU dataset. The proposed work features a multi-layered depthwise and pointwise convolutional architecture, created especially to manage the complex patterns and variances found in various diabetic foot ulcer stages and variants (DFUs). By integrating these convolutions and skip associations, the network facilitates hierarchical extraction of features and robust representation of features. Experimental results on a substantial and varied DFU dataset demonstrate that the ConvMixer model achieves an impressive accuracy rate of 97.16%, with a precision, F1-score and a sensitivity of 97%, 97% and 99.08% respectively. © 2024 IEEE.

关键词： Diseases

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