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12th International Conference on Computer communication and Informatics, ICCCI 2022

作者： Charitha, C. Chaitrasree, Amuluru Devi Varma, Penmetsa Chidananda Lakshmi, C. B.Tech Students in Electronics and Communication Engineering Sastra Deemed to Be University Thanjavur613401 India Faculty in School of Electrical and Electronics Engineering Sastra Deemed to Be University Thanjavur613401 India

ISBN: (纸本)9781665480352

Non-Insulin Dependent Diabetes Mellitus or Type2 Diabetes is one of the critical diseases and many people are suffering from it. Every year, approximately 2 to 5 million people are losing their lives as Diabetics. If Diabetes is predicted earlier, it can be controlled and also, deadly risks such as diabetes cardiac stroke, nephropathy and other disorders associated with it can be prevented. Therefore, early prediction of diabetes helps in maintaining good health. With the recent development in machine learning (ML), it is being applied to various aspects of the medical health. The Pima Indian Diabetes data set (PID), which was used in this paper, was acquired from the UCI repository. In this study, after undergoing a thorough data pre-processing and Feature engineering with feature importance models like Random Forest Importance and RFE, we used many Machine Learning models such as KNN, Logistic Regression, SVM, Random Forest, LightGbM and XGboost for train-test splits like 60-40, 70-30 and 80-20 to predict Type-II diabetes mellitus. Among all models, the highest accuracy is obtained as 91.47% from lightGbM model for 80-20 train test split. © 2022 IEEE.

关键词： Decision trees

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Prediction Of Used Car Prices Using Artificial Neural Networks And Machine Learning 12

Prediction Of Used Car Prices Using Artificial Neural Networ...

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12th International Conference on Computer communication and Informatics, ICCCI 2022

作者： Varshitha, Janke Jahnavi, K. Lakshmi, C. Sastra Deemed University B.Tech Students in Electronics and Communication Engineering Thanjavur613401 India Sastra Deemed University Faculty in School of Electrical and Electronics Engineering Thanjavur613401 India

ISBN: (纸本)9781665480352

With the extensive growth in usage of cars, the newly produced cars are unable to reach the customers for various reasons like high prices, less availability, financial incapability, and so on. Hence the used car market is escalated across the globe but in India, the used car market is in a very nascent stage and mostly dominated by the unorganized sector. This gives chance for fraud while buying a used car. Hence a high precision model is required which will estimate the price of an used car with none bias towards customer or *** this model, A Supervised learning-based Artificial Neural Network model and Random Forest Machine Learning model are developed which can learn from the car dataset provided to it. This project presents a working model for used car price prediction with a low error value. A considerable number of distinct attributes are examined for reliable and accurate predictions. The results obtained agree with theoretical predictions and have shown improvement over models which use simple linear models. An ANN (Artificial Neural Network) is built by using Keras Regression algorithm namely Keras Regressor and other Machine Learning Algorithms namely Random Forest, Lasso, Ridge, Linear regressions are built. These algorithms are tested with the car dataset. Experimental results have shown that the Random Forest model with a Mean Absolute Error value of 1.0970472 and R2 error value of 0.772584 has given the less error among all the other algorithms. The work presented here has shown profound implications for future studies of Used Cars price Prediction using Random Forest and might one day help to solve the problem of fraudswith one hundred percent accuracy. © 2022 IEEE.

关键词： Neural networks

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4X5 Dynamic Random Access Memory Stores Data Using Digital Circuits

4X5 Dynamic Random Access Memory Stores Data Using Digital C...

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International Conference on Signal Processing, communication, Power and Embedded System (SCOPES)

作者： Dalli Tushar N M Reddy B.Tech in Electronics and Communication Engineering Vignan's Institute Of Information Technology Visakhapatnam India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

This paper mainly focuses on building a 4X5 DRAM using logic circuits from transistor logic to devices. It concentrates on building a DRAM using a transistor that consumes less power, less area and proper functionality. Designing a DRAM is modeled using Cadence Virtuoso from schematics to layout. In schematics the cells are constructed using transistors, the cells are 2X4 decoder, 2X1 multiplexer, 1-bIT storage, read and write controller, 3 XOR gates and tristate buffer. All the cells are connected according the 4X5 DRAM architecture which is further discussed in the paper.

关键词： Multiplexing Logic circuits buildings Random access memory Computer architecture Logic gates Signal processing Decoding Transistors Servers

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Implementation and Comparison of Direct mapped and 4-way Set Associative mapped Cache Controller in VHDL 8

Implementation and Comparison of Direct mapped and 4-way Set...

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8th International Conference on Signal Processing and Integrated Networks, SPIN 2021

作者： Kaur, Gurjit Arora, Riyanshi Panchal, Sakshi Shailesh Department of Electronics and Communication Engineering Delhi Technological University New Delhi India B.Tech Electronics and Communication Engineering Delhi Technological University New Delhi India

ISBN: (纸本)9781665435642

This paper presents the implementation of design of a non-pipelined processor that generates memory read/write requests to a direct-mapped and a 4 - way set associative mapped cache controller frequently and examines their performances by calculating Cache hits versus misses. The paper also briefly explains cache memory, the Least Recently Used (LRU) algorithm, cache controller, memory mapping techniques. Following that, a cache controller based on Finite State Machine has been implemented for direct-mapped as well as Four-way set-associative synchronous Cache (buffer) memory of 256 byteswhose size of the block is 128 bits or 16 bytes (by default). A multi-banked interleaved main memory system comprising of 4memory banks of IK byte each has been designed and used. The implementation is done using VHDL, and the simulation has been performed using Eda playground, an online simulation tool. The maximum clock frequency is 110 MHz. © 2021 IEEE

关键词： Mapping

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Precise Payload Delivery via Unmanned Aerial Vehicles: An Approach Using Object Detection Algorithms

arXiv

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arXiv 2023年

作者： Vadduri, Aditya benjwal, Anagh Pai, Abhishek Quadros, Elkan Kammar, Aniruddh Uday, Prajwal B.Tech Mechatronics Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India B.Tech Electronics & Communication Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India B.Tech Mechanical Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

Recent years have seen tremendous advancements in the area of autonomous payload delivery via unmanned aerial vehicles, or drones. However, most of these works involve delivering the payload at a predetermined location using its GPS coordinates. by relying on GPS coordinates for navigation, the precision of payload delivery is restricted to the accuracy of the GPS network and the availability and strength of the GPS connection, which may be severely restricted by the weather condition at the time and place of operation. In this work we describe the development of a micro-class UAV and propose a novel navigation method that improves the accuracy of conventional navigation methods by incorporating a deep-learning-based computer vision approach to identify and precisely align the UAV with a target marked at the payload delivery position. This proposed method achieves a 500% increase in average horizontal precision over conventional GPS-based approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Lung Cancer Detecting using Radiomics Features and Machine Learning Algorithm

Lung Cancer Detecting using Radiomics Features and Machine L...

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technological Advancements in Computational Sciences (ICTACS), International Conference on

作者： M.N. Rajesh Nikita Tanni Z. Tanveer baig Electronics and Communication Engineering Jain (Deemed-to be-University) Bengaluru India B.Tech in Computer Science and Engineering (Data Science) Christ (Deemed to be University) Bengaluru India Dept of IT Amity University Taskent Uzbekistan

Lung Cancer Incidence across the globe is the second leading cancer type tallying to about 2,206,771 during 2020 and is estimated to rise to about 3,503,378 by 2040 for both male and female sexes and for all ages accounting to 11.4% as per Globocan 2020 [1]. It is the leading death-causing cancer. Lung Cancer [2] in broad terms encompasses Trachea, bronchus as well as lungs. Purpose: The study is aimed to understand Radiomics based approach in the identification as well as classification of CT Images with Lung Cancer when Machine Learning (ML) algorithms are applied. Method: CT Image from LIDC-IDRI [4] Dataset has been chosen. CT Image Dataset was balanced and image features by PyRadiomics library were collected. Various ML features classification algorithms are utilized to create models and matrices adopted in judging their accuracies. The models, distinctive capacity is assessed by receiver operating characteristics (ROC) analysis. Result: The Accuracy scores and ROC-AUC values obtained for various Classification Model are as follows, for Ada boosting, the accuracy score was 0.9993 ROC-AUC was 0.9993 and followed by GbM, the accuracy score was 0.9993, was 0.9992. Conclusion: Extracting texture parameters on CT images as well as linking the Radiomics method with ML would categorize Lung Cancer commendably.

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Novel VPDU based framework in V2X communication for Internet of vehicles (IoV) and performance evaluation of VAN in urban scenario 5

Novel VPDU based framework in V2X communication for Internet...

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5th International Conference on communication and electronics Systems, ICCES 2020

作者： Mishra, Ayaskanta Chaki, Soubhik Sadhukhan, Sukanya Sinha, Sounak Students B.Tech [Electronics & Telecommunication Engineering School of Electronics Engineering Kalinga Institute of Industrial Technology Bhubaneswar751024 India

ISBN: (纸本)9781728153711

Automobile industry is rapidly growing with new technologies. One of such prominent technologies is communication and network connectivity. New era smart vehicles are coming equipped with network adaptors for data connectivity. Modern smart vehicles are aided with hundreds or sensors and manufactures are working on making all sensors and driving statistics to make available to centralized cloud servers for different applications using IoT and cloud technologies making Internet of Vehicles (IoV). Even manufactures are working on some advance features like actuator control and vehicle control and telemetry using data network infrastructure. This work is focused on a proposed novel vehicular protocol data unit (VPDU) and its complete layered network stack implementation framework for vehicle to everythi ng (V2X) communication in an urban scenario. For validation of network framework and evaluation of performance matrices, the simulated vehicular area network (VAN) is proposed in an urban scenario using complete network stack and terrain data in QualNet® simulator. Two industry standard MAC protocols as IEEE 802.11a and 802.11p. AODV, bellman-Ford and OS PFv2 are used as routing protocols to analyze the network performance for all test cases. IEEE 802.11p MAC shows significant performance improvements from 802.11a counterparts with the proposed novel VPDU in a typical urban VAN scenario. The IEEE 802.11p MAC outperforms 802.11a with 11% less overall End-to-End delay, 29% overall less average jitter and overall 127% increase in packet delivery ratio (PDR). AODV routing protocol performs the best with minimum latency and packet error by improving PDR. The detailed performance analysis is presented in the scope of the paper. © 2020 IEEE.

关键词： Network layers

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VLSI implementation using fully connected neural networks for energy consumption over neurons

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SUSTAINAbLE ENERGY techNOLOGIES AND ASSESSMENTS 2022年第PartA期52卷

作者： Mehbodniya, Abolfazl Kumar, Ravi Mohanty, Sachi Nandan Geetha, A. bedi, Pradeep Tripathi, Rohit [a]Department of Electronics and Communications Engineering Kuwait College of Science and Technology (KCST) Doha Area 7th Ring Road Kuwait [b]Department of Electronics and Communication Engineering Jaypee University of Engineering and Technology Guna Madhya Pradesh India [c]Department of ECE Saveetha School of Engineering SIMATS Thadalam Chennai - 602105 Tamil Nadu India [d]Department of Computer Science & Engineering Vardhaman College of Engineering (Autonomous) Hyderabad India [e]B.Tech Department of ECE Indian Institute of Space Science and Technology Thiruvananthapuram 695547 Kerala India [f]Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Kattankulathur Tamil Nadu 603203 India

Increasingly, the capacity of artificial neural networks to identify and express top-level concepts in sets of data has become quite significant. Deep learning models like fully connected layer machine learning have demonstrated outstanding results in range of identification and verification applications. It's technology deployments suffered as from complexity and huge silicone areas with energy consumption. Memory accesses, the bulk from which happens in fully connected systems, comprise the energy usage of machine learning. It has many of the depths of neural network. Throughout this work, researchers suggest densely connected networks by demonstrating that there is indeed a drop in the number of links inside fully connected systems of up to 90% enhancing the quality of three popular sets of data. To decrease the memory needs have suggested low-connected grid networks, researchers present perhaps an effective hardware implementation based on the linear response registers. In comparison to the traditional design of fully connected layer neural network models, the suggested design could save up to 90 percent of storage. Findings of execution further reveal that artificial neurons of suggested weak network connections have a power consumption decrease up to 84 percent compared to one single atom of fully connected layer machine learning.

关键词： Neural Network Deep learning Energy consumption VLSI

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Ambient Parametric Monitoring of Farms Using Embedded IoT LoRa

Ambient Parametric Monitoring of Farms Using Embedded IoT Lo...

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2019 IEEE bombay Section Signature Conference, IbSSC 2019

作者： Raj, Tejaswi Johny, Thoms A Khetawat, Suyash Rajeshwari, b. Prasad, Sathya PES University B.Tech Department of Electronics Communication Engineering Bengaluru560085 India

ISBN: (纸本)9781538674017

This paper presents the development of a cost effective, self-sustainable and solar rechargeable smart farming product based on embedded IoT and providing end to end solution using the cutting edge LoRa technology. It portrays the innovative methods developed to monitor ambient parameters using the LoRa LPWAN. The product leverages the capabilities of the Arduino Mini MCU capitalizing the low power and ultra-low power optimization techniques to enhance self-sustainability of the device. It also provides an insight of the alternative, creative approaches developed for some soil sensors (NPK and pH) to make the product cost-effective. These real time ambient data parametric trends can be monitored over the cloud at user's convenience on an android application. © 2019 IEEE.

关键词： Cost effectiveness

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Channel allocation in cognitive radio networks using energy detection technique

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Materials Today: Proceedings 2020年 33卷 934-938页

作者： Deepak Kumar Nayak Arjuna Muduli Mohammad Tahir Hussain Asgar Ali Mirza Jagadeesh Reddy Gummadipudi Nalluri Sravan Kumar Department of ECE Koneru Lakshmaiah Education Foundation K L University Vaddeswaram Guntur District India Final Year B.Tech in Electronics & Communication Engineering in Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur District India

Cognitive radio (CR) is a modern and innovative technology that seeks to solve this issue of bandwidth depletion by increasing the use of the mobile cellular connectivity bandwidth. The distribution and re-allocation of channels is a crucial aspect of cellular mobile networks (CMNs) to maximize the utilization of the CMS. This work reviews the transmitter based cognitive radio (CR) techniques and analysis them based on signal to noise ratio . This paper also proposes a complete model for energy detection. The analysis and simulation are carried out in Matlab by taking bPSK and QPSK modulation. This energy detection has the advantages as no prior knowledge of the primary consumer is needed and this system's computational cost is very small.

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