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

作者： Patni, Hrithik Jagtap, Akash bhoyar, Vaishali Gupta, Aditya Final Year B.Tech Dept of Electronics and Telecommunication College of Engineering Pune India Adjunct Faculty Dept of Electronics and Telecommunication College of Engineering Pune India

ISBN: (纸本)9781665435642

In recent years, increasing attention is given to the research of the emotions present in speech. Various systems are developed aiming to detect the emotions in the speaker's statements. One of the biggest differences between a machine and a human is understanding the emotions of others and behaving accordingly. Researchers are working on bridging this gap by recognizing emotions in speech or voice. This paper proposes a deep learning-based technique for speech emotion recognition (SER). The SER system is based on various techniques that use distinguished modules for emotion recognition. The model differentiates emotions such as neutral state, happiness, sadness, anger, surprise, etc. The performance of the classification system is based on features extracted and generated models. The features utilized in this include energy, pitch, chromagram, mel-frequency spectrum coefficients (MFCC), and Gammatone frequency spectrum coefficients (GFCC). The emotions are classified using a two dimentional Convolutional Neural Network (CNN). The classification model achieved an overall accuracy of 92.59% on the test data which is comparatively better than the previous algorithm. In future, the intention is to increase the system performance and detect more emotions. © 2021 IEEE

关键词： Emotion Recognition

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Implementing blockchain based Security in EHR Using Ganache

Implementing Blockchain Based Security in EHR Using Ganache

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Kumar Satyam Ayush Sharma Runumi Devi B.Tech in Computer Science and Engineering Galgotias University Greater Noida Uttar Pradesh India Department of Computer Science and Engineering Galgotias University Greater Noida Uttar Pradesh India

A blockchain-based electronic health record (EHR) system has been proposed to manage patient data and maintain computerized records across different healthcare institutions. The main objective of this system is to provide immediate access to patient health information while addressing concerns about privacy and security. It is developed on the Ethereum network using programming languages, tools, and technologies such as Ganache, Solidity, and ***. The system uses smart contracts to provide security and privacy, and transactions are verified and propagated to the entire decentralized network. The system includes a cryptocurrency wallet (MetaMask) with a centralized privacy system that allows authorized parties to access and secure records quickly. The proposed system aims to achieve several objectives such as allowing users to receive the same data simultaneously, increasing efficiency, building trust, and reducing barriers. It secures data storage by restricting user access and enables the secure transfer of patient medical records. The solution allows for better transparency and ownership of sensitive data, which can be recorded and safeguarded via blockchain technology, therefore assisting the healthcare sector. This paper describes a medical record system and a new protocol that can be used quickly and safely, paving the way for more secure and efficient healthcare systems. Overall, the proposed system offers potential solutions to the challenges associated with traditional EHR systems and has the potential to revolutionize the healthcare industry.

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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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Improving Scalability Using blockchain in banking Sector

SSRN

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SSRN 2023年

作者： Krishna, Siva Rama K. Sagar, Vidya K. Subhani, Mahaboob S.K. Department of Computer Science and Engineering Andhra Loyola Institute of Engineering and Technology ALIET Vijayawada India IV B.Tech Department of Computer Science and Engineering Andhra Loyola Institute of Engineering and Technology ALIET JNTUK Vijayawada India

The blockchain is an encrypted database that is distributed across numerous computers and serves as a virtual record of transactions and contracts. This technology supports bitcoin transactions and has already upended the financial industry. Since blockchain technology has the potential to improve data security, provide a decentralized and transparent network, and lower operational costs, the goal of this study is to examine how it can affect the financial industry. blockchain's extraordinary properties make it a potential technology for determining the financial sector's future despite its limits. © 2023, The Authors. All rights reserved.

关键词： blockchain

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Cryptocurrency PricePrediction using LSTM

Cryptocurrency PricePrediction using LSTM

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International Conference on Computing and Networking technology (ICCNT)

作者： Swaraj Deo Gaurav Kumar Swayam Soni Gurmeet Singh B.Tech III Year School of Computing Science and Engineering Galgotias University Greater Noida India School of Computer Science and Engineering Galgotias University Greater Noida India

This paper focuses on utilizing the Long Short-Term Memory (LSTM) algorithm to forecast the prices of cryptocurrencies. that is implemented through the model, which will allow us to keep tabs on the fluctuating prices of the various coins that are available. As various cryptocurrencies are available for trading and investment and they are regarded as a type of digital asset. because trading and investing should be done after the knowledge of the market and trends, So, to solve this problem there should be analysis before trading any pair. Our goal is to present accurate prediction based on the dataset of the selected cryptocurrency. Hence, the proposed model is built and it concentrate mostly on technical analysis of the crypto coins which is the major indicator and most suitable for the prediction models. The proposed LSTM model achieves a variance regression score of 0.95 on test data of the bitcoin which serves as a representative cryptocurrency that is used in the model.

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brain Tumor Detection using MRI Image Processing techniques

Brain Tumor Detection using MRI Image Processing Techniques

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2024 IEEE International Conference on Innovation and Novelty in engineering and technology, INNOVA 2024

作者： Jadhav, Ambaji S. Pawar, Rashmi Hattaraki, Sunilkumar M. bagali, Shwetha Patil, Rajeshwari S. Patil, bhagyashree Department of Elecrtical and Electronics Engineering B.L.D.E. A’s V.P. Dr. P.G.H. College of Engg. and Tech. Vijayapura India Department of Electronics and Communication Engineering B.L.D.E. A’s V.P. Dr. P.G.H. College of Engg. and Tech. Vijayapura India Department of Computer Science and Engineering Sri Krishna Institute of Technology Bengaluru India

ISBN: (纸本)9798331505134

Medical image processing is highly developed and critical in the medical field, encompassing methods like X-rays, MRI, and CT scans. These techniques help to identify minute defects in the human body. brain tumors, which disrupt normal brain function, are detected through MRI using segmentation, feature extraction, and classification, processes that are time-consuming and depend on the expertise of clinical professionals. computer-aided technology addresses these challenges. This work aims to diagnose brain tumors accurately and quickly using MRI images. Image preprocessing is done using CLAHE and median filter to enhance contrast and eliminate noise. Thresholding is used for segmentation and a lightweight U-net is used for tumor segmentation, achieving high accuracy and efficiency without extensive training data. Finally, an MSD-CNN classifies the tumor, capturing both local and global features for better image recognition. Our proposed model outperforms over existing methods in terms of accuracy, robustness, and efficiency in brain tumor detection. © 2024 IEEE.

关键词： Median filters

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Recognition of Handwritten Medical Prescription using CNN bi-LSTM With Lexicon Search

Recognition of Handwritten Medical Prescription using CNN Bi...

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International Conference on Computing and Networking technology (ICCNT)

作者： Aayush Razdan Aditya Raghavan Megh Panandikar Arya Pol Karina Hedaoo Rajesh Patil B.Tech Electronics and Telecommunication Veermata Jijabai Technological Institute Mumbai India Department of Electrical Engineering Veermata Jijabai Technological Institute Mumbai India

Digital technologies have advanced more rapidly than any innovation in the history of the world. The field of Medicine, however, seems to be lagging behind in this regard. A large number of medical prescriptions around the world are still handwritten on paper. A handwriting detection model for medical prescriptions could digitalize written prescriptions for users in a matter of seconds from a simple picture. Moreover, the problems of ambiguity and confusion in deciphering a doctor's handwriting would be solved. Handwritten Text Recognition (HTR) is implemented fundamentally using a combination of Convoluted Neural Networks (CNN) and Recurrent Neural Networks (RNN) on the popular IAM dataset of written words. This paper presents multiple improvements to the current approach by introducing real-life written prescription data as the input, as well as tuning hyperparameters for more accurate results. Lexicon Search has been implemented as a decoding algorithm that checks output predictions against a vast database of drug names used in India. This makes the model work better specifically for medical prescription data in comparison to basic models.

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Real Time Data based Automated Resume Classification and Job Matching using SVC, Jaccard Index and Cosine Similarity

Real Time Data based Automated Resume Classification and Job...

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International Conference on computer Applications In Electrical engineering-Recent Advances (CERA)

作者： Maitri Dipak Amin Aishwarya Chandrashekhar Harkare Nitya Singh Parmar Ruchika Rajesh Wadhwa Rajesh A. Patil B.Tech Electronics & Telccommunications Engineering Veermata Jijabai Technological Institute Mumbai Inida Dept. of Electrical Engineering Veermata Jijabai Technological Institute Mumbai Inida

With ever increasing demand for online recruitment processes, traditional hiring processes have taken a leap into an automated hiring procedure. Document classification is fundamentally implemented using Convolution Neural Networks (CNN), Deep Learning, Natural Language Processing (NLP) etc. This paper presents improvement to the current approach by introducing real-time data scraped from LinkedIn. In previous papers, either cosine similarity or Jaccard coefficient were used. However, in this paper both cosine similarity and Jaccard index are used to get most optimized result. Moreover, the accuracy of this paper is more than the previous papers. This paper proposes a NLP based automated approach for resume classification primarily for technical roles using SVC algorithm and resume matching with a given job description using text distance and token based similarity. The machine learning model is then trained using this data and a comparative analysis of various algorithms is done using accuracy, precision, recall and F1 score to get the best suitable classification algorithm.

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IoT-Genomics Analysis for Personal Health with Cybergenomics and Cloud technology 10

IoT-Genomics Analysis for Personal Health with Cybergenomics...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Prabakaran, T. babu, A. Ramesh Hemalatha, S. Ramasamy, Jayabharathi Kovarasan, Rajesh Kambattan Elango, b. Joginpally B.R. Engineering College Department of Computer Science and Engineering Telangana Hyderabad India Vidya Vikas Institute of Engineering and Technology School of Computer Applications Karnataka Mysore India Tagore Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

Recent innovations at the convergence of genomics and the Internet of Things (IoT) have opened the way for an innovative strategy for administering individualized health care. This research study combines the potential of wearable health monitoring technologies with genetic insights to analyze the personal healthcare. A comprehensive knowledge of health dynamics may be achieved by seamlessly integrating real-time health data collected from IoT-enabled wearables with individual genetic profiles. Using algorithms for machine learning, connections between genetic differences and instantaneous health responses may be uncovered, paving the way for the development of individualized therapies and lifestyle recommendations. The suggested framework allows people to improve their health and gives medical professionals access to data-driven insights that can be used for the early diagnosis of diseases and the development of individualized treatment plans. The work that has been done sheds light on the revolutionary potential of merging genetics with the Internet of Things to establish a new paradigm of preventative and individualized medical treatment. © 2024 IEEE.

关键词： Genome

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A novel CZP-bC-FLASH ADC using zero phase bubble error robust controller for reliable VLSI circuits

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International Journal of Information technology (Singapore) 2022年第1期14卷 155-163页

作者： Kalaiyarasi, M. Saravanan, S. Karunanithi, K. Raja, S.P. Department of Electronics and Communication Engineering V.S.B. Engineering College Karur India Department of Electrical and Electronics Engineering B V Raju Institute of Technology Telangana Narsapur India Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Avadi Tamilnadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore 632014 India

To process the data, accurate and reliable, analog to digital converter (ADC) is an essential element in most applications such as satellite communication, instrumentation and measurement, industrial automation, defence, and aerospace applications. In reliable VLSI circuits, to mitigate the wastage of power, high quality and high-resolution Clocked Flash ADCs are utilized. It converts an analog electrical signal into a digital discrete-valued signal using a parallel set of comparators and the transformation time is very fast in flash ADC. Even though many approaches were proposed to convert the analog signal to a digital signal, the analog signal could not correctly convert because of its error. This paper proposed a novel flash ADC namely Clocked Zero Phase bubble Error Controller Flash ADC (CZP-bC-Flash ADC) using Clocked-Improved Threshold Inverter Quantization (CITIQ) comparator and Zero Phase bubble Error Robust Controller (ZPbEC). CITIQ comparator uses two cascaded inverters, Preamplifier and two parallel sets of PMOS and NMOS transistors whereas ZPbEC calculates the transfer function of the system based on the changes of the input and locates the error. There is a noticeable basis for eliminating the circuit board that unnecessarily spoils energy. The proposed CZP-bC-FLASH is executed using a tanner EDA with 250 nm size. When compared with QVC ADC technique, the error detection accuracy of proposed method has increased by 43% with the help of ZPbEC Controller. The simulation results showed that the proposed Controlled ADC method reduces power consumption by 63.32% when compared with QVC ADC method as well as compared with modified QVC ADC, it decreases to 60.98%. While the error controller controls the error signal by 56% compared with previous F-ADC and corrects the bits up to 4 and gives an efficiency of 97%. © 2021, bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Clocked flash Comparator Error controller Neural compensation VLSI

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