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

作者： Waghmare, Shreyash Chandavarkar, B.R. National Institute of Technology Department of Computer Science & Engineering Karnataka Surathkal India

ISBN: (纸本)9798350335095

One of the fundamental functions of any Underwater Acoustic Sensor Network (USANs) is routing. Due to long transmission delays, change in the position of nodes, bandwidth issues, and changes in frequency, designing routing protocols for UASNs presents several difficulties. Numerous UASNs routing protocols exist and were developed based on various factors such as link quality, hop count, and packet delivery probability. One of the key elements affecting how well routing functions in underwater communication is link quality which is measured using the physical characteristics of underwater. The paper's main contribution is designing an Enhanced Link Quality-based Routing Protocol(ELQRP) for underwater networks where cumulative packet delivery ratio(PDR) is being used to determine the next best hop for any node in a network. ELQRP chooses the link which has the highest cumulative PDR. Implementation of ELQRP is done using Matlab. © 2023 IEEE.

关键词： Routing protocols

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Diagnosis of Polycystic Ovary Syndrome Using Machine Learning Models: A Comparative Analysis

Diagnosis of Polycystic Ovary Syndrome Using Machine Learnin...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Majumdar, Sounak Biswas, Saroj Kr. Purkayastha, Biswajit Department of Computer Science and Engineering National Institute of Technology Silchar Silchar India

ISBN: (纸本)9798350325188

Polycystic Ovary Syndrome (PCOS) is a serious health problem affecting a significant percentage of female population worldwide during their reproductive years and adolescence. Besides being one of the leading causes of female infertility, PCOS also carries risks of long-term health issues such as diabetes, high blood pressure, and cardiovascular disease. While PCOS is not curable, proper treatment can alleviate some symptoms. Hence, timely diagnosis is crucial for effective treatment. PCOS diagnosis is challenging due to varying symptoms among affected women. Recently, promising progress in medical diagnosis has been made using machine learning techniques. This paper presents a comparative analysis of the performance of single classifier-based machine learning models and ensemble machine learning models in diagnosing PCOS using benchmark Kaggle PCOS dataset. 10-fold cross-validation has been used to assess the performance of the models. From the experimental results it is observed that among the thirteen different machine learning models considered for this study, the Random Forest classifier has achieved the best performance with an accuracy and F1-score of 90.1% and 83.6% respectively. © 2023 IEEE.

关键词： Accuracy Cross-validation Ensemble Classifiers F1-score Machine Learning PCOS Single Classifiers

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Simulation Based Analysis of Low-Voltage-Tunable Biquad Filter Using Operational Transconductance Amplifiers

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International Journal of Electronics Letters 2024年第1期12卷 9-20页

作者： Raj, Anamika Singh, Sangeeta Kumar, Pankaj National Institute of Technology Patna 800005 India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India

A CMOS realisation of a voltage tunable biquad filter using only MOSFET-based operational transconductance amplifier (OTA) and capacitors has been designed using silicon-based CMOS technology. It operates at a low supply voltage of (Formula presented.) V at a (Formula presented.) MHz frequency. The filter is theoretically analysed and then simulated using commercially available Cadence Virtuoso tools at (Formula presented.) (Formula presented.) CMOS technology parameters. The proposed design is used to implement second-order low pass, highpass, bandpass, and bandreject filters with the same circuit simply by tuning the input voltage. The bandwidth and cut-off frequency also can be changed by changing the capacitor values. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Biquad filter low voltage Operational Transconductance Amplifier (OTA)

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Song Recommendation based on Voice Tone Analysis 2

Song Recommendation based on Voice Tone Analysis

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Jaison C, Raichal Rose Rajeswari, M. Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350346640

Music suggestions based on elements including user listening history, music genre, etc. have become more popular in recent years. The user's emotional state is not considered by conventional music recommendation algorithms;therefore, they might not offer suggestions that are appropriate for their present mood. This study introduces a song recommendation system that uses artificial intelligence and machine learning to make individualized music suggestions based on the emotional state of the user. The system extracts feature with the help of MFCC to analyze the tone of the user. The suggested method incorporates deep learning models such as Artificial Neural Networks that provide better accuracy to train the model. The major challenge in creating such a system is to successfully determine the data for the recommendation process by accurately and consistently detecting the user's emotional state from speech. The proposed system offers new pathways for research in the fields of artificial intelligence and music recommendation and has the potential to alter the way people listen to music. © 2023 IEEE.

关键词： Music

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Deep Ear Biometrics for Gender Classification 4th

Deep Ear Biometrics for Gender Classification

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4th International Conference on Communication, Devices and Computing (ICCDC 2023)

作者： Singh, Ritwiz Kashyap, Keshav Mukherjee, Rajesh Bera, Asish Chakraborty, Mamata Dalui Department of Computer Science and Engineering Haldia Institute of Technology WB Haldia India Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani Rajasthan Pilani Campus 333031 India Department of Computer Science and Engineering National Institute of Technology West Bengal Durgapur India

ISBN: (纸本)9789819927098

Human gender classification based on biometric features is a major concern for computer vision due to its vast variety of applications. The human ear is popular among researchers as a soft biometric trait, because it is less affected by age or changing circumstances and is non-intrusive. In this study, we have developed a deep convolutional neural network (CNN) model for automatic gender classification using the samples of ear images. The performance is evaluated using four cutting-edge pre-trained CNN models. In terms of trainable parameters, the proposed technique requires significantly less computational complexity. The proposed model has achieved 93% accuracy on the EarVN1.0 ear dataset. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural network models

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VIhanceD: Efficient Video Super Resolution for Edge Devices 2

VIhanceD: Efficient Video Super Resolution for Edge Devices

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2nd Edition of IEEE Delhi Section Owned Conference, DELCON 2023

作者： Papanai, Ashish Babbar, Siddharth Pandey, Anshuman Kathuria, Harshit Sharma, Alok Kumar Gupta, Namita Maharaja Agrasen Institute of Technology Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350322064

Video Super Resolution (VSR) aims to generate high-resolution (HR) frames from corresponding low-resolution (LR) frames. It draws a stark contrast from single image super-resolution (SISR) because of its high temporal dependency on misaligned supporting frames. The existing methods involve using RNNs to learn the temporal dependency by using other networks (CNNs, GANs) for predicting neighboring pixels. Due to the memory and processing constraints and the inference time required for up-scaling LR frames, a wide variety of VSR techniques cannot be applied to mid-range and budget mobile devices. This paper presents VIhanceD, a real-time sliding window-based network that can operate on budget smartphones and laptops while producing cutting-edge results on various video datasets. Our approaches include both spatial and temporal dependencies to make the up-scaled HR frames coherent and free of motion distortions. We focus on enhancing the user experience in areas with internet restrictions due to social, political, and geographical limitations. The mobile app (and PC client) provides a continuous stream of HR frames without buffering. We obtained 33.9 PSNR on REDS Dataset with a single frame inference time of 23.6 ms demonstrating state-of-the-art performance. Our subsequent experiments on VIMEO-90K dataset demonstrates that the suggested method is generalizable and works on natural video frames and textual data, making it suitable for infotainment and multimedia. © 2023 IEEE.

关键词： Optical flows

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Privacy-Preserving sensitive data on Medical diagnosis using Federated Learning and Homomorphic Re-encryption 3

Privacy-Preserving sensitive data on Medical diagnosis using...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Dasari, Jahnavi Joshith, Telugu Sai Daya Lokesh, Duddupudi Kumar, Sanjipogu Sandeep Mahato, Ganesh Kumar Chakraborty, Swarnendu Kumar National Institute of Technology Department of Computer Science & Engineering Arunachal Pradesh India

ISBN: (纸本)9798350338607

Federated learning is an emerging technique that allows multiple participants to train a shared model without exchanging their private data. This approach is particularly useful for IoT applications, where data is often collected locally and stored in distributed devices like edge nodes. Privacy concerns arise when using IoT devices to record medical data for distributed learning. Techniques such as differential privacy and federated learning can help ensure data security while preserving privacy. Encryption and secure multi-party computation can also be used to securely share and compute data. By training a model on a combination of data from these devices. In the medical research, federated learning can be used to train models on data from multiple healthcare devices such as wearables, smart medical sensors, and electronic health records. By training models on local data, healthcare providers can improve the accuracy of diagnosis and treatment while protecting patient privacy. Security analysis can involve evaluating the potential vulnerabilities and risks of the system and identifying measures to protect against them. Experimental results can involve testing the performance of the system in terms of accuracy, convergence speed, and other metrics, and comparing it to other methods. © 2023 IEEE.

关键词： Diagnosis

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Efficient Regular Expression Matching and Hardware-Accelerated Finite Automata Pattern Recognition in NIDS 6

Efficient Regular Expression Matching and Hardware-Accelerat...

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6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Thota, Krishna Kishore Retna Raj, R. Jeberson Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu India

ISBN: (纸本)9798350394702

To protect network infrastructure from the ever-changing cyber threats, Intrusion Detection Systems (IDS) perform important duties. This study introduces a sophisticated algorithm aimed at overcoming key challenges in IDS. These challenges include efficient regular expression matching, hardware implementation, and improved pattern recognition. The algorithm employs a unique technique to reduce memory specifications by using controlled faults into the pattern matching process. It builds upon perfect hashing, ensuring precise and efficient regular expression matching, making it ideal for high-speed processing in IDS applications. Additionally, the central component of a hardware-based Network Intrusion Detection System (NIDS), the technique comprises a signature match processor that is based on a field-programmable gate array (FPGA). For effective string matching, this processor makes use of a CAM-based cellular architecture. For multiple signature matches, it generates priority-encoded addresses using a binary tree structure. To enhance IDS capabilities further, the algorithm introduces an efficient method for detecting regular expression matches within network traffic text using Field-Programmable Gate Arrays (FPGAs). It presents a hardware implementation approach based on Deterministic Finite Automata (DFA), enhancing both speed and accuracy in pattern matching. This proposed algorithm provides a comprehensive solution to optimize intrusion detection by integrating innovative techniques in regular expression matching, hardware acceleration, and pattern recognition, thereby enhancing network security against springing cyber threats. © 2023 IEEE.

关键词： Cryptography

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A Review on Parameters that Impact IoT Application—An Experimental Evaluation 1st

A Review on Parameters that Impact IoT Application—An Expe...

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1st International Conference on Microelectronics, Communication Systems, Machine Learning, and Internet of Things, MCMI 2020

作者： Gupta, Sakshi Snigdh, Itu Department of Computer Science and Engineering Birla Institute of Technology Mesra Ranchi India

ISBN: (纸本)9789811919053

With the advent of the Internet of Things (IoT), diverse long-range, low-power, and low-bit-rate wireless network technologies have gained impetus. The success is, however, based on the capabilities of the underlying internetwork of "things." Our article focuses on a generic perspective of how scalability issues impact communications among IoT. We present a detailed analysis of the impacts of sensing range and environmental factors that affect the network performance. The simulations provide a realistic outlay to estimate the dependency of factors that can be controlled for expected IoT network performance and for deciding on configuring and reconfiguring IoT scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Color and texture feature fusion for breast cancer classification using histopathology images 20

Color and texture feature fusion for breast cancer classific...

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20th IEEE India Council International Conference, INDICON 2023

作者： Deb, Dipti Dash, Ratnakar Mohapatra, Durga Prasad National Institute of Technology Rourkela Department of Computer Science and Engineering Rourkela India

ISBN: (纸本)9798350305593

Recent advancements in the fields of artificial intelligence and computer vision have made it possible to create computer-aided diagnosis (CAD) systems to detect breast cancer using histopathology images. In this research, a classification framework for breast cancer has been developed using histopathology images. In this regard, the color and texture features of the images are utilized for classification using several machine learning (ML) classifiers. For texture, three handcrafted feature extraction techniques, such as Gray-Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP), and Gabor have been investigated. Similarly, for color features, HSV histogram features and H, S, and V values of the HSV color model are used to classify the images. The features extracted from each method are evaluated individually. The best derived methods are combined and feed into different ML classifiers. The performance of each classifier is evaluated using performance metrics such as accuracy, precision, specificity, sensitivity, and F1-score. It is observed that LBP and HSV histogram with XGB classifier obtained an accuracy of 87.50% and 83.62%, respectively. After fusing the LBP and HSV histogram features with the XGB classifier obtained an accuracy of 93.34%. The experiment is carried out on the BreakHis dataset. The proposed methodology is useful in classifying breast cancer histopathology images with benign and malignant. © 2023 IEEE.

关键词： Breast Cancer Handcrafted features Histopathology images Machine Learning

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