Xcelerate5G: Optimizing Resource Allocation Strategies for 5G Network Using ML Neha Shukla, Ayush Siloiya, Apoorva Singh, Aayushi Saini Proceedings International Conference on Computing Power and Communication Technologies Ic2pct 2024, 2024 As we embrace the transformative era of 5G technology, promising unprecedented data rates, minimal latency, and extensive device connectivity, the need for effective resource allocation becomes paramount. This research delves into the realm of machine learning, specifically exploring linear regression, support vector machines (SVM), and k-nearest neighbor (KNN) models to optimize resource allocation in 5G networks. Examining previous research, we uncover a focus on training models to assess incoming traffic and predict network slices for unknown device types using key performance indicators (KPIs) [1]. To enhance resource utilization, our study introduces and compares three machine learning models: linear regression, SVM, and KNN. These models forecast optimal resource allocation based on past network data and user trends. While linear regression offers simplicity, SVM and KNN present more sophisticated and adaptive models. In the dynamic conditions of 5G networks, machine learning-based resource allocation outperforms traditional methods, excelling in bandwidth efficiency, user satisfaction, noise reduction, and signal strength. Key considerations include accuracy, scalability, and resource distribution for various application types. This study underscores the significance of machine learning techniques, contributing to a deeper understanding of resource allocation in 5G networks. It provides comprehensive insights into the advantages and limitations of linear regression, SVM, and KNN models, empowering network operators and researchers to make informed decisions that enhance the overall performance and efficiency of evolving 5G networks across diverse use cases.
Plant Disease Detection and Crop Recommendation Using CNN and Machine Learning Raj Kumar, Neha Shukla, Princee 2022 International Mobile and Embedded Technology Conference Mecon 2022, 2022 The wide-scale prevalence of diseases in crops and inefficient soil to grow crops highly damage the standard quality and quantity of crop production. So, the disease in the crops needs to be early diagnosed by developing or employing a fast and innovatory approach and crop recommendation system will benefit the farmers. Hence, this study proposed a system that has the ability to detect diseases in plants using CNN as well as recommend various crops based on the quality of the soil by performing analysis on its various parameters using ML. The dataset for disease prediction training and test is obtained from the Plant Village Dataset and correctly separated and therefore various species of plants are recognized and re-named to make an accurate database. The next step is to obtain a test database that will be consisting of different diseases in plants that are used to check the accuracy and confidence level of the proposed module. Then the classifier is trained using training data and after that, the output is going to be detected with the best accuracy. And for the crop recommendation system, the Support vector classifier (SVC) algorithm is used as it outperforms compared to other classifiers like KNN, Logistic Regression, Random Forest, and Decision Trees, in the system to improve the efficiency rate of our model. The developed model also maps the soil and crop database and suggests suitable crops based on the available nutrients level of the soil and thus allows formers to make better decisions regarding the type of crops that can be sown-in in the field. This study also compared the performance of various classifiers on the available dataset for study and chose the one with the highest accuracy.
Heart Anomalies Prediction Utilizing a Variety of Machine Learning Algorithms Neha Shukla, Anand Pandey, A P Shukla Proceedings of 5th International Conference on Contemporary Computing and Informatics Ic3i 2022, 2022 We are aware that cardiovascular diseases are very lethal, patients do not get enough time for treatment and the treatment is also expensive for most people. The goal of this study is to predict the likelihood of an acute heart attack using a variety of machine learning approaches, including K closest neighbour, logistic regression, random forest classifier, support vector machine, and XGB classifier. The accuracy score obtained by all the machine learning algorithms has been demonstrated with the help of a table.
Online Book Recommendation System using Custom Recommender Neha Shukla, Neeraj Soni, Nikhil Gupta, Nikhil Anand 2022 6th International Conference on Trends in Electronics and Informatics Icoei 2022 Proceedings, 2022 Recommendation systems can be considered as one of the most popular tools to raise the profit and retain users. In most of the fields Recommender systems are being used. The aim of the recommender system is that it suggests content for users based on their previous choices or what type of taste they are having. When there is a need for implementing an effective recommender system, it should always be diverse in content and it is not supposed to be biased towards the most popular content. In this perspective, the content-based filtering will provide well-suited results for the user. This research study attempts to propose a Recommender system for suggesting consecutive appropriate books for the user to read. The proposed recommender system is designed by using item-based collaborative filtering, content-based collaborative filtering (using Title, Author, Publisher, Category as features), Content-Based Collaborative Filtering (using Summary as a feature), Custom Recommender and at the end different recommenders are compared.
Traffic Congestion Management using Camera and Geolocation Neha Shukla, Dhruv Garg, Siddharth Singh, Chetan Upadhyaya 2022 6th International Conference on Trends in Electronics and Informatics Icoei 2022 Proceedings, 2022 As the world's population grows, traffic congestion has become a severe issue. The paper examines the issue of traffic congestion as well as proposes a solution. According to the solution, one of the primary causes of traffic management inefficiency is insufficient information of real-time traffic congestion to the police and lack of knowledge of local routes and neighborhoods. This paper deals with how people report the traffic and that data can be shared with traffic cops which allows traffic personnel to quickly and effectively track the traffic congestion and resolve it. It will help in the reduction of traffic congestion by enlisting the assistance of traffic cops and improving data management.
ECG-ViT: A Transformer-Based ECG Classifier for Energy-Constraint Wearable Devices Neha Shukla, Anand Pandey, Anand Prakash Shukla, Sanjeev Chandra Neupane Journal of Sensors, 2022 The advancement in deep learning techniques has helped researchers acquire and process multimodal data signals from different healthcare domains. Now, the focus has shifted towards providing end-to-end solutions, i.e., processing these data and developing models that can be directly implemented on edge devices. To achieve this, the researchers try to solve two problems: (I) reduce the complex feature dependencies and (II) reduce the complexity of the deep learning model without compromising accuracy. In this paper, we focus on the later part of reducing the complexity of the model by using the knowledge distillation framework. We have introduced knowledge distillation on the Vision Transformer model to study the MIT-BIH Arrhythmia Database. A tenfold crossvalidation technique was used to validate the model, and we obtained a 99.7% F1 score and 99.3% accuracy. The model was further tested on the Xilinx Alveo U50 FPGA accelerator, and it is found fit for any low-powered wearable device implementation.
Improved Frame-Wise Segmentation of Audio Signals for Smart Hearing Aid Using Particle Swarm Optimization-Based Clustering Tushar Mehrotra, Neha Shukla, Tarunika Chaudhary, Gaurav Kumar Rajput, Majid Altuwairiqi, et al. Mathematical Problems in Engineering, 2022 Labeling speech signals is a critical activity that cannot be overlooked in any of the early phases of designing a system based on speech technology. For this, an efficient particle swarm optimization (PSO)-based clustering algorithm is proposed to classify the speech classes, i.e., voiced, unvoiced, and silence. A sample of 10 signal waves is selected, and their audio features are extracted. The audio signals are then partitioned into frames, and each frame is classified by using the proposed PSO-based clustering algorithm. The performance of the proposed algorithm is evaluated using various performance metrics such as accuracy, sensitivity, and specificity that are examined. Extensive experiments reveal that the proposed algorithm outperforms the competitive algorithms. The average accuracy of the proposed algorithm is 97%, sensitivity is 98%, and specificity is 96%, which depicts that the proposed approach is efficient in detecting and classifying the speech classes.
Techniques of Sarcasm Detection: A Review Palak Verma, Neha Shukla, A.P. Shukla 2021 International Conference on Advance Computing and Innovative Technologies in Engineering Icacite 2021, 2021
E-assessments and Feedback Mechanisms in Moocs Neha Shukla, Arti Sharma, Amrit Kaur Saggu IEEE International Conference on Issues and Challenges in Intelligent Computing Techniques Icict 2019, 2019
RECENT SCHOLAR PUBLICATIONS
Desktop-Based Virtual Assistant Using Python Based on Natural Language Processing SG Vivek Sharma, Neha Shukla, Srijan Shahi, Sushant Singh International Conference On Data Mining And Information Security, 27-36 , 2025 2025
Comparative Analysis of Machine Learning Algorithms for Early Sepsis Detection. YP Puneet Kumar Goyal, Neha Shukla,Vishal Verma, Yash Goel International Conference On Data Mining And Information Security, 59-73 , 2025 2025
Xcelerate5G: Optimizing resource allocation strategies for 5G network using ML N Shukla, A Siloiya, A Singh, A Saini 2024 IEEE International Conference on Computing, Power and Communication … , 2024 2024 Citations: 10
Classification of Patient’s Heartbeat Obtained by ECG Using Active Learning N Shukla, A Pandey, AP Shukla Computational Intelligence: Select Proceedings of InCITe 2022, 571-581 , 2023 2023
Heart Anomalies Prediction Utilizing a Variety of Machine Learning Algorithms N Shukla, A Pandey, AP Shukla 2022 5th International Conference on Contemporary Computing and Informatics … , 2022 2022
TEXT BASED RECOGNITION SYSTEM VST NEHA SHUKLA, SHIVAM JAISWAL Advances and Applications in Mathematical Sciences 21 (12), 7053-7066 , 2022 2022
Online book recommendation system using custom recommender N Shukla, N Soni, N Gupta, N Anand 2022 6th International Conference on Trends in Electronics and Informatics … , 2022 2022 Citations: 7
Traffic Congestion Management using Camera and Geolocation N Shukla, D Garg, S Singh, C Upadhyaya 2022 6th International Conference on Trends in Electronics and Informatics … , 2022 2022
Plant disease detection and crop recommendation using CNN and machine learning R Kumar, N Shukla 2022 international mobile and embedded technology conference (MECON), 168-172 , 2022 2022 Citations: 31
[Retracted] ECG‐ViT: A Transformer‐Based ECG Classifier for Energy‐Constraint Wearable Devices N Shukla, A Pandey, AP Shukla, SC Neupane Journal of Sensors 2022 (1), 2449956 , 2022 2022 Citations: 20
Improved frame‐wise segmentation of audio signals for smart hearing aid using particle swarm optimization‐based clustering T Mehrotra, N Shukla, T Chaudhary, GK Rajput, M Altuwairiqi, ... Mathematical Problems in Engineering 2022 (1), 1182608 , 2022 2022 Citations: 15
Wild animal species detection using deep convolution neural network A Verma, V Sangwan, N Shukla Recent Trends in Communication and Electronics, 406-410 , 2021 2021 Citations: 2
Comparative analysis of IoT device security A Rastogi, V Sangwan, A Verma, N Shukla Recent Trends in Communication and Electronics, 572-576 , 2021 2021
Brain tumor segmentation using CNN AK Tiwari, N Shukla Recent Trends in Communication and Electronics, 411-415 , 2021 2021 Citations: 7
Techniques of sarcasm detection: A review P Verma, N Shukla, AP Shukla 2021 international conference on advance computing and innovative … , 2021 2021 Citations: 86
Improvement and reduction of clustering overhead in mobile Ad Hoc network with optimum stable bunching algorithm M Bhardwaj, N Shukla, A Sharma Evolution of Software-Defined Networking Foundations for IoT and 5G Mobile … , 2021 2021 Citations: 2
Study and Analysis of IOT Centric Cloud Technology NS Manish Bhardwaj, Arti Sharma Studies in Indian Place Names , 2020 2020
E-assessments and feedback mechanisms in Moocs N Shukla, A Sharma, AK Saggu 2019 international conference on issues and challenges in intelligent … , 2019 2019 Citations: 7
Prediction of diabetes using neural network & random forest tree N Shukla, M Arora International Journal of Computer Sciences and Engineering 4 (07), 101-104 , 2016 2016 Citations: 7
Random forest v/s scaled conjugate gradient to predict diabetes mellitus N Shukla, M Arora International Journal of Computational Intelligence Research 12 (2), 117-123 , 2016 2016 Citations: 1
MOST CITED SCHOLAR PUBLICATIONS
Techniques of sarcasm detection: A review P Verma, N Shukla, AP Shukla 2021 international conference on advance computing and innovative … , 2021 2021 Citations: 86
Plant disease detection and crop recommendation using CNN and machine learning R Kumar, N Shukla 2022 international mobile and embedded technology conference (MECON), 168-172 , 2022 2022 Citations: 31
[Retracted] ECG‐ViT: A Transformer‐Based ECG Classifier for Energy‐Constraint Wearable Devices N Shukla, A Pandey, AP Shukla, SC Neupane Journal of Sensors 2022 (1), 2449956 , 2022 2022 Citations: 20
Improved frame‐wise segmentation of audio signals for smart hearing aid using particle swarm optimization‐based clustering T Mehrotra, N Shukla, T Chaudhary, GK Rajput, M Altuwairiqi, ... Mathematical Problems in Engineering 2022 (1), 1182608 , 2022 2022 Citations: 15
Xcelerate5G: Optimizing resource allocation strategies for 5G network using ML N Shukla, A Siloiya, A Singh, A Saini 2024 IEEE International Conference on Computing, Power and Communication … , 2024 2024 Citations: 10
Online book recommendation system using custom recommender N Shukla, N Soni, N Gupta, N Anand 2022 6th International Conference on Trends in Electronics and Informatics … , 2022 2022 Citations: 7
Brain tumor segmentation using CNN AK Tiwari, N Shukla Recent Trends in Communication and Electronics, 411-415 , 2021 2021 Citations: 7
E-assessments and feedback mechanisms in Moocs N Shukla, A Sharma, AK Saggu 2019 international conference on issues and challenges in intelligent … , 2019 2019 Citations: 7
Prediction of diabetes using neural network & random forest tree N Shukla, M Arora International Journal of Computer Sciences and Engineering 4 (07), 101-104 , 2016 2016 Citations: 7
Wild animal species detection using deep convolution neural network A Verma, V Sangwan, N Shukla Recent Trends in Communication and Electronics, 406-410 , 2021 2021 Citations: 2
Improvement and reduction of clustering overhead in mobile Ad Hoc network with optimum stable bunching algorithm M Bhardwaj, N Shukla, A Sharma Evolution of Software-Defined Networking Foundations for IoT and 5G Mobile … , 2021 2021 Citations: 2
Random forest v/s scaled conjugate gradient to predict diabetes mellitus N Shukla, M Arora International Journal of Computational Intelligence Research 12 (2), 117-123 , 2016 2016 Citations: 1
Desktop-Based Virtual Assistant Using Python Based on Natural Language Processing SG Vivek Sharma, Neha Shukla, Srijan Shahi, Sushant Singh International Conference On Data Mining And Information Security, 27-36 , 2025 2025
Comparative Analysis of Machine Learning Algorithms for Early Sepsis Detection. YP Puneet Kumar Goyal, Neha Shukla,Vishal Verma, Yash Goel International Conference On Data Mining And Information Security, 59-73 , 2025 2025
Classification of Patient’s Heartbeat Obtained by ECG Using Active Learning N Shukla, A Pandey, AP Shukla Computational Intelligence: Select Proceedings of InCITe 2022, 571-581 , 2023 2023
Heart Anomalies Prediction Utilizing a Variety of Machine Learning Algorithms N Shukla, A Pandey, AP Shukla 2022 5th International Conference on Contemporary Computing and Informatics … , 2022 2022
TEXT BASED RECOGNITION SYSTEM VST NEHA SHUKLA, SHIVAM JAISWAL Advances and Applications in Mathematical Sciences 21 (12), 7053-7066 , 2022 2022
Traffic Congestion Management using Camera and Geolocation N Shukla, D Garg, S Singh, C Upadhyaya 2022 6th International Conference on Trends in Electronics and Informatics … , 2022 2022
Comparative analysis of IoT device security A Rastogi, V Sangwan, A Verma, N Shukla Recent Trends in Communication and Electronics, 572-576 , 2021 2021
Study and Analysis of IOT Centric Cloud Technology NS Manish Bhardwaj, Arti Sharma Studies in Indian Place Names , 2020 2020
