Dr Kanhaiya Sharma

Scopus Publications

Gain-enhanced petal-shaped MIMO antenna system with FSS loading for sub-6 GHz V2X communications
Bhaskara Rao Perli, K. Sathish, Aarti Bansal, Tathababu Addepalli, Baddireddy Satya Sridevi, Manish Sharma, Kanhaiya Sharma
Scientific Reports, 2026
In this research work, a compact dual-port petal-shaped MIMO antenna printed on FR4-substrate with volume of 24 × 24 × 0.787 mm3 is presented for vehicle-to-everything (5.85–5.95 GHz) wireless communication. The gain-enhancement is also achieved by loading frequency-selective-surface array (FSSV2X) of size 204 mm×204 mm printed on one surface of FR4 substrate with 1.60 mm thickness which records measured peak-realized-gain of 7.58 dBi within the operating-bandwidth of 4.12–6.92 GHz. High measured isolation of more than 25.0 dB is also achieved by placing multiple discontinuous rectangular strips between ground by an angle of 45°. The permissible diversity parameter also records excellent performance with ECCV2X<0.50, DGV2X>9.95 dB, TARCV2X<0.0 dB and CCLV2X<0.40 b/s/Hz. The above features of FSS-loaded MIMO antenna is a good candidate for vehicle-to-vehicle automotive (V2V), vehicle-to-pedestrian (V2P) and vehicle-to-infrastructure (V2I) applications.
Review of Hybrid and Data-Efficient Methods in Medical Image Segmentation
Abhishek Kr. Dubey, Vinod Kumar Singh, Kanhaiya Sharma, Anjali Dubey, Zakir Ali, Aparna Singh, Sankalp Yadav
Archives of Computational Methods in Engineering, 2026
Compact wearable microstrip antenna design using hybrid quasi-Newton and Taguchi optimization
Archana Tiwari, Aleefia A. Khurshid, Kanhaiya Sharma
Scientific Reports, 2025
A novel approach is introduced for designing a miniaturized wearable antenna. Utilizing Taguchi's philosophy typically entails numerous experimentations runs, but our method significantly reduces these by employing a quasi-Newton approach with gradient descent to estimate process parameter ranges. This hybrid technique expedites convergence by streamlining experiments. Additionally, the Taguchi array ensures a balanced design, equalizing factor weights. Unlike conventional Taguchi methods, which risk trapping optimized results at local minima with increased repetitions, our modified technique mitigates this issue by adjusting level differences, aiming for global minima. Antenna design often involves competing objectives, such as size, impedance matching, cross-polarization, directivity, and frequency range. This study addresses these multiobjective challenges using a hybrid approach. The proposed method is applied to design and fabricate a biosafe miniaturized antenna for integration into clothing. The comparison of computed and measured antenna parameters confirms the accuracy of our solution while demonstrating a reduction in the required number of experiments. This innovative approach significantly advances the efficient design of wearable antennas. The biosafe wearable antenna demonstrated compliant specific absorption rate (SAR) (1.2 W/kg), robust mechanical performance (up to 40° bending), and underwent human body effect investigation. Comparison of computed and measured antenna parameters confirms solution accuracy. By implementing the proposed hybrid approach, computational time is significantly reduced by 98%, outperforming electromagnetic (EM) solvers' built-in optimization.
A hybrid rule-based NLP and machine learning approach for PII detection and anonymization in financial documents
Kushagra Mishra, Harsh Pagare, Kanhaiya Sharma
Scientific Reports, 2025
Safeguarding Personally Identifiable Information (PII) in financial documents is essential to prevent data breaches and maintain regulatory compliance. This research presents a scalable hybrid approach that integrates rule-based Natural Language Processing (NLP), Machine Learning (ML) approaches, and a custom Named Entity Recognition (NER) model for the accurate detection and anonymization of Personally Identifiable Information (PII). A varied and accurate synthetic dataset was created to replicate genuine financial document formats, enhancing model training and assessment. The model has attained a precision of 94.7%, a recall of 89.4%, an F1-score of 91.1%, and an overall accuracy of 89.4% on synthetic datasets. Additional validation on actual financial documents, such as audit reports and vendor bills, revealed a consistent performance with an accuracy of 93%. The study utilizes confusion matrices, ROC curves, and precision-recall curves to evaluate the model which further validates the model's capabilities and generalization ability. The suggested approach provides a robust and efficient solution for protecting sensitive information in operational financial contexts, markedly enhancing current methods for PII protection.
Reducing energy consumption in air conditioning systems, a fuzzy logic-based optimization approach
Harsh Pagare, Kushagra Mishra, Kanhaiya Sharma, Ketan Kotecha, Ambarish Kulkarni
Energy Reports, 2025
The need for more intelligent air conditioning solutions is growing as global temperatures rise and energy efficiency concerns increase. In order to adapt dynamically to changing outdoor environmental factors like temperature fluctuations, humidity variations, and voltage instability, traditional air conditioning systems (ACS) usually rely on static control mechanisms. Rigid control paradigms like these result in ineffective operations, excessive energy use, and decreased user comfort. In order to get around these restrictions, this paper presents a practical fuzzy logic controller (FLC) that is specifically made to optimise compressor speed ratios and fan speed in real-time. Our method uses adaptive fuzzy logic, which offers greater flexibility in managing environmental uncertainties and disturbances than current controllers that either rely on fixed parameters or intricate machine learning models. Based on thorough simulations and performance evaluations, our suggested approach shows a significant 20–25 % decrease in energy consumption when compared to traditional systems. The main innovation is its straightforward but reliable fuzzy-based rule system, which strikes a balance between user comfort, adaptability, and energy efficiency and lays the groundwork for further intelligent climate-control advancements. • ACS controller for real-time parameter optimization based on fuzzy logic. • 20–25 % less energy was used than with traditional ACS. • Adaptability is increased through dynamic compressor and fan speed adjustment. • Temperature, humidity, and voltage fluctuations are managed by a robust fuzzy logic controller. • The suggested strategy strikes a balance between energy-efficient cooling and user comfort.
A DOA-Driven Adaptive Framework for Smart Traffic and Street Lighting in WSN
Savita Jadhav, D. G. Bhalke, Kanhaiya Sharma, Seyed Jalaleddin Mousavirad, Ghanshyam G. Tejani
International Journal of Computational Intelligence Systems, 2025
Traffic management and street lighting optimization are increasingly dependent on intelligent systems for smart cities. The main objective of this paper is to design the Intelligent Traffic and Lighting Systems (ITLS) using the dream optimization algorithm (DOA) for Wireless Sensor Networks (WSNs). The system assimilates single-lane roads, moving vehicles, sensor-equipped streetlights, and a centralized control station. The fitness function of DOA optimizes network performance by balancing energy consumption, reducing congestion, and stabilizing vehicle speed variations. The network adjusts to changing traffic conditions, optimizing routes and lighting efficiency. The MATLAB simulation shows that DOA surpasses traditional rule-based systems by refining traffic flow while reducing energy usage. The performance of our proposed approach, DOA-ITLS, is compared with existing techniques like IB-SEC and KFFOA-PDES in terms of network lifetime, packet delivery, throughput, and energy efficiency. The protocol enhances packet delivery by 50% and extends network lifetime by effectively delaying node failures. DOA-ITLS is found to be a scalable, robust, and energy efficient solution for urban traffic and lighting control. By enhancing data delivery and system responsiveness, this framework makes urban mobility more sustainable and efficient.
Design of an iterative method for adaptive federated intrusion detection for energy-constrained edge-centric 6G IoT cyber-physical systems
S. Phani Praveen, Kanhaiya Sharma, Deepak Parashar, V. S. N. Murthy, Uddagiri Sirisha, Deshinta Arrova Dewi
Scientific Reports, 2025
The increasing proliferation of 6G-enabled Internet of Things (IoT) in the Cyber-Physical Systems (CPS) domain has engendered requirements for distributed, intelligent, and energy-efficient Intrusion Detection Systems (IDS) operating to the edge. Thus, conventional IDS approaches are largely centralized and ignore some vital constraints of edge-centric CPS, such as limited energy, privacy preservation, and real-time responses to threats. Currently existing federated learning (FL)-based IDS solutions cannot optimize data relevance, model sparsity, or trade-offs for privacy efficiency, resulting in communications overhead and impaired performance under resource constraints. To this end, a Lightweight Federated Intrusion Detection Framework for Edge-Centric 6G IoT CPS is proposed in this paper, incorporating five novel analytical modules to achieve decentralized, adaptive, and resource-aware IDS operations. Foremost, Energy-Adaptive Federated Reinforcement Aggregation (EAFRA) will adjust model updates reasonably depending on local energy so that energy and accuracy can be optimized using reinforcement learning methods. Secondly, Spatio-Temporal Uncertainty-aware Federated Attention Filtering (STUFAF) applies Bayesian uncertainty with contextual metadata in giving priority for the informative updates while reducing false positives. Third, Lightweight Self-Evolving Edge Autoencoder Forest (LSE-EAF) assures low latency and high accuracy detection with minimal resource consumption using a hybrid of anomaly detectors. Fourth, Differentially Private Sparse Cluster Aggregation (DPSCA) does adaptive privacy-preserving sparse updates to contextually clustered nodes to balance privacy and communication costs. Finally, Federated Task-Aware Compression with Cyclical Consistency (FTAC 3 ) compresses models through task-relevant pruning while maintaining functional consistency on the sets across nodes. The empirical evaluations on standard benchmarks for CPS showed energy savings close to 60%, with a 30% drop in false-positive rates and 70% savings in communication overhead, all while maintaining a detection accuracy of over 93% Sets. This framework marks a huge leap forward in secure, intelligent, and autonomous intrusion detection across infrastructures and scenarios pertaining to next-generation 6G IoT CPS.
Flexible four-port MIMO antenna loaded with frequency selective surface for on-body applications
Manish Sharma, Dinesh Kumar Singh, Kanhaiya Sharma, Shashank Awasthi, Rana Gill, Tanweer Ali
Scientific Reports, 2025
This work reports a dual-band four-port MIMO antenna loaded with a 5 x 5 frequency-selective-surface (FSS) of size 72.5 mm×75.0 mm for gain enhancement. The antenna uses a novel radiating-patch and a modified rectangular-ground printed on a 0.254 mm thickness Rogers substrate, generating bandwidths of 4.04–6.64 GHz and 7.44–16.60 GHz with overall dimensions of 30.0 mm×30.0 mm. The FSS, which is printed on FR4 1.60 mm substrate, is placed below the antenna at a distance of 15.0 mm, which records a maximum peak-gain of 10.77 dBi. The characteristics-mode-analysis DWA (CMA DWA ) is simulated by subjecting the antenna to 10 modes, with Mode-2, Mode-4, Mode-5, Mode-6, Mode-7, and Mode-9 being the significant modes with modal significance values more than 0.707 and generating six resonance values at 6.67 GHz, 7.20 GHz, 7.958 GHz, 8.76 GHz, 9.20 GHz, and 11.80 GHz. The four identical radiating patches are arranged in orthogonal sequence, achieving spatial-diversity performance with ECC DWA ≤ 0.07 (Band-A), 0.03 (Band-B), DG DWA ≥ 9.70dB (Band-A), 9.85dB (Band-B), TARC DWA ≤ -5.0dB (Band-A), -2.50dB (Band-B), CCL DWA ≤ 0.38 b/s/Hz (Band-A), 0.30 b/s/Hz (Band-B), and the difference between the MEG DWA of two-ports to be $$\\cong$$ 0.0dB. The SAR DWA value corresponds to 0.158 W/Kg at 5.50 GHz, 0.076 W/Kg at 5.90 GHz, 0.0503 W/kg at 7.50 GHz, and 0.24 W/Kg at 10.0 GHz with conformal angles of 15 o , 30 o , and 45 0 , retaining the operational bandwidth.
A novel methodology for makeup invariant face recognition based on directional gradient and local derivative descriptors (DGLDD-FR)
Rajesh Kumar Tripathi, Subhash Chand Agrawal, Kanhaiya Sharma, Anand Nayyar
Journal of Supercomputing, 2025
Optimized Hybrid Features Enhance ML-Driven Defect Detection and Quality Control in Textiles
S. Phani Praveen, V. Esther Jyothi, Kanhaiya Sharma, S. Sindhura
Journal of the Textile Association, 2025
This work aims to investigate the utilization of ML algorithms to detect defects in textile production processes. Traditional textile inspections rely on manual methods, which are time-consuming, inconsistent, and prone to error. The research seeks to develop and evaluate MLalgorithms like SVM, CNN, Random Forest, and KNN to detect imperfections, including surface texture, color quality, and structural flaws. Special attention is paid to such factors as texture, color, and edge detection for their contribution to improving the accuracy of defect recognition. It is revealed that a higher accuracy rate of 96.7% is achievable when CNNs are applied with feature extraction methods, and they have better precision, recall, and F1-score measures. It also evaluates the enhancement in the quality control by presenting the difference in the production of defect- free fabric before and after the implementation of the ML-based system, where significant enhancements were observed. This study demonstrates a 10.3% increase in defect-free fabric rate after implementing the hybrid CNN-based ML model. These studies suggested that incorporating machine learning to improve the quality of textiles has the prospect of being a better approach than the traditional methods of detecting defects in textiles.
Experimental investigation of flexible eight-port asymmetric fed MIMO antenna with narrow-super-widebandn-s characteristics for future applications including internet of things
Manish Sharma, Selvaraj Praveen Chakkravarthy, Sathishkumar Nallusamy, Kanhaiya Sharma, Juliano Katrib, Rana Gill
Plos One, 2025
Enhancing Educational Quality: A Prototype System for Evaluating Faculty Performance in Higher Education
, Renuka Agrawal, Kanhaiya Sharma, and
Journal of Engineering Education Transformations, 2025
Modeling and simulation of an effectual triangular slotted UWB flexible antenna for breast cancer detection and healthcare monitoring
Aparna Singh, R. K. Dwivedi, Vinod Kumar Singh, Manish Sharma, Kanhaiya Sharma, Bulent Yilmaz
Plos One, 2025
Stable and Delay Efficient Routing Objective Function for Mobile IoT Networks
Soumya Nandan Mishra, Deepak Parashar, Dibya Nandan Mishra, Kanhaiya Sharma
2025 IEEE International Conference on Modern Electronics Devices and Intelligent Communication Systems Medcom 2025, 2025
Quantitative Evaluation of Smart Textile Adoption in Rural Weaving Communities using Machine Learning
S. Phani Praveen
Journal of the Textile Association, 2025
Design of a Multiband Patch Antenna and Analysis using Partial Ground for Wireless Applications
Gopika K, Allin Joe D, Sudipta Banerjee, Kanhaiya Sharma
Proceedings of 2025 1st International Conference on Radio Frequency Communication and Networks Rfcon 2025, 2025
A Multiband Slotted Patch Antenna for Wireless Applications
Pavin Prasanna Shanmugasundaram, Pranav Varshan R S, Allin Joe D, Sudipta Banerjee, Kanhaiya Sharma
Proceedings of 2025 1st International Conference on Radio Frequency Communication and Networks Rfcon 2025, 2025
Machine Learning Models for Early Detection of Cardiac Arrest Risk Factors
Shantanu Bindewari, Kanhaiya Sharma, Sesha Saisrikar Gaddam, Ananta Verma, Deepak Parashar, Mahesh Arse
2025 International Conference on Cognitive Computing in Engineering Communications Sciences and Biomedical Health Informatics Ic3ecsbhi 2025, 2025
Advancements in Suspicious and Violent Activity Recognition for Intelligent Video Surveillance
Niyati Rana, Deepak Parashar, Nilesh Bahadure, Hetal Jethani, Sudipta Banerjee, Kanhaiya Sharma
Icdt 2025 3rd International Conference on Disruptive Technologies, 2025
Design and Analysis of a Dual-Port MIMO Antenna for WLAN and n258 Bands with Flexible Configuration
Manish Sharma, Kanhaiya Sharma, Dinesh Kumar Singh, Gopal K, Amandeep Nagpal, Tamam Ali Abd Ulabbas Abedi
Icdt 2025 3rd International Conference on Disruptive Technologies, 2025
A slotted H shape patch antenna for C band communication
Allin Joe D, Sudipta Banerjee, Mahima Dharani N M, Kanhaiya Sharma, Nikithaa T, Rithika B
Proceedings of 2025 1st International Conference on Radio Frequency Communication and Networks Rfcon 2025, 2025
Dual Port Merged-Elliptical Patch with Tapered Ground Designed for Integrated Narrow WiMAX/C-band and Wideband Antenna for Multiple Wireless Applications
Manish Sharma, Kanhaiya Sharma, Rana Gill, Jayasheel Kumar Kalagatoori Archakam, Amandeep Nagpal, Ali Saeed
Icdt 2025 3rd International Conference on Disruptive Technologies, 2025
IntuitIQ - AI Driven Calculator
Himesh Bhattacharjee, Debolina Saha, Arup Roy, Priyanka Bal, Koushik Sarkar, Sudipta Banerjee, Kanhaiya Sharma
2025 IEEE International Conference on Modern Electronics Devices and Intelligent Communication Systems Medcom 2025, 2025
Adaptive and Scalable Cloud Data Sharing Framework with Quantum-Resistant Security, Decentralized Auditing, and Machine Learning-Based Threat Detection
P Raja Sekhar Reddy, Pulipati Srilatha, Kanhaiya Sharma, Sudipta Banerjee, Shailaja Salagrama, Manjusha Tomar, Ashwin Tomar
International Journal of Advanced Computer Science and Applications, 2025
Improved STNNet, A benchmark for detection, tracking, and counting crowds using Drones
Mohd Nazeer, Kanhaiya Sharma, S. Sathappan, Pulipati Srilatha, Arshad Ahmad Khan Mohammed
Methodsx, 2024
Corrigendum to “Improved STNNet, A benchmark for detection, tracking, and counting crowds using Drones” [MethodsX 13 (2024) 1-7/102820] (MethodsX (2024) 13, (S2215016124002735), (10.1016/j.mex.2024.102820))
Mohd Nazeer, Kanhaiya Sharma, S. Sathappan, Pulipati Srilatha, Arshad Ahmad Khan Mohammed
Methodsx, 2024
Flexible four-port MIMO antenna for 5G NR-FR2 tri-band mmWave application with SAR analysis
Manish Sharma, Bhaskara Rao Perli, Lovish Matta, Tathababu Addepalli, Kanhaiya Sharma, Fadi N. Sibai
Scientific Reports, 2024
Dual-Wide Multi-Band (DWMB) four-port flexible MIMO antenna for on-body multiple wireless applications including high diversity performance
Manish Sharma, Kanhaiya Sharma, Prabhakara Rao Kapula, Anand Nayyar, Muhammad Bilal
Plos One, 2024
Improved method for stress detection using bio-sensor technology and machine learning algorithms
Mohd Nazeer, Shailaja Salagrama, Pardeep Kumar, Kanhaiya Sharma, Deepak Parashar, Mohammed Qayyum, Gouri Patil
Methodsx, 2024
Predictive Modelling and Analysis of Cryptocurrency Daily Returns using Ensemble Model
Manasvi Mishra, Saloni Kulshrestha, Renuka Agrawal, Kanhaiya Sharma
2024 4th International Conference on Artificial Intelligence and Signal Processing Aisp 2024, 2024
Design and Analysis of Narrowband 28.0GHz n257 and n261 for Short Range Communication with Higher Data Rates
Manish Sharma, Kanhaiya Sharma, Ganga Prasad Pandey, Naresh Kumar, Rana Gill
2024 IEEE Wireless Antenna and Microwave Symposium Wams 2024, 2024
Big Data’s Ethical Frontier: Addressing Concerns in Data Acquisition and Analysis
Kanhaiya Sharma, Deepak Parashar, Vipin Tiwari, Siddhanth Shah, Sandeep Singh Rawat
Lecture Notes in Networks and Systems, 2024
Evaluating the Impact of Fuzzy Logic Controllers on the Efficiency of FCCUs: Simulation-Based Analysis
Harsh Pagare, Kushagra Mishra, Kanhaiya Sharma, Sandeep Singh Rawat, Shailaja Salagrama
International Journal of Advanced Computer Science and Applications, 2024
Dual-Band Four-Port MIMO Antenna Exclusively for K-Narrow and FR2-5G Applications
Manish Sharma, Naresh Kumar, Rana Gill, Kanhaiya Sharma, Dinesh Kumar Singh
2024 4th International Conference on Advancement in Electronics and Communication Engineering Aece 2024, 2024
Analyzing Twitter Data Using Apache Hive—A Big Data Technology Exploration
Kanhaiya Sharma, Mansi Kapshe, Parth Bhargava, Prakhar Trivedi, Sanika Changde, Om Mishra
Lecture Notes in Networks and Systems, 2024
A Four-Port Super-Wideband Monopole Antenna Including Multi-Band Wireless Applications for On-Body Applications with SAR Analysis
Manish Sharma, Kanhaiya Sharma, Ganga Prasad Pandey, Om Mishra, Samineni Peddakrishna, Rana Gill
2024 IEEE Wireless Antenna and Microwave Symposium Wams 2024, 2024
Malicious Website Detection Using Random Forest and Pearson Correlation for Effective Feature Selection
Esha Sangra, Renuka Agrawal, Pravin Ramesh Gundalwar, Kanhaiya Sharma, Divyansh Bangri, Debadrita Nandi
International Journal of Advanced Computer Science and Applications, 2024
Combining BERT and CNN for Sentiment Analysis A Case Study on COVID-19
Gunjan Kumar, Renuka Agrawal, Kanhaiya Sharma, Pravin Ramesh Gundalwar, Aqsa kazi, Pratyush Agrawal, Manjusha Tomar, Shailaja Salagrama
International Journal of Advanced Computer Science and Applications, 2024
Apache Spark for Analysis of Electronic Health Records: A Case Study of Diabetes Management
Kanhaiya Sharma, Deepak Parashar, Om Mengshetti, Raasha Ahmad, Rewaa Mital, Prerna Singh, Muskan Thawani
Revue D Intelligence Artificielle, 2023
A Deep Learning-Based Approach for Hand Sign Recognition Using CNN Architecture
Deepak Parashar, Sudhanshu Thakur, Kachapuram Basava Raju, Garine Bindu Madhavi, Kanhaiya Sharma
Revue D Intelligence Artificielle, 2023
Life Span Improvement of Bio Sensors Using Unsupervised Machine Learning for Wireless Body Area Sensor Network
Nazeer Mohd, Kanhaiya Sharma, Shailaja Salagrama, Renuka Agrawal, Harshal Patil
Revue D Intelligence Artificielle, 2023
Extreme Learning Machine: A Comprehensive Survey of Theories & Algorithms
Harshal Patil, Kanhaiya Sharma
Proceedings of International Conference on Computational Intelligence and Sustainable Engineering Solution Cises 2023, 2023
Computer-Aided Detection of Atrial Fibrillation Episodes from Electrocardiogram Signals Using Variational Mode Decomposition
Deepak Parashar, Kanhaiya Sharma
Proceedings of International Conference on Contemporary Computing and Informatics Ic3i 2023, 2023
Assessing the Landscape of Mobile Data Vulnerabilities: A Comprehensive Review
Harshal Patil, Kanhaiya Sharma
Proceedings of International Conference on Computational Intelligence and Sustainable Engineering Solution Cises 2023, 2023
Apache Spark in Riot Games: A Case Study on Data Processing and Analytics
Kanhaiya Sharma, Firdous Hussain Mohammad, Deepak Parashar
International Journal of Advanced Computer Science and Applications, 2023
Land Area Measurement System Design using Fastrax UP501 GPS Receiver Module
Deepak Parashar, Om Mishra, Kanhaiya Sharma, Dinesh Kumar Singh
Proceedings of International Conference on Computational Intelligence and Sustainable Engineering Solution Cises 2023, 2023
Drone-Based Predictive Structure Analysis: A Review
Trushant Patil, Durgesh Nandan, Kanhaiya Sharma, Deepak Parashar
2023 International Conference on Sustainable Emerging Innovations in Engineering and Technology Icseiet 2023, 2023
Efficient Routing Method based on Hybrid ACO Algorithm for Flying Ad-Hoc Networks
Deepak Parashar, Kanhaiya Sharma, Dinesh Kumar Singh, Yash Upendra Parkhi
2023 3rd International Conference on Advancement in Electronics and Communication Engineering Aece 2023, 2023
Improved Yoga Pose Detection Using MediaPipe and MoveNet in a Deep Learning Model
Deepak Parashar, Om Mishra, Kanhaiya Sharma, Amit Kukker
Revue D Intelligence Artificielle, 2023
2-D Empirical LP Wavelet Transform based Automated Framework for Glaucoma Screening
Deepak Parashar, Om Mishra, Kanhaiya Sharma, Shilpa Choudhary
2023 International Conference on Disruptive Technologies Icdt 2023, 2023
Towards the Applications of Generative Adversarial Networks Beyond Images
Renuka Agrawal, Kanhaiya Sharma, Sudhanshu Gonge, Rahul Joshi, Dinesh Kumar Singh
Proceedings of International Conference on Computational Intelligence and Sustainable Engineering Solution Cises 2023, 2023
A 5G-mmWave Four-Port MIMO Antenna Including High Diversity Performance in Narrow Bandwidth
Manish Sharma, Mohd Junedul Haque, Ganga Prasad Pandey, Kanhaiya Sharma, Dinesh Singh
2023 International Conference on Artificial Intelligence and Smart Communication Aisc 2023, 2023
A Miniaturized Polarization Independent Transparent FSS for Dual-Band Applications
Yogesh Solunke, Deven Patanvariya, Kanhaiya Sharma, Ashwin Kothari, Gaurav Chaitanya
2023 IEEE Microwaves Antennas and Propagation Conference Mapcon 2023, 2023
Enhancing Online Job Posting Security: A Big Data Approach to Fraud Detection
Kanhaiya Sharma, Deepak Parashar, Akshay Sangwan, Abhinav Vengali, Gouransh Agrawal
Proceedings International Carnahan Conference on Security Technology, 2023
Operating Frequency Prediction of Annular Ring Microstrip Antenna Using Extreme Learning Machine
Kanhaiya Sharma, Ganga Prasad Pandey, Dinesh Kumar Singh, Krishna Chaya Addagarrala, Shailaja Salagrama
Lecture Notes in Electrical Engineering, 2023
Design of Dual-Band Pass Polarization Insensitive and Transparent Frequency Selective Surface for Wireless Applications
Yogesh Solunke, Deven G Patanvariya, Ashwin Kothari, Kanhaiya Sharma, Dinesh Singh
2023 International Conference on Artificial Intelligence and Smart Communication Aisc 2023, 2023
A Circular Patch with Rectangular-Slotted Ground Super-Wideband Two-Port MIMO Antenna for Multiple Wireless Applications
Manish Sharma, Kanhaiya Sharma, Rana Gill, Shailaja Salagrama, Ganga Prasad Pandey
2nd International Conference on Wireless Antenna and Microwave Symposium Wams 2023, 2023
An Automatic Framework for Number Plate Detection using OCR and Deep Learning Approach
Yash Shambharkar, Shailaja Salagrama, Kanhaiya Sharma, Om Mishra, Deepak Parashar
International Journal of Advanced Computer Science and Applications, 2023
Analyzing User Behavior in Social Media through Big Data Analytics
Aman Yadav, Murhib Alahmar, Aniket Singh, Kanhaiya Sharma, Renuka Agrawal, Chandra Bhushan Sharma
3rd IEEE International Conference on ICT in Business Industry and Government Ictbig 2023, 2023
Real-Time Data Analysis, Significance, Architectures and Applications for Informed Decision-Making
Kanhaiya Sharma, Bindu G. Madhavi, Ankur Goyal, Deepak Parashar, Lalit Shrotriya, Anurag Gupta
2023 International Conference on Power Energy Environment and Intelligent Control Peeic 2023, 2023
Retail Transformation Through Big Data in Inventory Control and Consumer Analytics
Shikhar Srivastava, Kaustubh Mani Tripathi, Kanhaiya Sharma, Renuka Agarwal, Upamanyu Wable, Pratik Gaikwad
3rd IEEE International Conference on ICT in Business Industry and Government Ictbig 2023, 2023
Mitigating Malware Attacks using Machine Learning: A Review
Mahesh Arse, Kanhaiya Sharma, Shantanu Bindewari, Ashwin Tomar, Harshal Patil, Neha Jha
2023 International Conference on Artificial Intelligence and Smart Communication Aisc 2023, 2023
State of-the-Art Analysis of Multiple Object Detection Techniques using Deep Learning
Kanhaiya Sharma, Sandeep Singh Rawat, Deepak Parashar, Shivam Sharma
International Journal of Advanced Computer Science and Applications, 2023
A Multiple Millimeter-Wave 5G MIMO Antenna Including n257/n258/n259/n260/n261 FR2-Bands with High Diversity Performance
Manish Sharma, Kanhaiya Sharma, Smrity Dwivedi, Dinesh Kumar Singh, Rana Gill, Naresh Kumar
2023 International Conference on Power Energy Environment and Intelligent Control Peeic 2023, 2023
A Super-Wideband Multiband MIMO Antenna for Microwave and Millimeter Wave Wireless Applications
Manish Sharma, Kanhaiya Sharma, Rana Gill, Ganga Prasad Pandey, Shailaja Salagrama, Om Mishra
2nd International Conference on Wireless Antenna and Microwave Symposium Wams 2023, 2023
Black Friday Sales Prediction using Supervised Machine Learning
Shambhavi Patil, Om Nankar, Renuka Agrawal, Kanhaiya Sharma, Shashank Awasthi, Neha Jha
2023 International Conference on Artificial Intelligence and Smart Communication Aisc 2023, 2023
Apache Spark in Healthcare: Advancing Data-Driven Innovations and Better Patient Care
Lalit Shrotriya, Kanhaiya Sharma, Deepak Parashar, Kushagra Mishra, Sandeep Singh Rawat, Harsh Pagare
International Journal of Advanced Computer Science and Applications, 2023
A Tri-Band Quad Feed MIMOTB Antenna with Two-Narrow in Ku-K Bands and n259/n260 5G-mmWave FR2-Bands
Manish Sharma, Kanhaiya Sharma, Ganga Prasad Pandey, Naresh Kumar, Rana Gill, Deepak Parashar
2023 IEEE Microwaves Antennas and Propagation Conference Mapcon 2023, 2023
The Role of Big Data Analytics in Enhancing Customer Interactions and Loyalty
Kanhaiya Sharma, Deepak Parashar, Kanchapuram BasavaRaju, Dinesh Kumar Singh, Aqsa Kazi, Gunjan Kumar
2023 International Conference on Power Energy Environment and Intelligent Control Peeic 2023, 2023
Miniaturized Quad-Port Conformal Multi-Band (QPC-MB) MIMO Antenna for On-Body Wireless Systems in Microwave-Millimeter Bands
Manish Sharma, Prabhakara Rao Kapula, Shailaja Salagrama, Kanhaiya Sharma, Ganga Prasad Pandey, Dinesh Kumar Singh, Milind Mahajan, Anupma Gupta
IEEE Access, 2023
Ultrawideband printed monopole antenna for X band and 5G applications
Shailaja Salagrama, Suresh Kumar Pittala, Kanhaiya Sharma, Ganga Prasad Pandey, Nilesh Kumar U. Pande, Dinesh Kumar Singh
Proceedings of International Conference on Computational Intelligence and Sustainable Engineering Solution Cises 2022, 2022
Real time social distance detection using Deep Learning
Shailaja Salagrama, H. Kumar, R. Nikitha, G. Prasanna, Kanhaiyalal Sharma, Shashank Awasthi
Proceedings of International Conference on Computational Intelligence and Sustainable Engineering Solution Cises 2022, 2022
Pattern Correction in Planar Antenna Array using Multi-Objective Particle Swarm Optimization and Continuous Excitation
Kanhaiya Sharma, Ganga Prasad Pandey
Iranian Journal of Science and Technology Transactions of Electrical Engineering, 2021
Efficient modelling of compact microstrip antenna using machine learning
Kanhaiya Sharma, Ganga Prasad Pandey
AEU International Journal of Electronics and Communications, 2021
Efficient isolation modelling for two-port mimo antenna by gaussian process regression
Kanhaiya Sharma, Ganga Prasad Pandey
Progress in Electromagnetics Research C, 2021
Assessment of indoor-outdoor Wi-fi radiation on human body and its precise SAR measurement
Indian Journal of Pure and Applied Physics, 2021
Resonant Frequency Prediction of Patch Antenna in the Presence of Inserted Airgap Using Machine Learning
Mahima Soni, Kanhaiya Sharma, Ganga Prasad Pandey, Surendra K Gupta
Lecture Notes in Electrical Engineering, 2021
Fractal EBG Based Two Port Isolation Improvement in Compact MIMO Antenna
Kanhaiya Sharma, Ganga Prasad Pandey
Proceedings of the 2020 IEEE International Conference on Computational Electromagnetics Iccem 2020, 2020
Two port compact mimo antenna for ism band applications
Kanhaiya Sharma, Ganga Prasad Pandey
Progress in Electromagnetics Research C, 2020
Designing a compact microstrip antenna using the machine learning approach
Kanhaiya Sharma, Ganga Prasad Pandey
Journal of Telecommunications and Information Technology, 2020
Prediction of the resonant frequency of square patch microstrip antenna with DGS using Machine Learning
Kanhaiya Sharma, Ganga Prasad Pandey
2019 IEEE Indian Conference on Antennas and Propagation Incap 2019, 2019
A novel wideband 5G antenna with truncated ground
Kanhaiya Sharma, Ganga Prasad Pandey
2019 IEEE Indian Conference on Antennas and Propagation Incap 2019, 2019
Predicting resonant frequency of annular ring compact microstrip antenna using various machine learning techniques
Kanhaiya Sharma, Ganga Prasad Pandey
2019 IEEE 16th India Council International Conference Indicon 2019 Symposium Proceedings, 2019

Dr Kanhaiya Sharma

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