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A hybrid channel transmission method for effective spectrum access in cognitive radio networks

International Journal of Ad Hoc and Ubiquitous Computing 2023年第4期44卷 221-232页

作者： Dhurandher, Sanjay Kumar Kumar, Bhoopendra Woungang, Isaac Chao, Han-Chieh Department of Information Technology Netaji Subhas University of Technology New Delhi India National Institute of Electronics and Information Technology Delhi Centre New Delhi India CAITFS Division of Information Technology Netaji Subhas Institute of Technology University of Delhi Delhi India KIET Group of Institutions Delhi-NCR Ghaziabad India Department of Computer Science Ryerson University TorontoON Canada Department of Electrical Engineering National Dong Hwa University Hualien Taiwan Department of Computer Science and Information Engineering National Ilan University I-Lan Taiwan Institute of Computer Science and Innovation UCSI University Kuala Lumpur Malaysia

Cognitive radio is considered as a very influencing technique and has the great potential to solve the problem of shortage of the radio spectrum. A secondary user also called as a cognitive user may access the primary channel(s) with a certain restrictions in a cognitive radio network. Spectrum sensing is a very important activity during the process of spectrum access. This paper proposes a Markov chain-based proactive hybrid spectrum access method for maximising the radio spectrum usage by optimising the channel sensing process and establishing the channel switching conditions. Using the OMNeT++ simulator, the proposed method (proactive hybrid access) is compared against that of the conventional (reactive hybrid access), overlay, and underlay access methods, showing its superiority by about 20% in terms of performance improvement with respect to throughput, service delay, and PU detection probability. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Cognitive radio

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Performance Assessment of Fuzzy Logic Control Approach for MR-Damper Based-Transfemoral Prosthetic Leg

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第1期3卷 53-66页

作者： Sharma, Richa Gaur, Prerna Bhatt, Shaurya Joshi, Deepak Centre for Biomedical Engineering Indian Institute of Technology Delhi New Delhi India Division of Instrumentation and Control Engineering Netaji Subhas University of Technology New Delhi India

Transfemoral amputation commonly occurs due to some stroke, diabetes, physical or mental trauma, which reduces the person's movement capability. Therefore, an efficient prosthetic leg is essential to improve the life of an amputee by replacing the lost limb. This letter addresses the fuzzy logic-based control strategy for magneto-rheological damper based prosthetic leg for transfemoral amputees. The primary focus of this letter is to present the performance analysis of the control to achieve the entire gait cycle (both swing and stance phases) for a transfemoral prosthesis. The performance and robustness analysis of the developed prosthetic leg is tested for real-time gait data to obtain the precise and more natural gait by the transfemoral amputee. © 2020 IEEE.

关键词： Fuzzy logic

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Heart Disease Prediction Evaluation of Machine Learning Models with PSO-Optimized K-Fold Cross-Validation

Heart Disease Prediction Evaluation of Machine Learning Mode...

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Gupta, Nikita Jadon, Kuldeep Singh Soni, Preeti Gupta, Nitin Dhurandher, Sanjay Kumar Department of Computer Science & Engineering India Netaji Subhas University of Technology Department of Information Technology New Delhi India National Institute of Electronics and Information Technology New Delhi India

ISBN: (纸本)9798350364866

Heart disease prediction is a critical aspect of modern healthcare and demands accurate and efficient models for timely diagnosis. Machine learning models in heart disease prediction analyze patient data, with cross-validation ensuring reliable performance, aiding model selection, and optimizing parameters for enhanced accuracy. The k value in cross-validation determines the number of folds into which the dataset is divided for training and validation. In the proposed work, optimization of the k-value in cross-validation is done using Particle Swarm Optimization (PSO), which is utilized to improve the evaluation of the predictive accuracy of various models. PSO dynamically adjusts the k-value, balancing computational efficiency with accurate model evaluation. The result shows that among various ML models, Random Forest, with an 86.95% mean accuracy, emerges as the preferred choice, offering reliable predictions for improved diagnostics. © 2024 IEEE.

关键词： Prediction models

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Path Texture Detection for Visually Impaired using Transfer Learning 3

Path Texture Detection for Visually Impaired using Transfer ...

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3rd International Conference on Secure Cyber Computing and Communications, ICSCCC 2023

作者： Manjari, Kanak Animesh Verma, Madhushi Singal, Gaurav Bennett University Computer Science Engineering Gr. Noida India National Institute of Technology Computer Science & Engineering Delhi India Netaji Subhas University of Technology Computer Science Engineering India

ISBN: (纸本)9798350300710

The computer vision technique known as object detection is used to determine the location of an object in an image or video. Recent advances in a number of areas, including computer vision, speech recognition, and natural language processing have been made using deep learning approaches. Convolution neural networks (CNNs), which have several layers, are used to detect objects. A deep learning model that uses CNN to identify various surface regions has been suggested. The study of tracks, walkways, playgrounds, highways, and muddy areas is a pertinent and significant aspect of object detection for Visually Impaired (VI) individuals. In addition to adding indirect vision to the blind individual, this model recognizes surface regions and alerts them using text-to-sound conversion API. In this study, transfer learning was carried out utilizing Faster RCNN (Region Based Convolutional Neural Networks) with Inception V2 and ResNet50 model weights. The accuracy of both trained models was the same, while Faster RCNN Inception V2 had the lowest loss (0.02). © 2023 IEEE.

关键词： Object detection

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PDNet: A Novel Approach to Plant Disease Detection using CNN and Keras 6

PDNet: A Novel Approach to Plant Disease Detection using CNN...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Jain, Garima Shukla, Amita Chaudhary, Anamika Sharma, Nidhi Bisht, Sovers Singh Jain, Ankush Department of Computer Science and Buisness Systems Noida Institute of Engineering and Technology Greater Noida India Department of Data Science Noida Institute of Engineering and Technology Greater Noida India Department of Data Science Noida Institute of Engineering and Technology Delhi India Department of Computer Science and Engineering Netaji Subhas University of Technology Delhi India

ISBN: (纸本)9798331522667

This research addresses the pressing global demand for food by leveraging cutting-edge deep learning techniques for automating plant disease detection. Focusing on tomato and potato leaf diseases, the study utilized the PlantVillage dataset to compare classical machine learning methods, such as Local Binary Patterns (LBP) combined with classifiers like kNN, SVM, and Extreme Learning Machines, against modern Convolutional Neural Network (CNN) architectures. These included MobileNet, DenseNet201, ResNet50, Inception V3, and VGG16/19. A custom-designed CNN model was developed with unique configurations implemented using Keras and TensorFlow. The model was tested for binary, 6-class, and 10-class classifications, achieving remarkable accuracy rates of 99.6%, 98.4%, and 97.2%, respectively. Notably, after 50 training epochs, the model achieved a peak accuracy of 99.78% for plant disease classification. A practical web-based application was also developed, integrating the best-performing model to facilitate real-time leaf disease detection and notify farmers via email. This approach demonstrates a significant advancement in sustainable agriculture by providing an efficient, accurate, and scalable solution for early disease detection, which is critical for improving crop yield and productivity. © 2025 IEEE.

关键词： Convolutional neural networks

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Asymmetric Dual-Hop NOMA based Communication System with Practical Constraints: Imperfect CSI, HWI and ISIC 9

Asymmetric Dual-Hop NOMA based Communication System with Pra...

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9th International Conference on Signal Processing and Communication, ICSC 2023

作者： Khirwar, Isha Gupta, Akash Verma, Bharat Garg, Parul ECE Department The LNM Institute of Information Technology Jaipur India Division of Electronics and Communication Engineering Netaji Subhas University of Technology New Delhi India

ISBN: (纸本)9798350383201

In this paper, we present an asymmetric dual-hop decode-and-forward relay-based Non-Orthogonal Multiple Access (NOMA) based communication system. The source-to-relay link is characterized by η − µ distribution, while the relay to end users is characterized by Rayleigh distribution. Further, we have incorporated hardware impairments (HWI), and channel state information (CSI) errors due to imperfect successive interference cancellation (SIC), at the relay and end users. The mathematical framework for outage probability (OP) is being derived, and the analytical results are verified using Monte Carlo simulation results. © 2023 IEEE.

关键词： Channel state information

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Experimental investigations on finishing of a brass specimen by magneto-rheological honing technique

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Journal of Micromanufacturing 2022年第2期5卷 171-180页

作者： Kumari, Chinu Chak, Sanjay Kumar Division of Manufacturing Processes and Automation Engineering Netaji Subhas Institute of Technology (NSIT) Delhi New Delhi India

Magneto-rheological abrasive honing (MRAH) is an unconventional surface finishing technique that relies on abrasives mixed with a unique finishing fluid, which changes its characteristics on magnetic field application. This process imparts nanometric-level surface finish with a significant amount of uniformity. Rotating motion of the workpiece and continuous reciprocation of the finishing fluid in the MRAH process are recognized as the major aspects for adopting this process in finishing non-magnetic materials. The finishing obtained through the MRAH process relies on the workpiece’s material properties and process parameters such as concentration of abrasives in finishing fluid, rotational speed of the workpiece, and magnetic field strength/magnetizing current. To study the efficacy of MRAH process, a parametric study was conducted by performing few experiments on a brass workpiece. Design of experiment approach was adopted to plan the experiments, and the effect of different values of magnetizing current, the concentration of abrasives, and rotational speed on the surface finish were analyzed through the application of analysis of variance (ANOVA). From ANOVA, the rotational speed was found as the most significant parameter with a contribution of 48.90% on % reduction in roughness value (%∇R_a). Around 57% of roughness reduction was obtained at the optimized value of process parameters. © 2021 SAGE Publications.

关键词： abrasives finishing honing Magneto-rheological (MR) fluid roughness

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An optimized classification of apps reviews for improving requirement engineering

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Recent Advances in computer Science and Communications 2021年第5期14卷 1390-1399页

作者： Bhatia, M.P.S. Kumar, Akshi Beniwal, Rohit Division of Computer Engineering Netaji Subhas Institute of Technology Delhi India Department of Computer Science & Engineering Delhi Technological University Delhi India

Background: The App Stores, for example, Google Play and Apple Play Store provide a platform that allows users to provide feedback on the apps in the form of reviews. An app review typically includes star rating followed by a comment. Recent studies have shown that these reviews possess a vital source of information that can be used by the app developers and the vendors for improving the future versions of an app. However, in most of the cases, these reviews are present in unstructured form and extracting useful information from them requires a great effort. Objective: This article provides an optimized classification approach that automatically classifies the reviews into a bug report, feature request, and shortcoming & improvement request relevant to Requirement engineering. Methods: Our methodology used supervised Machine Learning techniques (Multinomial Naive Bayes, Linear SVC, CART Decision Tree) that have been first evaluated based on t h e conven-tional Bag of Words (BOW) feature extraction model along with other auxiliary features such as review rating, review length, review tense, and review sentiment score to automatically classify app reviews into their relevant categories, and subsequently, a feature selection method is used to provide an optimized classification approach for improving classifier performance using Particle Swarm Optimization (PSO) nature-inspired algorithm. Results: Result shows that we achieved best results with precision of 62.0 % and recall of 47.3 % with Linear SVC Machine Learning technique, which we further optimized with PSO nature-inspired algorithm, i.e., with PSO + Linear SVC, thus, resulting in a precision of 63.6 % and recall of 55.0 %. Conclusion: This optimized automatic classification improves the Requirement engineering where developer straightforwardly knows what to improve further in the concerned app. © 2021, Bentham Science Publishers. All rights reserved.

关键词： Sentiment analysis

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Framework for Domain Independent Hybrid Recommender Systems: Addressing Challenges of Imperfect Data

Framework for Domain Independent Hybrid Recommender Systems:...

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Information technology, Electronics and Intelligent Communication Systems (ICITEICS), IEEE International Conference on

作者： Amey Vibhute Department of Computer Engineering Netaji Subhas Institute of Technology Delhi India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

Recommender systems are an indispensable aspectof numerous contemporary online platforms, aiding users inuncovering new and pertinent content. However, both content-basedfiltering (CBF) and collaborative filtering (CF) systemsexhibit distinct strengths and limitations. CBF systems typicallyexcel at addressing cold start issues but their recommendationsmay falter when metadata is incomplete or absent. CF systems,on the other hand, can provide superior recommendationquality when ample data is accessible, but they may fail wheninsufficient data is available. To mitigate these limitations, thispaper puts forward a hybrid recommender system that harnessesthe strengths of both CBF and CF methodologies. Our systemassigns confidence scores to both models and dynamically assignsweights based on those scores to generate the most advantageousrecommendations. This approach empowers us to circumventthe drawbacks of each model and capitalize on their respectivestrengths. Our framework has been evaluated on a vast dataset ofuser ratings and movie metadata. The findings demonstrate thatour hybrid system outshines both CBF and CF systems in termsof recommendation quality, particularly for cold start users andunpopular items. Notably, our hybrid system empowers users tofine-tune the balance between the two models using a handfulof parameters which allows users to tailor the recommendationengine to their specific data and preferences.

关键词： Communication systems Collaborative filtering Metadata Motion pictures Information filters Information technology Recommender systems

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DARCVAA: A Deep Neural Networks Based Framework for Detecting Adverse Reactions of COVID-19 Vaccines and Association Analysis

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SN computer Science 2024年第5期5卷 622页

作者： Sinruwng, Ngamwal Yogita Pal, Vipin Verma, Anshul Department of Computer Science and Engineering National Institute of Technology Meghalaya Meghalaya Shillong 793003 India Department of Computer Science and Engineering National Institute of Technology Kurukshetra Haryana Kurukshetra 136119 India Department of Computer Science and Engineering Netaji Subhas University of Technology New Delhi New Delhi 110078 India Department of Computer Science Banaras Hindu University UP Varanasi 221005 India

In response to the global COronaVIrus Disease of 2019 (COVID-19) pandemic, widespread vaccination campaigns were initiated worldwide. To minimise Adverse Drug Reactions (ADRs) linked to the vaccines, extensive pre-clinical trials and post-marketing surveillance activities were undertaken in a traditional setting. However, there has been a limited exploration of social media data for this purpose. As social media data is composed of real-time user experiences on vaccines, its analysis is pivotal for understanding vaccine safety. This work proposes a framework named Detecting Adverse Reactions of COVID-19 Vaccines and Association Analysis (DARCVAA). It employs four Deep Neural Networks (DNN) based classification models to detect ADRs of COVID-19 vaccines from Reddit’s posts. Further, it uses the Apriori algorithm to extract associations between vaccines and ADRs to comprehensively understand their relationships. The statistical significance of the extracted associations has been evaluated in terms of support and confidence. The proposed framework has been applied to a dataset collected from Reddit’s platform from September 2020 to July 2021 and annotated with the help of a medical expert. The experimental results showed that the proposed framework has outperformed six state-of-the-art detection models, which include Bidirectional Encoder Representations from Transformers (BERT), Bidirectional Encoder Representations from Transformers for Biomedical Text Mining (BioBERT), Vaccine Adverse Events-Mine (VAEM-Mine), Vaccine Adverse Events (VAE), Support Vector Machines (SVM) and Naıve Bayes (NB) in respect to precision, recall, F1-score and accuracy. The identified ADRs have been validated from the official surveillance reports, and the validation results have proven the efficacy of the proposed framework toward ADR detection and capturing possibly emerging ADRs. The analysis of detected ADRs in terms of frequent ADRs, ADRs unique to vaccines and genders and associations o

关键词： Adverse reactions of vaccines BioBERT COVID-19 Deep neural networks Social media

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