Papers by Nasib Singh Gill
Object detection and classification from compressed video streams
Expert Systems, Jun 28, 2023
Gray scale image denoising technique using regression based residual learning
Multimedia Tools and Applications, May 18, 2023
Libraries generate, accumulate, and disseminate information to its readers and users in the desir... more Libraries generate, accumulate, and disseminate information to its readers and users in the desired format and at the desired location. The process of using library data more effectively begins by discovering ways to connect the sources of data created by Libraries. The concept of data warehousing and data mining can assist library management in making decisions and setting policies, and can assist the library's parent organization or community in understanding the information needs of its members. Data mining is the process of analyzing data from different perspectives and summarizing it into useful information. Data mining provides knowledge by uncovering valuable information that can be used for various purposes in different areas of applications. Using various techniques of data mining such as association rules, decision trees, and cross tabulation can help discover this unused information. The scope of this paper is to discuss the role and importance of data warehousing and data mining in making the libraries of the future. This paper first discusses the concept of data warehousing, its characteristics, and its uses. Further, the paper also discusses the concept of data mining and its use in library and information services.
International journal of electrical and computer engineering systems, Jul 15, 2022
Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus and popula... more Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus and population vulnerability increased all over the world due to lack of effective remedial measures. Nowadays vaccines are available; but in India, only 18.8% population has been fully vaccinated till now. Therefore, social distancing is only precautionary norm to avoid the spreading of this deadly virus. The risk of virus spread can be avoided by adhering to this norm. The main objective of this work is to provide a framework for tracking social distancing violations among people. This paper proposes a deep learning platform-based Smart Social Distancing Tracker (SSDT) model which is trained on MOT (Multiple Object Tracking) datasets. The proposed model is a hybrid approach that is a combination of YOLOv4 as object detection model merged with MF-SORT, Kalman Filter and brute force feature matching technique to distinguish people from background and provide a bounding box around these. Further, the results are also compared with another model, namely, Faster-RCNN in terms of FPS (frames per second), mAP(mean Average Precision) and training time over the dataset. The results show that the proposed model provides better and more balanced results. The experiment has been carried out in challenging conditions including, occlusion and under lighting variations with mAP of 97% and a real-time speed of 24 fps. The datasets provide numerous classes and from all the classes of objects, only people class has been used for identifying people in a closet. The ultimate goal of the model is to provide a tracking solution that will be helpful for different authorities to redesigning the layout of public places and reducing the risk. This model is also helpful in computing the distance between two people in an image and the results confirm that the proposed model successfully distinguishes between individuals who walk too close or breach the social distancing norms.
Artificial Neural Network for the Internet of Things Security
International journal of engineering trends and technology, Nov 25, 2020
International Journal of Advanced Research in Computer Science, Jun 20, 2017
Travelling Salesperson Problem (TSP) is one of the leading problems that are considered as an NP-... more Travelling Salesperson Problem (TSP) is one of the leading problems that are considered as an NP-hard. To tackle with this problem we don't have any best suitable algorithm that solves it in polynomial time. Although we have certain algorithms that gave better results. This paper reviews the heuristic algorithms which are used to solve the problem & account for optimal solution in case of smaller problem size & give sub-optimal solution for bigger problem size. A survey for each & every strategy used for solving TSP i.e. how they are modified with time & corresponding results obtained as per the modification. Here we take into account well recognized heuristic algorithms which are genetic algorithm, ant colony optimization, particle swarm optimization.
International journal of innovative technology and exploring engineering, Feb 28, 2020
IoT (Internet of Thing) is becoming ubiquitous day by day and making dumb devices smarter by enab... more IoT (Internet of Thing) is becoming ubiquitous day by day and making dumb devices smarter by enabling them to transfer the information over the network. IoT not confined to homes or in utilities but can be found in array of fields. IoT is rapidly making the world smarter by connecting physical to digital world and it is estimated that by 2024 more than 20 billion devices are likely to be connected. It brings opportunity but also brings numerous kind of risks. The worry is how we to keep billions of devices secure and what to ensure the security of networks these run on. The present paper focused on all the issues concerning about securing IoT environment and how machine learning techniques may help to address these security issues. The paper also discusses the proposed approaches, parameters, characteristic of techniques and explores which technique could be more effective.
Comparative Analysis of Machine Learning Algorithms for Securing IoT Enabled Environment
2022 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Nov 4, 2022
International Journal of Advanced Computer Science and Applications, 2022
The growing number of Internet of Things (IoT) objects and the operational and security challenge... more The growing number of Internet of Things (IoT) objects and the operational and security challenges in IoT systems are encouraging researchers to design suitable IoT architecture. Enormous data generated in the IoT environment face several kinds of security and privacy challenges. IoT system generally suffers from several issues like data storage, safety, privacy, integrity, transparency, trust, and single point of failure. IoT environment is emerging with several solutions to resolve these problems. The main objective of this paper is to design a cloud-blockchain-based secure IoT architecture that provides advanced and efficient storage and security solutions to IoT ecosystem. Blockchain technology appears to be a decent choice to resolve such kinds of problems. Blockchain technology uses a hash-based cryptographic technique for information security and integrity. Cloud computing provides advanced storage solutions with several remote services to store, compute and analyze the data. The proposed IoT architecture is based on the integration of cloud and blockchain services, which aim to provide transparent, decentralized, and trustworthy and secure storage solutions. In addition to the standard layers (perception layer, network layer, processing layer, and application layer) the proposed IoT architecture in the present paper includes a service layer, a security layer, and a parallel management and control layer, which focus on the security and management of the entire IoT infrastructure.
International Journal of Advanced Computer Science and Applications, 2022
The extensive use of Internet of Things (IoT) appliances has greatly contributed in the growth of... more The extensive use of Internet of Things (IoT) appliances has greatly contributed in the growth of smart cities. Moreover, the smart city deploys IoT-enabled applications, communications, and technologies to improve the quality of life, people's wellbeing, quality of services for the service providers and increase the operational efficiency. Nevertheless, the expansion of smart city network has become the utmost hazard due to increased cyber security attacks and threats. Consequently, it is more significant to develop the system models for preventing the attacks and also to protect the IoT devices from hazards. This paper aims to present a novel deep hybrid attack detection method. The input data is subjected for preprocessing phase. Here, data normalization process is carried out. From the preprocessed data, the statistical and higher order statistical features are extracted. Finally, the extracted features are subjected to hybrid deep learning model for detecting the presence of attack. The proposed hybrid classifier combines the models like Convolution Neural Network (CNN) and Deep Belief Network (DBN). To make the detection more precise and accurate, the training of CNN and DBN is carried out by using Seagull Adopted Elephant Herding optimization (SAEHO) model by tuning the optimal weights.
A novel finetuned YOLOv6 transfer learning model for real-time object detection
Journal of Real-time Image Processing, Apr 10, 2023
Indian journal of computer science and engineering, Oct 20, 2021
Twitter is an important source of information but it is challenging to analyze this data in order... more Twitter is an important source of information but it is challenging to analyze this data in order to recover meaningful inference. The present paper uses topic modelling and sentiment analysis to draw useful context from Twitter data set related to 'Clean India Mission'. Latent Dirichlet Allocation is used in the research to identify twenty most trending topics and top seven terms related to each of the twenty topics. Coherence and prevalence values represent model efficiency. Topic clustering is also used in the research to identify how strongly topics are related to each other. Five different clusters are created from the top trending topics reflecting different aspects in the corpus. The average silhouette width is employed to determine the optimal number of clusters. Lexicon based classification using 'nrc' sentiment directory is also used to reflect people's sentiment at ten different sentiment levels for the mission. Twitter data for the research is collected from seven different Hashtags, including the official page of the clean India campaign. The most relevant subject segments are identified after evaluating the trending topics by utilizing topic coherence value.
International journal of computer applications, Dec 18, 2012
In AORE, a conflict occurs when two or more crosscutting concerns i.e aspects having the same pri... more In AORE, a conflict occurs when two or more crosscutting concerns i.e aspects having the same priority contribute negatively to each other, need to be composed in the same match point. Conflict resolution is a process that establishes a critical trade-off among such kind of aspects. So, the conflict resolution process is a compulsory process and need to achieve it. Over the last few years, several research efforts have been devoted to resolve conflict in AORE but, still a lot of work is needed. The use of fuzzy logic to conflict resolution is an emerging area that will incorporate one domain in other. In this paper, an attempt is made to apply fuzzy logic for the conflict resolution in AORE.
International journal of computer theory and engineering, 2020
Twitter sentiment analysis has been explored in various domains including Business reviews, Polit... more Twitter sentiment analysis has been explored in various domains including Business reviews, Political forecasting, decision support, Movie reviews and many more. The nature of data collected by Twitter imposes several challenges for sentiment analysis. There are other factors also like the selected classifier, multiclass sentiment analysis, feature selection method, number of feature selected, level of preprocessing, preprocessing techniques involved that can affect the accuracy of classification. This paper discusses various factors affecting the accuracy of Twitter sentiment analysis. Consideration of these factors can be very beneficial while designing an efficient classification model for twitter sentiment analysis. The survey also focuses on various metrics used for representation of sentiment analysis result and their relevance.
Journal of High Speed Networks, Mar 31, 2020
In this paper, a self-observation and recommendation based trust model with defense scheme to fil... more In this paper, a self-observation and recommendation based trust model with defense scheme to filter dishonest recommendations is proposed and analyzed to reduce the impact of false positive and false negative attacks in WANET. The Bayesian statistical approach is used to compute direct and indirect trust values. The confidence value assures the maturity of the interactive relationship between the trust evaluating node and the evaluated node. The defense scheme filters the received recommendations by comparing them with the node's own opinion and in case of no interaction with the evaluated node, it compares with a trusted neighbor's opinion. The neighbor nodes are tagged into three categories: Trusted neighbor, untrusted neighbor and unknown neighbor according to the overall similarity score. The defense scheme only accepts the recommendations from trusted and unknown neighbors. The proposed trust model is simulated and the results show that the model is capable of mitigating the influence of badmouthing and ballot-stuffing attacks.
Sustainability, Jan 25, 2023
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Flow-MotionNet: A neural network based video compression architecture
Multimedia Tools and Applications, Aug 10, 2022
Complexity, Aug 3, 2022
e stability of the power grid is concernment due to the high demand and supply to smart cities, h... more e stability of the power grid is concernment due to the high demand and supply to smart cities, homes, factories, and so on. Different machine learning (ML) and deep learning (DL) models can be used to tackle the problem of stability prediction for the energy grid. is study elaborates on the necessity of IoT technology to make energy grid networks smart. Different prediction models, namely, logistic regression, naïve Bayes, decision tree, support vector machine, random forest, XGBoost, k-nearest neighbor, and optimized artificial neural network (ANN), have been applied on openly available smart energy grid datasets to predict their stability. e present article uses metrics such as accuracy, precision, recall, f1-score, and ROC curve to compare different predictive models. Data augmentation and feature scaling have been applied to the dataset to get better results. e augmented dataset provides better results as compared with the normal dataset. is study concludes that the deep learning predictive model ANN optimized with Adam optimizer provides better results than other predictive models. e ANN model provides 97.27% accuracy, 96.79% precision, 95.67% recall, and 96.22% F1 score.
International journal of computer applications, Jul 28, 2012
Financial statement fraud has reached the epidemic proportion globally. Recently, financial state... more Financial statement fraud has reached the epidemic proportion globally. Recently, financial statement fraud has dominated the corporate news causing debacle at number of companies worldwide. In the wake of failure of many organisations, there is a dire need of prevention and detection of financial statement fraud. Prevention of financial statement fraud is a measure to stop its occurrence initially whereas detection means the identification of such fraud as soon as possible. Fraud detection is required only if prevention has failed. Therefore, a continuous fraud detection mechanism should be in place because management may be unaware about the failure of prevention mechanism. In this paper we propose a data mining framework for prevention and detection of financial statement fraud.
Metaverse and Its Impact on Climate Change
Springer eBooks, 2023
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Papers by Nasib Singh Gill