Browsing by Author "Singh, Parvinder"

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Bell�s inequality with biased experimental settings
(Springer, 2022-04-30T00:00:00) Singh, Parvinder; Faujdar, Jyoti; Sarkar, Maitreyee; Kumar, Atul
We analyse the efficiency of nonlocal correlations in comparison with classical correlations under biased experimental set-up, e.g. for a nonlocal game or a class of Bell-CHSH inequality where both Alice and Bob choose their measurements with a certain bias. We demonstrate that the quantum theory offers advantages over classical theory for the whole range of biasing parameters except for the limiting cases. Moreover, by using fine-grained uncertainty relations to distinguish between classical, quantum and superquantum correlations, we further confirm the underlined advantage of quantum correlations over classical correlations. Our results clearly show that all pure bi-partite entangled states violate the Bell-CHSH inequality under biased set-up. Although for the two-qubit mixed Werner state, the Horodecki state and a state proposed by Ma et al. (Phys Lett A 379:2802, 2015) the range of violation is same in both biased and unbiased scenarios, the extent of violation is different in both cases. We extend our analysis to detect nonlocal correlations using quantum Fisher information and demonstrate a necessary condition for capturing nonlocality in biased scenario. Furthermore, we also describe properties of nonlocal correlations under noisy conditions considering a biased experimental set-up. � 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
CottonLeafNet: cotton plant leaf disease detection using deep neural networks
(Springer, 2023-03-18T00:00:00) Singh, Paramjeet; Singh, Parvinder; Farooq, Umar; Khurana, Surinder Singh; Verma, Jitendra Kumar; Kumar, Munish
India is a cover crop region whereby agricultural production sustains a substantial proportion of the populace and upon which the whole Indian economy is heavily reliant. As per research, it provides subsistence for around 70% of rural households. In terms of agricultural output and exports, India ranks second and ninth, respectively. However, it accomplishes the first position globally in terms of cotton exports thereby adequately contributing to the economy of the country. However, it has been documented that various crops especially cotton plants are severely harmed by various pests, extreme climatic variations, nutrient inadequacy and toxicity, and so on. Cotton plant diseases cause a wide range of illnesses ranging from bacterial to nutritional deficiency giving a hard time for the human eye to recognize. However, most of the researchers have considered only a few types of cotton leaf diseases and excluded many. Keeping these constraints in consideration, this research seeks to aid the detection of these diseases by employing deep learning paradigms. The research begins with acquiring a near-balanced dataset with 22 leaf disease types including bacterial, fungal, viral, nutrient deficiency, etc. followed by data augmentation to boost the performance of the models. Many algorithms were tested, however, CNN happens to be very efficient and productive. The proposed model when evaluated on the test set achieves an accuracy of 99.39% with a negligible error rate, thus outperforming all the existing approaches by consuming less computational time. The outcome portrays that the proposed approach has the efficiency to be implemented in real-time detection systems to aid the precise detection of cotton leaf diseases to help the farmers in taking appropriate actions. � 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Detection of content-based cybercrime in Roman Kashmiri using ensemble learning
(Springer, 2023-09-25T00:00:00) Farooq, Umar; Singh, Parvinder; Khurana, Surinder Singh; Kumar, Munish
The official language of Kashmir, Kashmiri language or Koshur, is spoken by more than 7 million people, yet its content-based cybercrime detection remains unexplored in theoretical and experimental research. Furthermore, the absence of programming libraries for sentimental analysis and a benchmark corpus has impeded advancements in this field. Challenges persist in working with diverse scripts of Kashmiri, including Perso-Arabic, Sharada, Devanagari, and Roman. Detecting cybercrime in this language is challenging due to its complex morphological nature, lack of resources, scarcity of annotated datasets, and varied linguistic characteristics, emphasizing the importance of overcoming these obstacles to develop effective detection systems. This paper attempts to detect content-based cybercrime in Roman Kashmiri script, extensively utilized on online platforms like social media, chat rooms, emails, etc., by the Kashmiri community. A well-balanced and meaningful dataset, the first of its kind in this context, is compiled, incorporating positive and negative comments, and three strategies were employed for analysis. The findings reveal that the Tf-Idf Vectorizer outperforms other tokenization methods (Count Vectorizer and Tf-Idf Transformer), bi-gram notation exhibits superior performance compared to one and tri-gram notations, and the XGBM proves to be the most effective in terms of evaluation metrics. Leveraging these strategies, Python applications were developed for text classification, successfully distinguishing cyberbullying (unsafe) from non-cyberbullying (safe) instances, with the XGBM exhibiting exceptional accuracy using the Tf-Idf Vectorizer with bi-gram, a Bag of Words, and lexical features. This pioneering research underscores the urgent need for content-based cybercrime detection advancements in the Kashmiri language, paving the way for effective detection systems to address language-specific challenges and promote a safer online environment for the Kashmiri community. Furthermore, this research opens new avenues for further advancements in detecting and preventing cybercrime in Kashmiri and potentially in other languages lacking robust cybercrime detection methodologies. � 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
An Empirical Study on Detection of Android Adware Using Machine Learning Techniques
(Springer, 2023-10-06T00:00:00) Farooq, Umar; Khurana, Surinder Singh; Singh, Parvinder; Kumar, Munish
The Android operating system, without showing signs of diminishing, has experienced unprecedented popularity and continues to thrive with a significant user base. Its notable aspect for supporting third-party applications has revolutionized the digital landscape, allowing developers to generate revenue through advertising. Adware has emerged as a prominent monetization method for developers of both Adware and the applications that integrate it. However, as the utilization of Adware proliferates, it simultaneously escalates the risk of fraudulent activities associated with advertising approaches. The increasing prevalence of Adware introduces a pressing need for robust detection and mitigation strategies to address the potentially detrimental effects of fraudulent practices. In response, the proposed system focuses on analyzing and identifying alterations in network traffic acquired from Android devices. This research delves into an extensive exploration of machine and deep learning models, aiming to enhance the detection and mitigation of Adware. The exceptional capabilities of the LGBM model highlight the system's noteworthy performance in binary classification. However, in multiclass classification, the XGBM model emerges as the frontrunner, outperforming other models and showcasing superior effectiveness in distinguishing and classifying Adware and general Malware. These outcomes highlight the remarkable efficacy of the system in accurately classifying adware instances, regardless of the classification scenario. The findings not only validate the viability of the proposed system but also underscore the superior performance of specific machine learning models employed in the research. With further refinement and optimization, the system holds great promise in enhancing the security and integrity of the Android ecosystem. � 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Entanglement and separability of graph Laplacian quantum states
(Springer, 2022-04-07T00:00:00) Joshi, Anoopa; Singh, Parvinder; Kumar, Atul
In this article, we study the entanglement properties of multi-qubit quantum states using a graph-theoretic approach. For this, we define entanglement and separability for m-qubit quantum states associated with a weighted graph on 2 m vertices. We further explore the properties of a block graph and a star graph to demonstrate criteria for entanglement and separability of these graphs. � 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Feature Engineering and Ensemble Learning-Based Classification of VPN and Non-VPN-Based Network Traffic over Temporal Features
(Springer, 2023-07-29T00:00:00) Abbas, Gazy; Farooq, Umar; Singh, Parvinder; Khurana, Surinder Singh; Singh, Paramjeet
With the rapid advancement in technology, the constant emergence of new applications and services has resulted in a drastic increase in Internet traffic, making it increasingly challenging for network analysts to maintain network security and classify traffic, especially when encrypted or tunneled. To address this issue, the proposed strategy aims to distinguish between regular traffic and traffic tunneled through a virtual private network and characterize traffic from seven different applications. The proposed approach utilizes various ensemble machine learning techniques, which are efficient and accurate and consume minimal computational time for training and prediction compared to conventional machine and deep learning models. These models were applied for both the classification and characterization of network traffic, deriving efficient results. The extreme and light gradient boosting algorithms performed well in multiclass classification, while AdaBoost and Light GBM performed well in binary classification. However, when all the datasets were merged and categorized into two classes and various feature engineering methods were applied, the proposed system achieved an accuracy of more than 99%, with minimal error scores using light GBM with min�max scaling over stratified fivefold, thereby outperforming all existing approaches. This research highlights the efficiency and potential of the proposed model in detecting network traffic. � 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
Identification of Counterfeit Indian Currency Note using Image Processing and Machine Learning Classifiers
(Institute of Electrical and Electronics Engineers Inc., 2023-03-27T00:00:00) Sharan, Vivek; Kaur, Amandeep; Singh, Parvinder
Technology is continuously changing our life. Day by day, it makes our life easy, but some challenges and issues exist. Counterfeit currency is one of them. It happens because of the production and circulation of currency without the permission of an authorized system. Some people use scanning and printing technology to produce such notes and circulate them around us, which is a kind of forgery.It leads to personal loss and degrades the Country's economy. Such notes are very similar to the original, which becomes a problem for ordinary people to identify the authenticity of the currency, especially for visually impaired people. However, most researchers proposed different methods to differentiate real notes from fake ones based on the currency's shape, colors, and size. The notes are easily detected when the condition of the note is good but difficult when it deteriorates over time. Extracting features from such notes is another challenging task. Some systems are available to only specific sectors and not easily available to common people. Therefore, a system that can distinguish between real and fake notes is required. This article classifies Indian Currency notes as real or fake with four supervised Machine Learning algorithms i.e., Support Vector Classifier, K-Nearest Neighbor, Decision Tree, and Logistic Regression followed by Image Processing techniques. This study has implemented these algorithms on the dataset of 1372 currency image samples, out of which 762 are real, and the remaining are fake, available on the UCI machine learning repository. Further, the performance of all the algorithms is measured in terms of accuracy, recall, Precision, and F1-score and after analyzing, it is observed that the K-Nearest Neighbor leverage outstanding results compared to other algorithms. � 2023 IEEE.
Improved Multisignature Scheme for Authenticity of Digital Document in Digital Forensics Using Edward-Curve Digital Signature Algorithm
(Hindawi Limited, 2023-04-08T00:00:00) Shankar, Gauri; Ai-Farhani, Liwa H.; Anitha Christy Angelin, P.; Singh, Parvinder; Alqahtani, Abdullah; Singh, Abha; Kaur, Gaganpreet; Samori, Issah Abubakari
At the moment, digital documents are just as important as paper documents. As a result, authenticity is essential, especially in legal situations and digital forensics. As technology advances, these digital signature algorithms become weaker, necessitating the development of digital authentication schemes capable of withstanding current security threats. This study proposed a scheme based on an asymmetric key cryptosystem and the user's biometric credentials to generate keys for digital signatures. A single document can be signed by multiple signatories at the same time under this scheme. The primary goal of this article is to create a safe and cost-effective multiignature scheme. To create keys for document signing and verification, the Edwards-curve Digital Signature Algorithm (EdDSA), especially Ed25519, is employed. The Edwards-curve Digital Signature Algorithm is used with blockchain technology to sign crypto wallets. The Python implementation of a scheme that enables platform independence. We performed performance, security, and comparative analysis to ensure maximum usability. The article's main findings are that the Ed25519 algorithm can be used in blockchain. � 2023 Gauri Shankar et al.
Nonlocality and efficiency of three-qubit partially entangled states
(Birkhauser, 2022-09-08T00:00:00) Faujdar, Jyoti; Kaur, Hargeet; Singh, Parvinder; Kumar, Atul; Adhikari, Satyabrata
We analyse nonlocal properties in three-qubit partially entangled Wn states to understand the efficiency of these states as entangled resources. Our results show that Wn states always violate the three-qubit Svetlichny inequality, and the degree of violation increases with the increase in degree of entanglement. We find that nonlocal correlations in W1 states are the highest in comparison to all other Wn states. We further demonstrate that within the limits of experimentally achievable measurements the W1 state proves to be a better quantum resource for specific protocols in comparison to standard W states, even though the degree of entanglement and nonlocality in the W1 state are less than the degree of entanglement and nonlocality in the standard W state. Moreover, we also consider superpositions of the Greenberger�Horne�Zeilinger (GHZ) state with W and W1 states to show that more entanglement is not a necessity for better efficiency in all protocols. In addition, we also demonstrate the preparation of three qubit quantum states represented as linear superpositions of the GHZ state with W and W1 states. � 2022, The Author(s) under exclusive license to Chapman University.
OG-CAT: A Novel Algorithmic Trading Alternative to Investment in Crypto Market
(Springer, 2023-03-28T00:00:00) Khurana, Surinder Singh; Singh, Parvinder; Garg, Naresh Kumar
Cryptocurrencies have emerged as a good tool for investment/trading in the last decade. The investors have achieved promising gains with the long-term investments made at reasonably good price/time. However, investment in cryptocurrencies is also exposed to extremely high volatility. Due to this, the investment may suffer from a high drawdown as the price may fall. In this work, we proposed optimized Greedy-cost averaging based trading (OG-CAT) a novel trading framework as an alternative to long-term investment in cryptocurrencies. The approach exploits the wavy structure of the price movement of cryptocurrencies, the high volatility of price, and the concept of cost averaging. Furthermore, the parameters of the approach are optimized with the simulated annealing algorithm. The approach is evaluated on the two prominent cryptocurrencies: bitcoin and ethereum. During the evaluation, OG-CAT not only outperformed the buy-and-hold investment approach in terms of profit but also demonstrated a lower drawdown. The profit percentage in the case of trading BTC with OG-CAT is 1.63 times more and the max drawdown is 1.62 times less than compared to the buy-and-hold strategy. � 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Revolutionize AI Trading Bots with AutoML-Based Multi-timeframe Bitcoin Price Prediction
(Springer, 2023-06-27T00:00:00) Khurana, Surinder Singh; Singh, Parvinder; Garg, Naresh Kumar
Multi-timeframe analysis/prediction provides essential information to traders. It gives a broader perspective of market trends and is used to identify significant levels of support and resistance. This will help traders/trading bots in making trading decisions. The majority of current studies focused on forecasting the closing price of daily candlesticks or high-frequency time frames, such as those of 1�min or 5�min. For artificially intelligent trading bots focusing on swing trading, price prediction related to other time frames is very significant. In this research, we present a study on developing a model to enable artificial intelligent-based trading bots to predict price components (open, high, low, and close prices) of the next 30-min, 1-h, and 4-h candlesticks of Bitcoin price. The study used two Auto-Machine Learning libraries: Tree-Based Pipeline Optimization Tool (TPOT) and AutoSklearn, to find the most suitable model for the task. The models are trained on historical price data of Bitcoin, and technical indicators are computed on these data. The performance of the trained models is evaluated in terms of R2 Score (Coefficient of Determination), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results showed that TPOT outperformed AutoSklearn library for all three time frames. It predicted all price components of 30-min candlestick with R2 Score of 0.999. � 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
The role of entanglement for enhancing the efficiency of quantum kernels towards classification
(Elsevier B.V., 2023-06-19T00:00:00) Sharma, Diksha; Singh, Parvinder; Kumar, Atul
Quantum kernels are considered as potential resources to illustrate benefits of quantum computing in machine learning. Considering the impact of hyperparameters on the performance of a classical machine learning model, it is imperative to identify promising hyperparameters using quantum kernel methods in order to achieve quantum advantages. In this work, we analyse and classify sentiments of textual data using a new quantum kernel based on linear and full entangled circuits as hyperparameters for controlling the correlation among words. We also find that the use of linear and full entanglement further controls the expressivity of the Quantum Support Vector Machine (QSVM). In addition, we also compare the efficiency of the proposed circuit with other quantum circuits and classical machine learning algorithms. Our results show that the proposed fully entangled circuit outperforms all other fully or linearly entangled circuits in addition to classical algorithms for most of the features. In fact, as the feature increases the efficiency of our proposed fully entangled model also increases significantly. � 2023 Elsevier B.V.
Sentiment analysis of Hindi language text: a critical review
(Springer, 2023-11-11T00:00:00) Sidhu, Simran; Khurana, Surinder S.; Kumar, Munish; Singh, Parvinder; Bamber, Sukhvinder S.
Sentiment analysis involves extracting sentiments from various forms of text, including customer reviews, tweets, blogs, and news clips expressing opinions on diverse subjects, even populist events. The advent of tools supporting regional languages has resulted in a substantial surge of regional language texts. As Hindi ranks fourth in terms of native speakers, the development of sentiment analysis mechanisms for Hindi text becomes crucial. This paper provides a comprehensive review of specific approaches used in Hindi sentiment analysis, encompassing negation handling and the evolution of SentiWordNet for the Hindi Language. Moreover, it offers an overview of available Hindi lexicons and insights into diverse stemmers and morphological analyzers designed for the language. Additionally, the paper conducts an in-depth literature review of various sentiment analysis tasks carried out in Hindi, thereby opening avenues for future research in sentiment analysis and opinion mining in the Hindi language. � 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
A systematic review of artificial intelligence in agriculture
(Elsevier, 2022-01-15T00:00:00) Singh, Parvinder; Kaur, Amandeep
The current world population is 7.8 billion and is projected to reach 9.8 billion by 2050. The limited land area and strong need to produce more crop to feed the ever-increasing population is a major challenge today, especially for developing countries. The strong need to produce more crop from lesser land has led to several challenges in the field of agriculture. Reduction in agriculture yield due to climate change and global warming due to farming has become a vicious circle. Excessive use of chemicals in farms to increase soil fertility and reduce weeds and pests have adversely affected the environment and the human health. There is limited availability of natural resources like phosphorous and energy required in agriculture. Water scarcity and increase in plant diseases are other major concerns. Artificial intelligence (AI) has emerged as a promising technology in digital agriculture. Digital agriculture relates to using digital technologies for collecting, storing, and further analyzing the electronic agricultural data for better reasoning and decision-making using AI techniques. Precision agriculture is one such technique that monitors soil moisture and composition, temperature, and humidity and determines optimized fertilizer and water requirements for a specific crop and different areas of a farm. Then there are computer vision and machine learning techniques to detect diseases and deficiencies in plants, recognizing weeds that helps in spraying only those parts of land where the plants are disease-infected or where weeds are present instead of the whole field. Utilization of AI in agriculture is helping in developing agricultural methods capable of increasing crop yield and reducing the previously stated challenges. With merits of using AI, there are certain issues. The first major issue in using the AI techniques is the need for high computational power that, again, leads to global warming. Also, in developing countries, the internet infrastructure needs to be improved to use AI techniques effectively. Cost of using AI is high, and countries need AI experts to use the techniques to full potential. The focus of this chapter is to review how AI techniques are helping in increasing yield and overcoming limitations, like global warming, excessive use of fertilizers, limited availability of natural resources, plant disease, and water scarcity. The chapter concludes by discussing the issues and challenges in using AI, especially as it related to agriculture. � 2022 Elsevier Inc. All rights reserved.