NEERENDRA KUMAR
@cujammu.ac.in
Associate Professor, Department of Computer Science and IT
CENTRAL UNIVERSITY OF JAMMU
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Engineering
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Narinder Verma, Neerendra Kumar, Zakir Ahmad Sheikh, Neha Koul, and Ankit Ashish
Chapman and Hall/CRC
Zaid Bin Mushtaq, Shoaib Mohd Nasti, Chaman Verma, Maria Simona Raboaca, Neerendra Kumar, and Samiah Jan Nasti
MDPI AG
In the original publication [...]
Sumiya Mushtaq, Neerendra Kumar, Yashwant Singh, and Pradeep Kumar Singh
World Scientific Pub Co Pte Ltd
Personality is a psychological construct that embodies the unique characteristics of an individual. Automatic personality computing enables the assessment of personality elements with the help of machines. Over the last few decades, a lot of researchers have focussed on computing aspects of personality, emotions, and behavior with the help of machine learning. Efficient personality recognition using machine learning can provide inroads to almost every field of human advancement. However, we found out there are not enough good surveys to consolidate the progress of the growing field. In our effort, we have taken a sub-field of personality computation known as apparent personality perception or First Impressions. In addition to providing an exhaustive compilation of available First Impression research, we have classified the existing literature according to the data modalities and machine learning techniques they have utilized. Our work also lists various limitations and gaps within the existing literary works with possible measures to address them. The paper concludes with our comments on the future work in the field of first impressions.
Aqsa Sayeed, Zoltán Vámossy, Neerendra Kumar, Yash Paul, Yatish Bathla, and Neha Koul
Springer Nature Singapore
Yatish Bathla and Neerendra Kumar
Springer Nature Singapore
Saraswati Narayan, Neerendra Kumar, Neha Koul, Chaman Verma, Florentina Magda Enescu, and Maria Simona Raboaca
Springer Nature Singapore
Kaleem Ullah Bhat, Neerendra Kumar, Neha Koul, Chaman Verma, Florentina Magda Enescu, and Maria Simona Raboaca
Springer Nature Singapore
Inam ul Haq, Neerendra Kumar, Neha Koul, Chaman Verma, Florentina Magda Eneacu, and Maria Simona Raboaca
Springer Nature Singapore
Ajay Kumar, Mayank Chopra, Yashwant Singh, and Neerendra Kumar
EManuscript Technologies
Artificial intelligence is becoming more prevalent across diverse disciplines, and aerial vehicles are increasingly becoming "Unmanned”. It is beneficial when residents might otherwise be in danger, such as during COVID-19 medicine delivery, gathering information about the enemy, or using it in agriculture. This study aims to provide a scientometric assessment of the latest research centres, patterns, and global reach of UAVs from 2007 to 2022. The study uses bibliographic information downloaded in CSV format from Scopus to examine the in-depth visualization of the index item's properties. In addition to examining article expansion, field classifications, global dispersion, citation analysis, and the impact of the institutions and writers, the study examines UAV applications distributed throughout the world. To analyse term co-occurrence, we use a Java-based program called VOSviewer, which lists hubs and the latest innovations in UAV research. © Author (s) 2023.
Arpit Jain, Chaman Verma, Neerendra Kumar, Maria Simona Raboaca, Jyoti Narayan Baliya, and George Suciu
MDPI AG
The estimation of an image geo-site solely based on its contents is a promising task. Compelling image labelling relies heavily on contextual information, which is not as simple as recognizing a single object in an image. An Auto-Encode-based support vector machine approach is proposed in this work to estimate the image geo-site to address the issue of misclassifying the estimations. The proposed method for geo-site estimation is conducted using a dataset consisting of 125 classes of various images captured within 125 countries. The proposed work uses a convolutional Auto-Encode for training and dimensionality reduction. After that, the acquired preprocessed input dataset is further processed by a multi-label support vector machine. The performance assessment of the proposed approach has been accomplished using accuracy, sensitivity, specificity, and F1-score as evaluation parameters. Eventually, the proposed approach for image geo-site estimation presented in this article outperforms Auto-Encode-based K-Nearest Neighbor and Auto-Encode-Random Forest methods.
Sumiya Mushtaq and Neerendra Kumar
Springer Nature Singapore
Deep Singh, Amit Paul, Neerendra Kumar, Veronika Stoffová, and Chaman Verma
MDPI AG
Boolean functions are important in terms of their cryptographic and combinatorial properties for different kinds of cryptosystems. The nonlinearity and resiliency of cryptographic functions are crucial criteria with respect to protection of ciphers from affine approximation and correlation attacks. In this article, some constructions of disjoint spectra Boolean that function by concatenating the functions on a lesser number of variables are provided. The nonlinearity and resiliency profiles of the constructed functions are obtained. From the profiles of the constructed functions, it is observed that the nonlinearity of these functions is greater than or equal to the nonlinearity of some existing functions. Furthermore, in the security analysis of cryptosystems, 4th order nonlinearity of Boolean functions play a crucial role. It provides protection against various higher order approximation attacks. The lower bounds on 4th order nonlinearity of some classes of Boolean functions having degree 5 are provided. The lower bounds of two classes of functions have form Tr1n(λxd) for all x∈F2n,λ∈F2n*, where (i) d=2i+2j+2k+2ℓ+1, where i,j,k,ℓ are integers such that i>j>k>ℓ≥1 and n>2i, and (ii) d=24ℓ+23ℓ+22ℓ+2ℓ+1, where ℓ>0 is an integer with property gcd(ℓ,n)=1, n>8 are provided. The obtained lower bounds are compared with some existing results available in the literature.
Parveiz Nazir Lone, Deep Singh, Veronika Stoffová, Deep Chandra Mishra, Umar Hussain Mir, and Neerendra Kumar
MDPI AG
In the present era of digital communication, secure data transfer is a challenging task in the case of open networks. Low-key-strength encryption techniques incur enormous security threats. Therefore, efficient cryptosystems are highly necessary for the fast and secure transmission of multimedia data. In this article, cryptanalysis is performed on an existing encryption scheme designed using elliptic curve cryptography (ECC) and a Hill cipher. The work shows that the scheme is vulnerable to brute force attacks and lacks both Shannon’s primitive operations of cryptography and Kerckchoff’s principle. To circumvent these limitations, an efficient modification to the existing scheme is proposed using an affine Hill cipher in combination with ECC and a 3D chaotic map. The efficiency of the modified scheme is demonstrated through experimental results and numerical simulations.
Aqsa Sayeed, Chaman Verma, Neerendra Kumar, Neha Koul, and Zoltán Illés
MDPI AG
The Internet of robotic things (IoRT) is the combination of different technologies including cloud computing, robots, Internet of things (IoT), artificial intelligence (AI), and machine learning (ML). IoRT plays a major role in manufacturing, healthcare, security, and transport. IoRT can speed up human development by a very significant percentage. IoRT allows robots to transmit and receive data to and from other devices and users. In this paper, IoRT is reviewed in terms of the related techniques, architectures, and abilities. Consequently, the related research challenges are presented. IoRT architectures are vital in the design of robotic systems and robotic things. The existing 3–7-tier IoRT architectures are studied. Subsequently, a detailed IoRT architecture is proposed. Robotic technologies provide the means to increase the performance and capabilities of the user, product, or process. However, robotic technologies are vulnerable to attacks on data security. Trust-based and encryption-based mechanisms can be used for secure communication among robotic things. A security method is recommended to provide a secure and trustworthy data-sharing mechanism in IoRT. Significant security challenges are also discussed. Several known attacks on ad hoc networks are illustrated. Threat models ensure integrity confidentiality and availability of the data. In a network, trust models are used to boost a system’s security. Trust models and IoRT networks play a key role in obtaining a steady and nonvulnerable configuration in the network. In IoRT, remote server access results in remote software updates of robotic things. To study navigation strategies, navigation using fuzzy logic, probabilistic roadmap algorithms, laser scan matching algorithms, heuristic functions, bumper events, and vision-based navigation techniques are considered. Using the given research challenges, future researchers can get contemporary ideas of IoRT implementation in the real world.
Zaid Mushtaq, Shoaib Nasti, Chaman Verma, Maria Raboaca, Neerendra Kumar, and Samiah Nasti
MDPI AG
The images in high resolution contain more useful information than the images in low resolution. Thus, high-resolution digital images are preferred over low-resolution images. Image super-resolution is one of the principal techniques for generating high-resolution images. The major advantages of super-resolution methods are that they are economical, independent of the image capture devices, and can be statically used. In this paper, a single-image super-resolution network model based on convolutional neural networks is proposed by combining conventional autoencoder and residual neural network approaches. A convolutional neural network-based dictionary method is used to train low-resolution input images for high-resolution images. In addition, a linear refined unit thresholds the convolutional neural network output to provide a better low-resolution image dictionary. Autoencoders aid in the removal of noise from images and the enhancement of their quality. Secondly, the residual neural network model processes it further to create a high-resolution image. The experimental results demonstrate the outstanding performance of our proposed method compared to other traditional methods. The proposed method produces clearer and more detailed high-resolution images, as they are important in real-life applications. Moreover, it has the advantage of combining convolutional neural network-based dictionary learning, autoencoder image enhancement, and noise removal. Furthermore, residual neural network training with improved preprocessing creates an efficient and versatile single-image super-resolution network.
Neha Koul, Neerendra Kumar, Aqsa Sayeed, Chaman Verma, and Maria Simona Raboaca
Institute of Electrical and Electronics Engineers (IEEE)
The world we live in is where a human-machine connection is a barometer, and this tendency is only growing to provide richer and universal human reminiscences. New technologies, as well as the fusion of different classes of current technologies, are used to create such unique solutions. The Internet of Robotic Things (IoRT) is the product of collaboration between robotics and the Internet of Things (IoT), which has opened up incredible potential for localized self-sufficient solutions. Robots can collect accurate data from the environment and provide a comprehensive service instantaneously. Being an amalgamation of two different concepts i.e. IoT and Robotics, the IoRT system is anticipated to benefit both by expanding their potential to innovative heights. This paper gives a concise introduction to the communication architecture of the IoRT system. The taxonomy of various data exchange protocols used in an IoRT system is proposed. The necessary improvements in protocols that are required to enhance communication in an IoRT system are presented. A Review of some data exchange methodologies based on Artificial Intelligence (AI) for secure and reliable communication is also given in this paper. The key motive of this paper is to understand the data exchange within an IoRT system using common communication protocols.
Ajay Kumar, Yashwant Singh, and Neerendra Kumar
Springer Singapore
Parul Parihar, Devanand, and Neerendra Kumar
IEEE
Product promotion for increasing the sale of the product is critical in today’s competitive environment. Online medium of promotion is vital in this regard. Product promotion especially in the field of movies goes through fake promotion issues. Movies are promoted by the entities through fake rating. This work primary focuses on the detection of fake profiles. To accomplish this collaborative filtering with the pre-processing mechanism is used. Demonstration of work will be done through movie lense dataset. The nature of proposed approach is modular; this means entire work will be divided into phase. Data acquisition is performed in the first phase. After collecting the dataset, pre-processing mechanism is applied by using nominal conversion. Collaborative filtering is applied along with clustering to determine the fake promotion of within movie lense dataset. Nominal conversion is also required since recommender system may not able to handle string values. By the classification accuracy we can show the result of the proposed work.
Neerendra Kumar and Pragti Jamwal
University of Kerbala - KIJOMS
Raashid Manzoor and Neerendra Kumar
Springer International Publishing
Shagun Verma and Neerendra Kumar
Springer Singapore
Mahvish Bijli and Neerendra Kumar
Springer Singapore
Neerendra Kumar and Zoltán Vámossy
Springer Singapore
Yash Paul and Neerendra Kumar
Springer International Publishing
Yatish Bathla, Chaman Verma, and Neerendra Kumar
Springer International Publishing