Darpan Anand
@cuchd.in
Professor
Chandigarh University
Prof. (Dr.) Darpan Anand is a Professor in the Computer Science Engineering Department at Chandigarh University, India with more than 17 years of experience in teaching, industry, and research. He is currently a member of the Board of Studies, a Member of the research Degree Committee, Outcome Based Education Coordinator, ABET Accreditation Coordinator, Research Coordinator, and the Coordinator of Projects in the Department of Computer Science, Chandigarh University. He teaches Computer Networks, Operating Systems, Cryptography & Network Security, Machine Learning, Robotics Process Automation, etc. His research interests include Information Security, e-governance, machine learning, Intelligent Information Processing, Evolutionary Algorithms, etc. He has guided several Ph.D. and PG Dissertations. He is an author/co-author of more than 50+research papers (indexed in SCI, ESCI, Scopus, etc.), 1 Textbook, 6 book chapters (IET, Springer, and Elsevier), 4 patents, SWAYAM MOOC courses,
EDUCATION
B.Tech., M.Tech., Ph.D.
RESEARCH INTERESTS
Information Security, Computer Networks, Machine Learning, Blockchain
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Richard Essah, Darpan Anand, and Abhishek Kumar
Springer Nature Singapore
Amit Malik, Edeh Michael Onyema, Surjeet Dalal, Umesh Kumar Lilhore, Darpan Anand, Ashish Sharma, and Sarita Simaiya
Elsevier BV
Ruchi Doshi, Kamal Kant Hiran, Maad M. Mijwil, and Darpan Anand
IGI Global
Business intelligence (BI) is no longer adequate to handle the day-to-day operations of any firm considering the ever-increasing volume of data and the resulting overload. As the amount of data grows, it becomes increasingly difficult to evaluate, making the introduction of a decision-making methodology that can be described as real-time BI, very taxing and cumbersome. Because of this, it is becoming increasingly difficult to implement effective decision-making at the enterprise level that was driven by BI, so that the company may remain robust and resilient to both man-made dangers and natural calamities. With today's sophisticated malware and the growing importance of the Internet of Things (IoT), we require a more sophisticated intelligence system, which we currently refer to as Artificial Intelligence (AI). We have a better chance of surviving a cyber-attack thanks to AI and its two other subsets, Machine Learning (ML) and Deep Learning (DL). These technologies strengthen our organization's day-to-day operations and help us make more reliable decisions as stakeholders.
Vishan Kumar Gupta, Sarvesh Vishwakarma, Darpan Anand, Arun Kumar, Harish Tiwari, and Ankit Kumar
IEEE
To know whether specific chemical compounds can interfere with bodily functions that might have a negative impact on health, scientists have developed an effective bagging-based ensemble model for better activity detection. Here, authors are utilizing a variety of physicochemical characteristics (molecular descriptors), where an in-silico method has been developed for the detection of the activity of estrogen receptor alpha ligand binding domain pharmaceuticals. To extract features, one uses the PaDEL-Descriptor. The 7741 drug molecules in the ER-LBD dataset have a total of 1444 features, with 438 of them being active and 7303 of them being inactive. This dataset has a lot of features and is very unbalanced. First, a problem with class imbalance is fixed by Smote algorithm and selecting important features with the CFS algorithm. An ensemble learning-based prediction model is created, where categorization was done using four existing classifiers i.e., SVM, decision tree, AdaBoost, and random forest. With the help of this strategy, the proposed bagging- based detection model provides outperformed accuracy. To assess the model's consistency across all target classes, the K-fold cross validation is used.
Harsh Bansal, Divya Gupta, and Darpan Anand
IEEE
As the Fourth Industrial Revolution marks the beginning of an era of digitization, key principles like inter-operability, decentralisation, and virtualisation are significantly impacting a variety of fields, including education. Aiming to combine traditional educational techniques with cutting-edge ICT-enabled tools, including artificial intelligence, automation, and blockchain, this paradigm coined as “Education 4.0” intertwines with “Industry 4.0”. With its intrinsic qualities of decentralisation, immutability, consensus, and transparency, blockchain stands up as an effective solution to problems like governance and credential credibility of the courses in which the student is enrolled can be resolved. This paper has contribution in four folds. This paper makes four significant contributions to literature. It begins by highlighting the goals of Education 4.0 and the relationship between modern teaching methods with cutting-edge technology. It then delves into an analysis of blockchain protocols, spotlighting their alignment with the DeFi protocol stack. The next part divides blockchain projects into four distinct categories, following which reviews diverse blockchain-powered projects in education, evaluating their implementations, advantages, and potential drawbacks.
Kaljot Sharma and Darpan Anand
CRC Press
Amandeep Kaur, Rakesh Sahu, and Darpan Anand
IEEE
This study established a novel routing technique that is dubbed reinforcement learning for SIoT (MCTAR-SIoT). Its purpose is to ensure that data interchange in IoT networks is both energy-efficient and safe. At the subsequent hop node selection, this technique used three measures: Communication Trust (CT), Energy Trust (ET), and Hop Count (HC). The CT assures that nodes can be trusted, the ET ensures that nodes have adequate energy, and the HC provides a route with reduced time. By integrating all of these indicators and giving each characteristic its own weight, we are able to develop a new CRM metric. The first feature ensures that communications are kept private, the second extends the device's operational lifespan, and the third shortens the response time. Each node that is picked is the next-hop node, and it is responsible for transmitting the data to the destination node via it. The CRM uses deep reinforcement learning to choose which values to use. Extensive simulations were run for experimental validation, and the performance of the network was evaluated using MDR. These simulations included altering the proportion of members of the network who were malevolent. They saw an increase in throughput of 3% from CNN and 7% from SVM, respectively. Next, they saw an improvement in their MDR of roughly 2.6341% from CNN and 7.8112% from SVM, respectively. In conclusion, the NL is enhanced by 8% from OCNN and 10% from SVM, respectively, resulting in an improved score.
Amandeep Kaur, Rakesh Sahu, and Darpan Anand
IEEE
A recent trend in academic study is the Social Internet of Things (SIoT), which integrates social network theory into the various layers of the IoTs to open up novel avenues for the IoTs' future growth. Slot seems to be a subset of lots that utilizes intelligent equipment and humans as nodes, social networks as the organization type, the social connection between things, things and humans, and humans, and formats study models and techniques with social media network characteristics in order to realize the connection, service, and usage of the lots. In addition, Slot is a realization of the innovation, architectural style, and application of lots using social network research methodology, and it helps promote the incorporation between the real and the virtual cyberspace, contributes to the realization of the lots, broadens the study scope of social networking, and offers a new remedy for the particular problems of the lots. Researchers must, therefore, have a firm background in the SloT developments in machine learning. SIoT uses Machine Learning (ML) methods to determine the features of connected devices. This special issue solicits scientific research articles that provide a picture of the current state of SIoT research. In this research improve the features of SIoT by residual network mapping and learning by XGBOOST
Jayant Singh Jhala and Darpan Anand
IEEE
The Indian automobile industry had its highest-ever annual domestic passenger vehicle sales last year. A total of 3.793 million or 37.93 lakh units were sold in the country in 2022, which is 23.1 percent higher than the preceding year. Similarly, the used car sale is also be increased day by day. The actual and reasonable rates of used cars are important to sale and purchase so that, buyers and sellers will be get benefited. The disparity in prices due to various characteristics or features consistently making prediction of price a difficult job. In the matter of used car price prediction, it has been equally tough. It is challenging to determine when the advertised price is indeed legitimate. Used car prices are highly affected by features like new car price, engine power (cc), maximum power (bhp). For the sake of predicting used car prices on ground of its characteristics, this research target to generate machine learning models incorporating multiple linear regression, decision tree regression and random forest regression. The Car Dekho data set that is used, initially had 13 attributes and 19974 records. In this Research Field, significant study has been carried out; although, not all of them utilized Scikit-learn. Our Suggested models produced accuracy of 94.10% with random forest regression whereas 92.45% with decision tree regression and 89.85% with multiple linear regression.
Shikhar Bhardwaj, Sandeep Harit, N.A. Shilpa, and Darpan Anand
Inderscience Publishers
Richard Essah, Darpan Anand, and Surender Singh
Springer Nature Singapore
Richard Essah, Darpan Anand, and Surender Singh
Elsevier BV
Gurpreet Singh, Darpan Anand, Woong Cho, Gyanendra Prasad Joshi, and Kwang Chul Son
MDPI AG
The practice of Deep Convolution neural networks in the field of medicine has congregated immense success and significance in present situations. Previously, researchers have developed numerous models for detecting abnormalities in musculoskeletal radiographs of upper extremities, but did not succeed in achieving respectable accuracy in the case of finger radiographs. A novel deep neural network-based hybrid architecture named ComDNet-512 is proposed in this paper to efficiently detect the bone abnormalities in the musculoskeletal radiograph of a patient. ComDNet-512 comprises a three-phase pipeline structure: compression, training of the dense neural network, and progressive resizing. The ComDNet-512 hybrid model is trained with finger radiographs samples to make a binary prediction, i.e., normal or abnormal bones. The proposed model showed phenomenon outcomes when cross-validated on the testing samples of arthritis patients and gives many superior results when compared with state-of-the-art practices. The model is able to achieve an area under the ROC curve (AUC) equal to 0.894 (sensitivity = 0.941 and specificity = 0.847). The Precision, Recall, F1 Score, and Kappa values, recorded as 0.86, 0.94, 0.89, and 0.78, respectively, are better than any of the previous models’. With an increasing appearance of enormous cases of musculoskeletal conditions in people, deep learning-based computational solutions can play a big role in performing automated detections in the future.
Darpan Anand and Aashish Kumar
Wiley
Shardeep Kaur Sooch, Darpan Anand, and Rajesh Deorari
IEEE
Early brain tumor detection has become important to provide timely diagnosis and treatment. Several methodologies are focusing to minimize the manual efforts required for diag-nosing by increasing not only the accuracy but also the speed of detection. This proposed methodology includes Otsu's threshold-based segmentation technique after which feature extraction is done by Grey Level Co-Occurrence Matrix (GLCM) to extract 13 intensities based and textual based features. The classification is done through the hybrid model of K-Nearest neighbor and Random Forest. The final outcome is generated by majority voting which castes its vote to either one of the above hybrid models. The results are compared to existing algorithms through various performance parameters which includes how accurate the result is, recall, specificity and time taken for execution
Talib ul Haq, Darpan Anand, and Nitika Kapoor
IEEE
Peer reviewing plays an important role if a re-searcher wants to publish his/her manuscript. Peer review is the impartial evaluation of your research article by subject matter experts in your area. Its objective is to assess the manuscript's quality and publishing readiness. This paper will be considering these peer reviews and use computational linguistics to relate them with acceptance of the manuscript. This paper will focus on text conversions in depth and discuss the flow of natural language processing. Natural language processing is a field of computer science and artificial intelligence that studies how computers interact with human languages, In specific it is an automated process of analyzing human language in order to gain information from it. Automation is the ultimate goal for efficiency driven organisations and Natural language processing is playing an important role in it. A machine can fully understand humans only when it communicates it with human language, its like communicating to someone who speaks your native language versus someone who speaks language you hardly can speak or understand. Natural language processing removes this gap. Text processing and text summarization, Automatic translation of languages, user interfaces, speech recognition and expert systems are examples of natural language processing applications. Chal-lenges for processing the complex language system of humans are far too many as human emotions and expressions through voice and body language are not fully decode in data. In near future everyone will have to communicate with a machine for household work as well as space work, so it becomes important to understand the working of Artificial Intelligence and Natural language processing is an important part of it.
Richard Essah, Darpan Anand, and Surender Singh
IEEE
Information technology changes the world and leads to the transformation of the industry to Industry 4.0. Further, this development includes the part of artificial intelligence, which will become the industrial revolution 5.0. This transformation impacts all the associated fields of quality human life. Agriculture is also a very important field for bettering human life. There are various types agriculture technology and this technology includes the Internet of Things, artificial intelligence, machine learning, and many more. The Internet of Things is the technology used worldwide to connect all the machines. For monitoring the various agriculture task, sensors are on board for communication through a low-power wireless sensor network to promote sustainable solution. Various IoT and other information and communication technology- based applications in the field of agriculture are reported in the literature. This paper aims to highlight the major contributions of information technology, information communication technology, the Internet of Things, artificial intelligence, machine learning, and many more in the field of agriculture. These technologies are used to provide solutions and transformations of various agriculture mechanisms and processes, including farm management systems, crop yield projections, crop monitoring, and smart agriculture. At ease selling and buying of agricultural yields to interested parties, Crop disease control, Crop monitoring, Crop production cycle enhancement, Soil testing method and Smart Agriculture services. Finally, the manuscript tries to emphasize the importance of implementing some critical processes for crop yield procurement through the technology related to computer science. The problem is addressed in the context of the Cocoa crop in the specific region of Africa, i.e., the Country Ghana. The purpose of the study is to review crop testing based on IoT and machine learning.
Harsh Bansal, Divya Gupta, and Darpan Anand
IEEE
Redesigning the current procedures and incorporating blockchain technology into them, much can be accomplished. Blockchain has the potential to upend corporate models by increasing their efficiency. The blockchain is promising in many respects because of its immutability, transparency, provenance, and disintermediation in a distributed environment. In order to obtain the best results, it is crucial to understand when to use blockchain solutions to the processes and when to avoid doing so. This paper has contribution in three folds. Firstly, the evolution of blockchain, types of blockchain and their suitability based upon the use cases has been reviewed. Secondly, it provides the parametric review of consensus mechanisms based upon energy efficiency, throughput, finality of the transactions. Finally, this paper reviews the factors which can help determine the suitability for blockchain based applications.
Parshant Sharma, Darpan Anand, and Nitika Kapoor
IEEE
Predicting unemployment rates has become crucial since it can assist governments in making decisions and designing strategies. Conventional univariate time series models and econometric approaches for predicting unemployment rates have garnered considerable attention from governments, corporations, research institutes, and academics in prior studies. Novel approaches for forecasting unemployment rates based on search engine query data have recently been developed. To help comprehend how graduates are hired, four learning algorithms were used: SVM, DT, XGBoost, RF. The accuracy, precision, and recall metrics, as well as the f1-score and support measures, were all assessed using the efficiency matrix. During the studies, the Extreme Gradient Boosting (XGBoost) achieved an accuracy rate of 81.22 percent, which was much higher than all of the learning algorithms, including SVM 79.6 percent, DT 79.4 and RF 79.1 percent. This study is quite promising, which has pushed the researchers to improve the procedure and evaluate the developed prediction model for further study.
Shardeep Kaur Sooch and Darpan Anand
IEEE
When extra number of cells are present in the brain, it may cause lump in the tissue which gives rise to tumor. The human brain is enclosed in the skull, which is very rigid. Hence various issues can occur due to the growth of any tumor inside this restricted space. Image processing processes the digital information stored in the form of pixels. Medical image processing is a branch of image processing which aims at processing of medical information such as MRI images, CT images, etc. The processing for these images allows brain tumor detection which consist of various steps such as pre-processing, segmentation, extraction of features and classification. The paper focuses on different approaches and work done in the medical field of detection of brain tumor using artificial intelligence.
Aashish Kumar, Darpan Anand, Sudan Jha, Gyanendra Prasad Joshi, and Woong Cho
Computers, Materials and Continua (Tech Science Press)
Surender Singh and Darpan Anand
IEEE
In this paper, the focus is on energy efficiency issues related to node positioning, competent data accumulation schemes, and effective manipulation of routing algorithms such as LEACH and PEGASIS cluster-based hierarchical algorithms for achieving energy efficiency in wireless sensor networks (WSN). This recommends changing advanced routing procedures, such as efficient system collection, data collection, and routing procedures to achieve the potential of WSN for energy using the soft computing techniques etc. The purpose of the work is to discuss the existing technical limitations and advantages of new methods. This may help to improve the time the WSN lasts and finally gives the problem a low-cost solution. A structure for applying soft computing directions is anticipated in WSN.
Vishal Bharti and Darpan Anand
IEEE
Blockchain is the emerging technological field for the current era due to its advantages such as distributed computing, transparency, cryptography enabled secure communication and message as well as source integrity. This technology is the innovation which can prompt critical changes the existing business climate and will have unexpected outcome. Blockchain can change the existing system and processes in which the organizations and business works, and able to modify our economy and financial system. It is distributed, decentralized and disseminated record novelty that expects to guarantee truthfulness. Since it can't be transformed or counterfeit most of the current focus in the research area as of cryptocurrency like BITCOIN. Blockchain Technology is something beyond digital currency, and it can have a few applications in government, money and banking industry, bookkeeping and Business Process Management. Hence, this examination endeavors to research and investigate its chances and difficulties for the momentum or future uses of Blockchain Technology. In this way, an enormous number of distributed investigations were painstakingly checked on and broke down dependent on their commitments to the Blockchain's assortment of information.
Darpan Anand and Vineeta Khemchandani
Springer International Publishing