Vishal Jain
@sharda.ac.in
Associate Professor, Department of CSE, SSET
Sharda University, Greater Noida, India
Dr. Vishal Jain is presently working as an Associate Professor at the Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Greater Noida, U. P., India. Before that, he worked for several years as an Associate Professor at Bharati Vidyapeeth’s Institute of Computer Applications and Management (BVICAM), New Delhi. He has more than 17 years of experience in the academics. He has earned degrees: Ph.D (CSE), M.Tech (CSE), MBA (HR), MCA, MCP, and CCNA. He has more than 1350 research citations with Google Scholar (h-index score 18 and i-10 index 34) and has authored more than 100 research papers in professional journals and conferences. He has authored and edited more than 45 books (most of them are indexed at Scopus) with various reputed publishers, including Elsevier, Springer, IET, Apple Academic Press, CRC, Taylor and Francis Group, Scrivener, Wiley, Emerald, NOVA Science, River Publishers, IGI-Global and Bentham Science.
EDUCATION
Ph.D (CSE), M.Tech (CSE), MCA, MBA (HR), MCP, CCNA
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science, Artificial Intelligence, Decision Sciences, Software
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Vishal Jain and Archan Mitra
IGI Global
This research explores energy harvesting systems through biomimicry, the practice of emulating natural processes for technological solutions. Addressing the need for sustainable and efficient energy sources, the study investigates the design and performance of biomimetic energy systems compared to conventional methods. Utilizing a mixed-methods approach, including literature review, case studies, and computational simulations, the research evaluates the efficiency, resilience, and environmental impact of these systems. Preliminary findings reveal that biomimetic designs offer enhanced efficiency and reduced ecological footprints, showcasing the potential of nature-inspired approaches in renewable energy. The study highlights the importance of further exploration in this field, particularly focusing on scalability and long-term sustainability.
Meenal Arora, Amit Mittal, Anshika Prakash, and Vishal Jain
IGI Global
Customer analytics is essential for creating insights from massive data that can be used to enhance management decision-making at various consumer levels, product creation, and service innovation. However, no studies have examined the potential of consumer analytics for achieving long-term corporate success. This research examines the structures of customer analytics capabilities in order to fill this gap by drawing upon a rigorous assessment of the big data literature. The interpretative framework for this study shows the concept of customer analytics, its significance, and the building blocks for consumer analytics capabilities. The research suggests a model of consumer analytics capabilities made up of four main constructs and some significant supporting sub-constructs. The study elaborates on developing a model to analyze sustainable firm performance through dimensions of customer analytics capabilities.
Raj Gaurang Tiwari, Himani Maheshwari, Vinay Gautam, Ambuj Kumar Agarwal, Naresh Kumar Trivedi, and Vishal Jain
IEEE
A new feature fusion network called “MedLeafNet” is presented in this study specifically for the categorization of Indian medicinal leaves. Using a hybrid architecture, MedLeafNet integrates the best features of Convolutional Neural Network (CNN) modules for spatial feature extraction and Transformer modules for sequence modeling. Because of the complexity of classifying Indian medicinal leaves—which are famous for their varied and powerful medicinal properties—the suggested methodology is well suited to the task. For this research, 80 different types of Indian leaves were analyzed, each with its well-known therapeutic properties. To improve its capacity to detect complex patterns in leaves, Me dLeafNet incorporates Transformer and CNN modules to efficiently gather both global and local information. The suggested model was optimized using several different optimizers, such as Adam, Adadelta, Gradient Descent, AdaGrad, RMSProp, Adamax, Momentum, and Adaptive Moment Estimation. When trained with the Adam optimizer, MedLeafNet attained its maximum accuracy of 98.97%, as shown by extensive testing. MedLeafNet is also compared against some of the most well-known deep learning architectures, such as ResNet, Inception, VGG, Xception, and MobileNet. In terms of classification accuracy, the findings show that it is superior to these architectures, proving that it is effective for the difficult job of classifying Indian medicinal leaves.
Raj Gaurang Tiwari, Himani Maheshwari, Ambuj Kumar Agarwal, and Vishal Jain
IEEE
The purpose of this study is to classify soybean plant diseases using a new method called Hybrid Ensemble Convolutional Neural Networks (HECNNet). To effectively extract unique characteristics and categorize pictures, the proposed approach makes use of convolutional neural networks. The suggested approach is organized around three main phases: data collection, preprocessing, and classification. The novel component of this study is the hybrid ensemble model, which consists of ResNeXt, SqueezeNet, and SegNet, three strong backbone networks. With convolutional layers, dropouts, and max-pooling layers carefully adjusted to match the needs of the dataset, these backbone networks function as feature extractors and classifiers. HECNNet is the product of the interplay between these three extractors. Notably, the prediction scores from each sub-network are combined using a weighted sum operation in HECNNet, which significantly improves the final recognition rate compared to a single network method. The proposed algorithm tackles the complexity of soybean disease categorization and obtains an amazing accuracy rate of 91.7%. This study is a major advance in agricultural disease diagnosis since it provides a reliable and effective method for diagnosing ailments affecting soybean plants.
Vishal Jain and Archan Mitra
IGI Global
Customer input has increased as digital manufacturing and smart factories advance. However, standard analysis methods struggle to turn this feedback into useful insights. This research study examined the use of machine learning (ML) sentiment analysis algorithms to improve digital manufacturing customer feedback interpretation. Machine learning, sentiment analysis, and digital industrialization theories underpin the research. Sentiment analysis may reveal nuanced consumer feedback insights that traditional methods miss, according to customer experience management and complex data analytics theories. A specially constructed ML system for sentiment analysis was used to real-world customer feedback data from numerous digital manufacturing enterprises in a case study. This method classified feedback sentiment using natural language processing. The program picked up small changes in client emotions that previous methods missed. These findings imply that machine learning-based sentiment analysis improves digital manufacturing customer feedback interpretation.
Rajendra Kumar, Aman Anand, Masanori Fukui, and Vishal Jain
AIP Publishing
Rajendra Kumar, Aman Anand, Vishal Jain, Praveen Pachauri, and Khar Thoe Ng
De Gruyter
Jain Sachin and Jain Vishal
Totem Publisher, Inc.
Arpita Nayak, Ipseeta Satpathy, B. C. M. Patnaik, and Vishal Jain
IGI Global
The definition of sustainable manufacturing is the use of commercially viable techniques that minimize adverse environmental effects while conserving energy and other resources, improving the environment for coming generations. The rise in environmental pollution and degradation has attracted the attention of manufacturers, therefore manufacturing industries are now introducing sustainable business practices in response to the rising and fast change in the operation of commercial activities, as they give significant economic and environmental advantages. Improved worker, community, and consumer safety is another benefit of safer manufacturing techniques. Manufacturing industries have begun to embrace and seek out more sustainable techniques for human development, and the manufacturers' role has shifted towards sustainable development. This study seeks to provide an overview of the various benefits of sustainable manufacturing techniques on various important factors, including the environment, the economy, and society.
Sapna Juneja, Abhinav Juneja, Arti Sharma, Vishal Jain, and Amena Mahmoud
Chapman and Hall/CRC
Rohit Anand, Sapna Juneja, Abhinav Juneja, Vishal Jain, and Ramani Kannan
Chapman and Hall/CRC
Sachin Jain and Vishal Jain
Springer Science and Business Media LLC
Raj Gaurang Tiwari, Ambuj Kumar Agarwal, Vishal Jain, and Anurag Kumar
IEEE
UNESCO has recognized the centuries-old art of batik weaving in Indonesia as a Masterpiece of the Oral and Intangible Heritage of Humanity. It has been passed down through the centuries in Indonesia, and its intricate designs and themes have won international praise. The classification of batik patterns according to factors such as their cultural background, manufacturing process, and symbolic meaning has been more popular in recent years. The purpose of this study is to investigate how batik categorization has affected Indonesia’s tourism and economy. It looks at how batik has evolved through time and across various cultures, as well as its historical and cultural importance in Indonesia. The proper categorization of batik has been achieved with the use of fundamental machine learning and deep learning techniques (CNN and EfficientNet). An improved version of EfficientNet achieves 93.81 percent accuracy in training, according to the results. The importance of batik museums and batik festivals in boosting tourism in Indonesia is also explored in this study. This article suggests that categorizing batik might have far-reaching effects on Indonesia’s tourism and economy. By promoting and preserving the rich cultural heritage of Batik, Indonesia can attract more tourists and generate greater economic benefits for the country.
Ankita Suryavanshi, Vishal Jain, Vinay Kukreja, Ankur Choudhary, and Sushant Chamoli
IEEE
In a world where cats' silent suffering is often overlooked, scientists set out on a life-changing journey aided by technology's empathetic hand. The adventure goes beyond human comprehension, venturing into the domain of AI-driven empathy and the search for a key to the secret language of cat agony. They found a way to shed light on the intricacies of feline discomfort using Convolutional Neural Networks (CNNs) and the insight of Random Forest. Each layer in the study reflects a new stanza in the song of recognition, and the research itself evolves like a symphony of pixels. Patterns emerge for the authors when others only see pictures, and they pick up on the agony hidden in the stillness of feline emotions. By bringing together scientific rigor and humaneness, we present a new method for identifying pain in feline companions. Inspiringly, the model distinguished pain intensity among five unique categories with an accuracy of 90%, proving its adeptness in realizing the goals. Researchers saw an instrument that leveled the playing field between man and feline experience when accuracy, recall, and F1 scores all came together. The authors introduce a model that expresses the inexpressible and ease the unspoken as pixels dance, filters learn, and forests decide. In this work, authors celebrate the unspoken connection between people and their feline friends by exploring how technology may serve as a conduit for greater human comprehension, empathy, and the goal of a future in which no one's suffering is ignored.
Sachin Jain and Vishal Jain
IEEE
According to a February 2018 WHO report, Asia has the highest brain or CNS cancer death rate. Early tumor detection can save many of these lives. Targeted therapy requires tumor classification. Due to the high cost, lengthy process, and risk of infection associated with diagnosing a tumor, we have an immediate need for non-invasive, cost-effective, and efficient methods of describing and determining the grade of brain tumors. MRI, CT, and other brain scans can detect tumors quickly and safely. This study summarizes brain cancer pathophysiology, imaging modalities, and machine and deep learning-based brain tumor characterization.
Yashu, Vinay Kukreja, Tejinder Pal Singh Brar, and Vishal Jain
IEEE
An important difficulty in computer vision and agricultural technology is the automatic classification of apple cultivars based on their shape, texture, and colour properties. In this article, we suggest a unique method for tackling this problem that combines Random Forest classifiers and Convolutional Neural Networks (CNNs). In total, 5,510 high-resolution photos of the five well-known apple cultivars Gala, Cortland, Pink Lady, Honeycrisp, and Fuji were gathered in our extensive collection. In the first step of our process, a CNN automatically extracts distinguishing features from these photos. Four convolutional layers with ReLU activation make up the CNN architecture, which is followed by layers with maximum pooling to reduce spatial dimension. The Random Forest classifier then uses the output probabilities from the CNN to determine the final classification. We rigorously experimented to show the accuracy and resilience of our model in identifying these apple types. Deep learning and ensemble approaches work together to successfully capture minute variations in shape, texture, and colour. The agriculture and food industries stand to benefit significantly from this research in terms of automated quality control, precise inventory management, and increased customer satisfaction. Our method opens up new directions for future study in automated fruit classification and agricultural automation while demonstrating the potential of contemporary machine learning techniques to simplify difficult agricultural operations.