Chiranji Lal Chowdhary
@vit.ac.in
Associate Professor
Vellore Institute of Technology Vellore, India
RESEARCH INTERESTS
Image Processing, Pattern Recognition, Deep Learning, Neural Networks, Computational Intelligence
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Chiranji Lal Chowdhary, R. K. Nadesh, and P. Kumaresan
Springer Nature Singapore
Harpreet Kaur Channi and Chiranji Lal Chowdhary
Springer Nature Singapore
Arjun Singh, Vijay Shankar Sharma, Shakila Basheer, and Chiranji Lal Chowdhary
MDPI AG
Ensuring the security of picture data on a network presents considerable difficulties because of the requirement for conventional embedding systems, which ultimately leads to subpar performance. It poses a risk of unauthorized data acquisition and misuse. Moreover, the previous image security-based techniques faced several challenges, including high execution times. As a result, a novel framework called Graph Convolutional-Based Twofish Security (GCbTS) was introduced to secure the images used in healthcare. The medical data are gathered from the Kaggle site and included in the proposed architecture. Preprocessing is performed on the data inserted to remove noise, and the hash 1 value is computed. Using the generated key, these separated images are put through the encryption process to encrypt what they contain. Additionally, to verify the user’s identity, the encrypted data calculates the hash 2 values contrasted alongside the hash 1 value. Following completion of the verification procedure, the data are restored to their original condition and made accessible to authorized individuals by decrypting them with the collective key. Additionally, to determine the effectiveness, the calculated results of the suggested model are connected to the operational copy, which depends on picture privacy.
Sinh Cong Lam, Chiranji Lal Chowdhary, Tushar Hrishikesh Jaware, and Subrata Chowdhury
CRC Press
Subhranshu Sekhar Tripathy, Sujit Bebortta, Chiranji Lal Chowdhary, Tanmay Mukherjee, SeongKi Kim, Jana Shafi, and Muhammad Fazal Ijaz
Elsevier BV
Sivashankari Rajadurai, Kumaresan Perumal, Muhammad Fazal Ijaz, and Chiranji Lal Chowdhary
MDPI AG
Malignant lymphoma, which impacts the lymphatic system, presents diverse challenges in accurate diagnosis due to its varied subtypes—chronic lymphocytic leukemia (CLL), follicular lymphoma (FL), and mantle cell lymphoma (MCL). Lymphoma is a form of cancer that begins in the lymphatic system, impacting lymphocytes, which are a specific type of white blood cell. This research addresses these challenges by proposing ensemble and non-ensemble transfer learning models employing pre-trained weights from VGG16, VGG19, DenseNet201, InceptionV3, and Xception. For the ensemble technique, this paper adopts a stack-based ensemble approach. It is a two-level classification approach and best suited for accuracy improvement. Testing on a multiclass dataset of CLL, FL, and MCL reveals exceptional diagnostic accuracy, with DenseNet201, InceptionV3, and Xception exceeding 90% accuracy. The proposed ensemble model, leveraging InceptionV3 and Xception, achieves an outstanding 99% accuracy over 300 epochs, surpassing previous prediction methods. This study demonstrates the feasibility and efficiency of the proposed approach, showcasing its potential in real-world medical applications for precise lymphoma diagnosis.
Chiranji Lal Chowdhary, Siva Rama Krishnan Somayaji, Vijay Kumar, and Sandeep Singh Sengar
Springer Nature Switzerland
C. Vanmathi, Harpreet Kaur Channi, Muhammad Fazal Ijaz, Ritik Srivastava, Sai Meghana Bommana, Lauryn Arora, and Chiranji Lal Chowdhary
Springer Nature Switzerland
Chiranji Lal Chowdhary and Abhishek Ranjan
Springer Nature Switzerland
Musiri Kailasanathan Nallakaruppan, Chiranji Lal Chowdhary, SivaramaKrishnan Somayaji, Himakshi Chaturvedi, Sujatha. R, Hafiz Tayyab Rauf, and Mohamed Sharaf
American Institute of Mathematical Sciences (AIMS)
<abstract><p>Fake face identity is a serious, potentially fatal issue that affects every industry from the banking and finance industry to the military and mission-critical applications. This is where the proposed system offers artificial intelligence (AI)-based supported fake face detection. The models were trained on an extensive dataset of real and fake face images, incorporating steps like sampling, preprocessing, pooling, normalization, vectorization, batch processing and model training, testing-, and classification via output activation. The proposed work performs the comparative analysis of the three fusion models, which can be integrated with Generative Adversarial Networks (GAN) based on the performance evaluation. The Model-3, which contains the combination of DenseNet-201+ResNet-102+Xception, offers the highest accuracy of 0.9797, and the Model-2 with the combination of DenseNet-201+ResNet-50+Inception V3 offers the lowest loss value of 0.1146; both are suitable for the GAN integration. Additionally, the Model-1 performs admirably, with an accuracy of 0.9542 and a loss value of 0.1416. A second dataset was also tested where the proposed Model-3 provided maximum accuracy of 86.42% with a minimum loss of 0.4054.</p></abstract>
Thandra Jithendra, Mohammad Zubair Khan, S. Sharief Basha, Raja Das, A. Divya, Chiranji Lal Chowdhary, Abdulrahman Alahmadi, and Ahmed H. Alahmadi
Institute of Electrical and Electronics Engineers (IEEE)
The adoption of adaptive neuro-fuzzy inference systems (ANFIS) and metaheuristic optimization approaches has been widely observed in recent research. Even so, integrating these methods improves the model’s capability to solve complex problems. A novel enhanced prediction method based on COOT bird optimization was developed for selecting the optimal parameters of ANFIS in the current study. This method combines COOT optimization with ANFIS to model the quality of service (QoS) characteristics of web services by using the adaptive neuro-fuzzy inference system COOT (ANFIS-COOT). In this instance, the quality of the web service (QWS) dataset was obtained from the GitHub database, which consists of 120 web services data, and then evaluated using the presented model on the dataset for estimating response time and throughput of web services. As significant evidence of ANFIS-COOT’s efficiency, the similar QWS data set is analyzed using four different prediction models: ANFIS, ANFIS-Beetle Antennae Search (ANFIS-BAS), ANFIS-Reptile Search Algorithm (ANFIS-RSA), and ANFIS-Snake Optimizer (ANFIS-SO). Moreover, the exploratory study used statistical benchmarks such as root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and determination coefficient (<inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>) to emphasize the accuracy of the proposed model. Based on analysis results, the presented model achieved optimal values of RMSE (59.7473), MAE (15.8531), MAPE (0.0705), and <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> of 96.32 %, as well as RMSE (1.335), MAE (1.1255), MAPE (0.1818), and <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> of 97.12 % for modelling response time and throughput of web services, compared to other models. Eventually, this report demonstrates the viability of the ANFIS-COOT while tackling a complex problem and improving predictive performance.
Sujit Bebortta, Subhranshu Sekhar Tripathy, Shakila Basheer, and Chiranji Lal Chowdhary
MDPI AG
In contemporary healthcare, the prediction and identification of cardiac diseases is crucial. By leveraging the capabilities of Internet of Things (IoT)-enabled devices and Electronic Health Records (EHRs), the healthcare sector can largely benefit to improve patient outcomes by increasing the accuracy of disease prediction. However, protecting data privacy is essential to promote participation and adhere to rules. The suggested methodology combines EHRs with IoT-generated health data to predict heart disease. For its capacity to manage high-dimensional data and choose pertinent features, a soft-margin L1-regularised Support Vector Machine (sSVM) classifier is used. The large-scale sSVM problem is successfully solved using the cluster primal–dual splitting algorithm, which improves computational complexity and scalability. The integration of federated learning provides a cooperative predictive analytics methodology that upholds data privacy. The use of a federated learning framework in this study, with a focus on peer-to-peer applications, is crucial for enabling collaborative predictive modeling while protecting the confidentiality of each participant’s private medical information.
Basanta Kumar Swain, Chiranji Lal Chowdhary, and Rakesh Gain
IGI Global
Sign language recognition is a worldwide concern across the globe. The use of technology has a scope in aiding the necessary help in the recognition of sign language. The major challenge lies in detecting and understanding signs, as the language differs across the various geographical regions, and there are no specific rules for understanding them. Hence, this research article uses a transfer learning algorithm with TensorFlow object detection to recognize Indian sign language. The proposed model has achieved an accuracy of around 97.87% for different types of sentences used in the experimentation. The main advantage of the proposed model is that it is feasible to identify Indian sign language and produce the corresponding voice output using speech synthesis system. The system is helpful to the deaf and dumb community's society and encourages such people's upliftment.
Harpreet Kaur Channi and Chiranji Lal Chowdhary
IGI Global
Quantum computing might accelerate diagnosis, personalize treatment, and optimize prices in healthcare. Quantum-enhanced machine learning is important. Quantum Computing and Healthcare are innovative partnerships. The healthcare sector advances with new technologies. Quantum computing was bound to revolutionize healthcare. With Quantum technology on the rise, a new age of computing is coming. Quantum technology and mechanics is an abstract technical subject, yet it might revolutionize healthcare and other sectors. Quantum computing is real. Quantum has great promise in healthcare. AI and other technologies are also significant in healthcare. Such technologies improve healthcare treatments, diagnoses, and assistance. Quantum Computing intends to change healthcare. Personalized healthcare hinges on genomes, physiology, and pharmacokinetics. Thus, more clinical data must be processed. Quantum Computing is the solution. This article explains quantum computing's influence on healthcare and its uses.
Harpreet Kaur Channi and Chiranji Lal Chowdhary
IGI Global
Numerous industries, including e-healthcare, are capitalizing on and using blockchain and internet of things (IoT) technology. IoT devices may collect patient vitals and other sensory information in real-time, which medical professionals can then examine. All information gathered from the internet of things is stored, processed, and computed in one place. Such concentration raises concerns since it increases the likelihood of a catastrophic failure, distrust, tampering with data, and even the circumvention of privacy protections. By offering decentralized processing and storage for IoT data, blockchain has the potential to address these critical issues. As a result, designing a decentralized IoT-based e-healthcare system that incorporates IoT and blockchain technology might be a viable option. First, the authors provide some context about blockchain in this essay. The viability of blockchain systems for the internet of things-based e-healthcare is then assessed.
Sabrina Manickam, Laasya Yarlagadda, Shynu Padinjappurathu Gopalan, and Chiranji Lal Chowdhary
Institute of Electrical and Electronics Engineers (IEEE)
Digital Twins possess the capability to create virtual representations of a device’s components and dynamics. They transcend static images or blueprints, offering intricate models that reveal the entire lifecycle of system design, construction, and operation. Digital Twins now spearhead the virtual revolution, equipped to faithfully replicate each component through sensor-driven data collection. This replication aids in informed decision-making, monitoring complex systems, and validating novel products and services. Numerous companies already leverage digital twins within these domains to detect issues and enhance productivity. Conversely, accurate data collection and analysis from digital twins can pose challenges, potentially introducing ambiguity in decision-making and complicating object lifecycle management. Consequently, ongoing debates and discussions revolve around the fundamental concepts, frameworks, and technologies of digital twins. In this work, we delve into the realm of Industrial Applications of Digital Twins, exploring their merits and limitations.
Sandeep Kumar Sharma, Vijay Shankar Sharma, Shakila Basheer, Amit Chaurasia, and Chiranji Lal Chowdhary
Institute of Electrical and Electronics Engineers (IEEE)
Object formation is imperative to the recent computer vision, pattern recognition, healthcare, and automation applications. The objects are generated from images by defining edges and the segmentation process. This article introduced a novel method, Outer Totality Cellular Automata (OTCA), for defining actual and continuous edges of the image objects. The OTCA analysis nearby 25 neighbourhood pixels of all the pixels and generate a unique and efficient threshold. The proposed method has three primary functions, i.e. vitality, rule mapping, and improved morphological functions. The key objectives are image smoothing, neighbourhood analysis, defining game of life rule, and edges smoothing. Notably, the proposed method aimed to segment different coloured images, i.e., RGB, HSV, and YUV. The proposed method also aimed to produce more truthful results on blurred, reflected, shaded night vision images. The experimental process demonstrates using standard open-source datasets and validated using image quality assessment parameters, i.e., entropy, PSNR, SSIM, and MSE. The results claim 3% – 12% more structural analogous, factual, and accurate than existing classical methods and recent searches.
Sandeep Kumar Sharma, Amit Chaurasia, Vijay Shankar Sharma, Chiranji Lal Chowdhary, and Shakila Basheer
Institute of Electrical and Electronics Engineers (IEEE)
Resource selection, sharing, and aggregation are the key functions of grid computing. However, managing the resources in a grid-based environment is a stimulating task. It is necessary to update the topographical dispersal of the resources possessed by the various organisations with proper load distribution, and availability patterns. Different types of Users and servers have specific objectives and needs that could be achieved using a grid environment. This article suggests a cost-effective efficient framework for resource management in grid computing to look at and address the resource management difficulties. The proposed framework has three main functions, which help in grid construction, load balancing, and resource allocation. A Genetic engineering approach has been implemented to establish a relationship between the resource pool and the jobs of the nodes that improve resource utilization. The proposed methodology also optimizes the overall cost by minimizing turnaround time. The results of the proposed research are compared with commonly used algorithms and claim 1.5 to 10% better results.
M. Lawanya Shri, E. Gangadevi, K. Santhi, and Chiranji Lal Chowdhary
Apple Academic Press
Mathew D. A. Steve, Shree N. Durga, and Lal Chowdhary Chiranji
International Hellenic University