Saurabh Agarwal
@andong.ac.kr
Korean Research Fellow
Andong National University
Computer science researcher with strong problem-solving skills. Working on state-of-the-art technologies related to image forensics, artificial intelligence, and deep learning. A team player with practical knowledge of multi-cultural & diverse teams, seeking a career in research & academics with 10+ years experience in academics and research.
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Vision and Pattern Recognition, Computational Theory and Mathematics, Artificial Intelligence
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Meenakshi Malhotra, Savita Walia, Chia-Chen Lin, Inderdeep Kaur Aulakh, and Saurabh Agarwal
Springer Science and Business Media LLC
AbstractAir is an essential human necessity, and inhaling filthy air poses a significant health risk. One of the most severe hazards to people’s health is air pollution, and appropriate precautions should be taken to monitor and anticipate its quality in advance. Among all the countries, the air quality in India is decreasing daily, which is a matter of concern to the health department. Many studies use machine learning and Deep learning methods to predict atmospheric pollutant levels, prioritizing accuracy over interpretability. Many research studies confuse researchers and readers about how to proceed with further research. This paper aims to give every detail of the considered air pollutants and brief about the techniques used, their advantages, and challenges faced during pollutant prediction, which leads to a better understanding of the techniques before starting any research related to air pollutant prediction. This paper has given numerous prospective questions on air pollution that piqued the study’s interest. This study discussed various machine and deep learning methods and optimization techniques. Despite all the discussed machine learning and deep learning techniques, the paper concluded that more datasets, better learning techniques, and a variety of suggestions would enhance interpretability while maintaining high accuracy for air pollution prediction. The purpose of this review is also to reveal how a family of neural network algorithms has helped researchers across the globe to predict air pollutant(s).
Ehsanolah Assareh, Parisa Kazemiani-Najafabadi, Ehsan Amiri Rad, Mohammad Firoozzadeh, Ehsan Farhadi, Saurabh Agarwal, Xiaolin Wang, Mehdi Hosseinzadeh, and Wooguil Pak
Elsevier BV
Jenefa Archpaul, Edward Naveen VijayaKumar, Manoranjitham Rajendran, Thompson Stephan, Punitha Stephan, Rishu Chhabra, Saurabh Agarwal, and Wooguil Pak
Springer Science and Business Media LLC
Ishaya Gambo, Rhodes Massenon, Roseline Oluwaseun Ogundokun, Saurabh Agarwal, and Wooguil Pak
Elsevier BV
Uwigize Patrick, S. Koteswara Rao, B. Omkar Lakshmi Jagan, Hari Mohan Rai, Saurabh Agarwal, and Wooguil Pak
MDPI AG
Machine learning, a rapidly growing field, has attracted numerous researchers for its ability to automatically learn from and make predictions based on data. This manuscript presents an innovative approach to estimating the covariance matrix of noise in radar measurements for target tracking, resulting from collaborative efforts. Traditionally, researchers have assumed that the covariance matrix of noise in sonar measurements is present in the vast majority of literature related to target tracking. On the other hand, this research aims to estimate it by employing deep learning algorithms with noisy measurements in range, bearing, and elevation from radar sensors. This collaborative approach, involving multiple disciplines, provides a more precise and accurate covariance matrix estimate. Additionally, the unscented Kalman filter was combined with the gated recurrent unit, multilayer perceptron, convolutional neural network, and long short-term memory to accomplish the task of 3D target tracking in an airborne environment. The quantification of the results was achieved through the use of Monte Carlo simulations, which demonstrated that the convolutional neural network performed better than any other approach. The system was simulated using a Python program, and the proposed method offers higher accuracy and faster convergence time than conventional target tracking methods. This is a demonstration of the potential that collaboration can have in research.
Saurabh Agarwal and Ki-Hyun Jung
Elsevier BV
Saurabh Agarwal and Ki-Hyun Jung
Springer Science and Business Media LLC
Dilip Kumar Sharma, Ravi Prakash Varshney, Saurabh Agarwal, Amel Ali Alhussan, and Hanaa A. Abdallah
Elsevier BV
Saurabh Agarwal and Ki-Hyun Jung
Springer Science and Business Media LLC
Sumit Singh Dhanda, Brahmjit Singh, Chia-Chen Lin, Poonam Jindal, Deepak Panwar, Tarun Kumar Sharma, Saurabh Agarwal, and Wooguil Pak
Institute of Electrical and Electronics Engineers (IEEE)
The most widely used asymmetric cipher is ECC. It can be applied to IoT applications to offer various security services. However, a wide range of sectors have been investigated for applying ECC. The field of elliptic curve cryptographic processors for GF (2191) has received less attention. This study presents a low-resource, high-efficiency architecture for a 191-bit ECC processor. This design uses a novel hybrid Karatsuba multiplier for the multiplication of finite fields. For GF (2191), the Quad-Itoh-Tsuji algorithm has been altered to provide a small-size inversion unit. PlanAhead software synthesizes the CPU, which is then implemented on several Xilinx FPGAs. With savings in slice consumption ranging from 16 to 43 percent, the implemented design is the most restricted compared to the current designs. Compared to previously published designs, it is 3.8–1000 times faster. The elliptic curve scalar multiplication on the Virtex-7 FPGA is computed in $7.24~\\mu $ s. Additionally, the proposed design achieves savings in area-time products of 77 to 90 percent. It may be beneficial for IoT edge devices. It utilizes 3120 mW of power for the operation. A state-of-the-art comparison based on the figure of merit (FoM) reveals that the proposed design outclasses the newest designs by a large margin. It also exhibits a throughput of 138.121 Kbps.
Ishaya Gambo, Rhodes Massenon, Chia-Chen Lin, Roseline Oluwaseun Ogundokun, Saurabh Agarwal, and Wooguil Pak
Institute of Electrical and Electronics Engineers (IEEE)
Mobile app developers struggle to prioritize updates by identifying feature requests within user reviews. While machine learning models can assist, their complexity often hinders transparency and trust. This paper presents an explainable Artificial Intelligence (AI) approach that combines advanced explanation techniques with engaging visualizations to address this issue. Our system integrates a bidirectional Long Short-Term Memory (BiLSTM) model with attention mechanisms, enhanced by Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). We evaluate this approach on a diverse dataset of 150,000 app reviews, achieving an F1 score of 0.82 and 89% accuracy, significantly outperforming baseline Support Vector Machine (F1: 0.66) and Convolutional Neural Network (CNN) (F1: 0.72) models. Our empirical user studies with developers demonstrate that our explainable approach improves trust (27%) when explanations are provided and correct interpretation (73%). The system’s interactive visualizations allowed developers to validate predictions, with over 80% overlap between model-highlighted phrases and human annotations for feature requests. These findings highlight the importance of integrating explainable AI into real-world software engineering workflows. The paper’s results and future directions provide a promising approach for feature request detection in app reviews to create more transparent, trustworthy, and effective AI systems.
Saurabh Agarwal, Savita Walia, and Ki-Hyun Jung
Springer Science and Business Media LLC
Saurabh Agarwal, Savita Walia, and Ki-Hyun Jung
Springer Science and Business Media LLC
Saurabh Agarwal and Ki-Hyun Jung
MDPI AG
Digital images are widely used for informal information sharing, but the rise of fake photos spreading misinformation has raised concerns. To address this challenge, image forensics is employed to verify the authenticity and trustworthiness of these images. In this paper, an efficient scheme for detecting commonly used image smoothing operators is presented while maintaining symmetry. A new lightweight deep-learning network is proposed, which is trained with three different optimizers to avoid downsizing to retain critical information. Features are extracted from the activation function of the global average pooling layer in three trained deep networks. These extracted features are then used to train a classification model with an SVM classifier, resulting in significant performance improvements. The proposed scheme is applied to identify averaging, Gaussian, and median filtering with various kernel sizes in small-size images. Experimental analysis is conducted on both uncompressed and JPEG-compressed images, showing superior performance compared to existing methods. Notably, there are substantial improvements in detection accuracy, particularly by 6.50% and 8.20% for 32 × 32 and 64 × 64 images when subjected to JPEG compression at a quality factor of 70.
Saurabh Agarwal, Dae-Jea Cho, and Ki-Hyun Jung
MDPI AG
Digital image forensics is a crucial emerging technique, as image editing tools can modify them easily. Most of the latest methods can determine whether a specific operator has edited an image. These methods are suitable for high-resolution uncompressed images. In practice, more than one operator is used to modify image contents repeatedly. In this paper, a reliable scheme using information fusion and deep network networks is presented to recognize manipulation operators and the operator’s series on two operators. A transposed convolutional layer improves the performance of low-resolution JPEG compressed images. In addition, a bottleneck technique is utilized to extend the number of transposed convolutional layers. One average pooling layer is employed to preserve the optimal information flow and evade the overfitting concern among the layers. Moreover, the presented scheme can detect two operator series with various factors without including them in training. The experimental outcomes of the suggested scheme are encouraging and better than the existing schemes due to the availability of sufficient statistical evidence.
Saurabh Agarwal, Hyenki Kim, and Ki-Hyun Jung
MDPI AG
Digital images cannot be excluded as part of a popular choice of information representation. Covert information can be easily hidden using images. Several schemes are available to hide covert information and are known as steganography schemes. Steganalysis schemes are applied on stego-images to assess the strength of steganography schemes. In this paper, a new steganalysis scheme is presented to detect stego-images. Predefined kernels guide the set of a conventional convolutional layer, and the tight cohesion provides completely guided training. The learning rate of convolutional layers with predefined kernels is higher than the global learning rate. The higher learning rate of the convolutional layers with predefined kernels assures adaptability according to network training, while still maintaining the basic attributes of high-pass kernels. The Leaky ReLU layer is exploited against the ReLU layer to boost the detection performance. Transfer learning is applied to enhance detection performance. The deep network weights are initialized using the weights of the trained network from high-payload stego-images. The strength of the proposed scheme is verified on the HILL, Mi-POD, S-UNIWARD, and WOW content-adaptive steganography schemes. The results are overwhelming and better than the existing steganalysis schemes.
Arun Kumar Rai, Hari Om, Satish Chand, and Saurabh Agarwal
Elsevier BV
Saurabh Agarwal and Ki-Hyun Jung
Springer Science and Business Media LLC
Saurabh Agarwal and Ki-Hyun Jung
MDPI AG
Detecting image manipulation is essential for investigating the processing history of digital images. In this paper, a novel scheme is proposed to detect the use of low-pass filters in image processing. A new convolutional neural network with a reasonable size was designed to identify three types of low-pass filters. The learning experiences of the three solvers were combined to enhance the detection ability of the proposed approach. Global pooling layers were employed to protect the information loss between the convolutional layers, and a new global variance pooling layer was introduced to improve detection accuracy. The extracted features from the convolutional neural network were mapped to the frequency domain to enrich the feature set. A leaky Rectified Linear Unit (ReLU) layer was discovered to perform better than the traditional ReLU layer. A tri-layered neural network classifier was employed to classify low-pass filters with various parameters into two, four, and ten classes. As detecting low-pass filtering is relatively easy on large-dimension images, the experimental environment was restricted to small images of 30 × 30 and 60 × 60 pixels. The proposed scheme achieved 80.12% and 90.65% detection accuracy on ten categories of images compressed with JPEG and a quality factor 75 on 30 × 30 and 60 × 60 images, respectively.
Dilip Kumar Sharma, Bhuvanesh Singh, Saurabh Agarwal, Nikhil Pachauri, Amel Ali Alhussan, and Hanaa A. Abdallah
MDPI AG
A figurative language expression known as sarcasm implies the complete contrast of what is being stated with what is meant, with the latter usually being rather or extremely offensive, meant to offend or humiliate someone. In routine conversations on social media websites, sarcasm is frequently utilized. Sentiment analysis procedures are prone to errors because sarcasm can change a statement’s meaning. Analytic accuracy apprehension has increased as automatic social networking analysis tools have grown. According to preliminary studies, the accuracy of computerized sentiment analysis has been dramatically decreased by sarcastic remarks alone. Sarcastic expressions also affect automatic false news identification and cause false positives. Because sarcastic comments are inherently ambiguous, identifying sarcasm may be difficult. Different individual NLP strategies have been proposed in the past. However, each methodology has text contexts and vicinity restrictions. The methods are unable to manage various kinds of content. This study suggests a unique ensemble approach based on text embedding that includes fuzzy evolutionary logic at the top layer. This approach involves applying fuzzy logic to ensemble embeddings from the Word2Vec, GloVe, and BERT models before making the final classification. The three models’ weights assigned to the probability are used to categorize objects using the fuzzy layer. The suggested model was validated on the following social media datasets: the Headlines dataset, the “Self-Annotated Reddit Corpus” (SARC), and the Twitter app dataset. Accuracies of 90.81%, 85.38%, and 86.80%, respectively, were achieved. The accuracy metrics were more accurate than those of earlier state-of-the-art models.
D. Sharma, Bhuvanesh Singh, Saurabh Agarwal, Hyunsung Kim and Raj Sharma
Korean Society for Internet Information (KSII)
Raghav Dayal, Parv Nangia, Surbhi Vijh, Sumit Kumar, Saurabh Agarwal, and Shivank Saxena
Springer Nature Singapore
Hyunho Ryu, Hyunsung Kim, Saurabh Agarwal, Dilip Kumar Sharma, Beaton Kapito, and Patrick Ali
IEEE
Remote healthcare services provide more opportunities for communication between doctors and patients, strengthening their relationships and improving patient satisfaction and loyalty. However, those services face specific security and privacy challenges that constrain growth. Furthermore, patients’ information is scattered in several hospitals with various formats, which does not provide the data sovereignty of patients. Data sovereignty is a way for any service to control or regain its data. This paper proposes a privacy-preserving blockchain for remote healthcare services to solve these issues. There is only one database of patient electronic health records in our data structure, and various hospitals could use it. The proposed data structure keeps the patient’s electronic health records and the doctor’s diagnosis and prescription data separate. Each entity in our system has different access rights to its role. The proposed system is based on some security primitives, including pseudonym policy, delegation, data encryption, and digital signature, depending on the privacy requirements. The proposed blockchain could be a solution against the previous centralized system and provide remote healthcare services’ data sovereignty, privacy, and security.
Saurabh Agarwal and Ki-Hyun Jung
MDPI AG
Digital images are very popular and commonly used for hiding crucial data. In a few instances, image steganography is misused for communicating with improper data. In this paper, a robust deep neural network is proposed for the identification of content-adaptive image steganography schemes. Multiple novel strategies are applied to improve detection performance. Two non-trainable convolutional layers is used to guide the proposed CNN with fixed kernels. Thirty-one kernels are used in both non-trainable layers, of which thirty are high-pass kernels and one is the neutral kernel. The layer-specific learning rate is applied for each layer. ReLU with customized thresholding is applied to achieve better performance. In the proposed method, image down-sampling is not performed; only the global average pooling layer is considered in the last part of the network. The experimental results are verified on BOWS2 and BOSSBase image sets. Content-adaptive steganography schemes, such as HILL, Mi-POD, S-UNIWARD, and WOW, are considered for generating the stego images with different payloads. In experimental analysis, the proposed scheme is compared with some of the latest schemes, where the proposed scheme outperforms other state-of-the-art techniques in the most cases.