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Kavita
Department of Computer Science and Engineering, Chandigarh University, Mohali 140413, India

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Journal article
Published: 06 August 2021 in Symmetry
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Due to Internet of Things (IoT), it has become easy to surveil the critical regions. Images are important parts of Surveillance Systems, and it is required to protect the images during transmission and storage. These secure surveillance frameworks are required in IoT systems, because any kind of information leakage can thwart the legal system as well as personal privacy. In this paper, a secure surveillance framework for IoT systems is proposed using image encryption. A hyperchaotic map is used to generate the pseudorandom sequences. The initial parameters of the hyperchaotic map are obtained using partial-regeneration-based non-dominated optimization (PRNDO). The permutation and diffusion processes are applied to generate the encrypted images, and the convolution neural network (CNN) can play an essential role in this part. The performance of the proposed framework is assessed by drawing comparisons with competitive techniques based on security parameters. It shows that the proposed framework provides promising results as compared to the existing techniques.

ACS Style

Gopal Ghosh; Kavita; Divya Anand; Sahil Verma; Danda B. Rawat; Jana Shafi; Zbigniew Marszałek; Marcin Woźniak. Secure Surveillance Systems Using Partial-Regeneration-Based Non-Dominated Optimization and 5D-Chaotic Map. Symmetry 2021, 13, 1447 .

AMA Style

Gopal Ghosh, Kavita, Divya Anand, Sahil Verma, Danda B. Rawat, Jana Shafi, Zbigniew Marszałek, Marcin Woźniak. Secure Surveillance Systems Using Partial-Regeneration-Based Non-Dominated Optimization and 5D-Chaotic Map. Symmetry. 2021; 13 (8):1447.

Chicago/Turabian Style

Gopal Ghosh; Kavita; Divya Anand; Sahil Verma; Danda B. Rawat; Jana Shafi; Zbigniew Marszałek; Marcin Woźniak. 2021. "Secure Surveillance Systems Using Partial-Regeneration-Based Non-Dominated Optimization and 5D-Chaotic Map." Symmetry 13, no. 8: 1447.

Journal article
Published: 01 August 2021 in IEEE Transactions on Industrial Informatics
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With the proliferation of Information and Communication Technology (ICT) in every walks of the society, including healthcare services; digitization and increased sophistication have been gaining pace. With increased patient volumes recording and management of patients data digital healthcare alternatives, particularly, Electronic Healthcare Record (EHR) has gained prominence. However, traditional EHR based systems are plagued by data loss risks, security and immutability consensus over health records, gapped communication among constituted hospitals, inefficient clinical data retrieval systems among others. Blockchain has been developed as a decentralized technology that holds the promise to address the aforesaid facilities in EHR based systems. This paper presents a patient-centric design of a decentralized healthcare management system with blockchain based EHR using javascript based Smart Contracts. A working prototype based on hyperledger fabric and composer technology has also been implemented that guarantees the security of proposed model. Experiments with the hyperledger caliper benchmarking tool provides performance of metrics such as latency, throughput, resource utilization and so on under varied scenarios and control parameters and the results affirm the efficacy of the proposed approach.

ACS Style

Akhilendra Pratap Singh; Nihar Ranjan Pradhan; Ashish Kr. Kr. Luhach; Shivanshu Agnihotri; Noor Zaman Jhanjhi; Sahil Verma; Kavita; Uttam Ghosh; Diptendu Sinha Roy. A Novel Patient-Centric Architectural Framework for Blockchain-Enabled Healthcare Applications. IEEE Transactions on Industrial Informatics 2021, 17, 5779 -5789.

AMA Style

Akhilendra Pratap Singh, Nihar Ranjan Pradhan, Ashish Kr. Kr. Luhach, Shivanshu Agnihotri, Noor Zaman Jhanjhi, Sahil Verma, Kavita, Uttam Ghosh, Diptendu Sinha Roy. A Novel Patient-Centric Architectural Framework for Blockchain-Enabled Healthcare Applications. IEEE Transactions on Industrial Informatics. 2021; 17 (8):5779-5789.

Chicago/Turabian Style

Akhilendra Pratap Singh; Nihar Ranjan Pradhan; Ashish Kr. Kr. Luhach; Shivanshu Agnihotri; Noor Zaman Jhanjhi; Sahil Verma; Kavita; Uttam Ghosh; Diptendu Sinha Roy. 2021. "A Novel Patient-Centric Architectural Framework for Blockchain-Enabled Healthcare Applications." IEEE Transactions on Industrial Informatics 17, no. 8: 5779-5789.

Review
Published: 12 July 2021 in Sensors
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In the modern era, deep learning techniques have emerged as powerful tools in image recognition. Convolutional Neural Networks, one of the deep learning tools, have attained an impressive outcome in this area. Applications such as identifying objects, faces, bones, handwritten digits, and traffic signs signify the importance of Convolutional Neural Networks in the real world. The effectiveness of Convolutional Neural Networks in image recognition motivates the researchers to extend its applications in the field of agriculture for recognition of plant species, yield management, weed detection, soil, and water management, fruit counting, diseases, and pest detection, evaluating the nutrient status of plants, and much more. The availability of voluminous research works in applying deep learning models in agriculture leads to difficulty in selecting a suitable model according to the type of dataset and experimental environment. In this manuscript, the authors present a survey of the existing literature in applying deep Convolutional Neural Networks to predict plant diseases from leaf images. This manuscript presents an exemplary comparison of the pre-processing techniques, Convolutional Neural Network models, frameworks, and optimization techniques applied to detect and classify plant diseases using leaf images as a data set. This manuscript also presents a survey of the datasets and performance metrics used to evaluate the efficacy of models. The manuscript highlights the advantages and disadvantages of different techniques and models proposed in the existing literature. This survey will ease the task of researchers working in the field of applying deep learning techniques for the identification and classification of plant leaf diseases.

ACS Style

Vijaypal Dhaka; Sangeeta Meena; Geeta Rani; Deepak Sinwar; Kavita Kavita; Muhammad Ijaz; Marcin Woźniak. A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases. Sensors 2021, 21, 4749 .

AMA Style

Vijaypal Dhaka, Sangeeta Meena, Geeta Rani, Deepak Sinwar, Kavita Kavita, Muhammad Ijaz, Marcin Woźniak. A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases. Sensors. 2021; 21 (14):4749.

Chicago/Turabian Style

Vijaypal Dhaka; Sangeeta Meena; Geeta Rani; Deepak Sinwar; Kavita Kavita; Muhammad Ijaz; Marcin Woźniak. 2021. "A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases." Sensors 21, no. 14: 4749.

Conference paper
Published: 31 December 2020 in IOP Conference Series: Materials Science and Engineering
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In this paper, we are going to evaluate various wireless network security related issues and how do they affect the quality of service of a wireless network and Mobile AdHoc Networks(MANETs). The time systems are increasingly required Getting educated more and more. Systems are simple Drastically converting the structure into more functional and dynamic. Computer networks have undergone a major move from cable to wireless A recent development has been networks and fast wireless infrastructure. MANET has appeared between many other cellular networks. Why? Why? dynamic topology without any centralization, conventional path MANETs do not comply with protocols and authentication systems. These MANETs are responsive to a lot of people It is not feasible in other networks to attack and participate in disruptive operations. A Wireless Network can be a very vulnerable entity and is always susceptible to various types of attacks and attackers, we are going to discuss a few of these attack and how do they affect the overall network performance, finally we are going to evaluate some of the solutions offered and how they improve the network.

ACS Style

Keshav Kumar; Sahil Verma; Kavita; Nz Jhanjhi; M N Talib. A Survey of The Design and Security Mechanisms of The Wireless Networks and Mobile Ad-Hoc Networks. IOP Conference Series: Materials Science and Engineering 2020, 993, 012063 .

AMA Style

Keshav Kumar, Sahil Verma, Kavita, Nz Jhanjhi, M N Talib. A Survey of The Design and Security Mechanisms of The Wireless Networks and Mobile Ad-Hoc Networks. IOP Conference Series: Materials Science and Engineering. 2020; 993 (1):012063.

Chicago/Turabian Style

Keshav Kumar; Sahil Verma; Kavita; Nz Jhanjhi; M N Talib. 2020. "A Survey of The Design and Security Mechanisms of The Wireless Networks and Mobile Ad-Hoc Networks." IOP Conference Series: Materials Science and Engineering 993, no. 1: 012063.

Conference paper
Published: 31 December 2020 in IOP Conference Series: Materials Science and Engineering
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In the current data transmission situation, digital images constitute a large part of visual communication. Their security is thus an essential field of concern. This paper analyses several chaotic maps for the encryption of images and discusses their advantages and disadvantages. The characteristics of chaotic maps such as stochastic, ergodicity and highly sensitive initial conditions allow them reliable to encrypt images. Many of the previously proposed imaging approaches used chaotic, low-dimensional charts that display the lowest security and have very less potential to handle force and attacks. To solve this challenge, scientists have proposed multiple broad chaotic charts. In this paper the characteristics and techniques of some chaotic maps used to encrypt images were reviewed. Also for images like boat, airplane, peppers, lake, house chaotic encryption is applied and analysed.

ACS Style

Gopal Ghosh; Kavita; Sahil Verma; Nz Jhanjhi; M N Talib. Secure Surveillance System Using Chaotic Image Encryption Technique. IOP Conference Series: Materials Science and Engineering 2020, 993, 012062 .

AMA Style

Gopal Ghosh, Kavita, Sahil Verma, Nz Jhanjhi, M N Talib. Secure Surveillance System Using Chaotic Image Encryption Technique. IOP Conference Series: Materials Science and Engineering. 2020; 993 (1):012062.

Chicago/Turabian Style

Gopal Ghosh; Kavita; Sahil Verma; Nz Jhanjhi; M N Talib. 2020. "Secure Surveillance System Using Chaotic Image Encryption Technique." IOP Conference Series: Materials Science and Engineering 993, no. 1: 012062.

Journal article
Published: 22 July 2020 in IEEE Access
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Nonalcoholic Fatty Liver Disease (NAFLD) is the most common cause of chronic liver disease around the world. Remaining silent in the early stages makes its evaluation a challenge. Liver biopsy is still the gold standard method used to classify NAFLD stages but has important sample error issues and subjectivity in the interpretation. This research is an effort to overcome liver biopsy to a possible extent by forming a non-invasive clinical spectrum. This paper proposed an intelligent scheme using the forward algorithm, Viterbi algorithm, and Baum-welch algorithm for examining the disease, and a new clinical spectrum is introduced that incorporates most likely attributes associated with NAFLD stages. The experimental results verify that our method is efficient in distinguishing the credibility of an attribute being associated with a specific stage in case it is linked with more than one stage. Moreover, the proposed scheme can successfully estimate the likelihood of stage progression and supports medical knowledge more proficiently and realistically.

ACS Style

Aman Singh; Pinku Nath; Vivek Singhal; Divya Anand; Kavita; Sahil Verma; Tzung-Pei Hong. A New Clinical Spectrum for the Assessment of Nonalcoholic Fatty Liver Disease Using Intelligent Methods. IEEE Access 2020, 8, 138470 -138480.

AMA Style

Aman Singh, Pinku Nath, Vivek Singhal, Divya Anand, Kavita, Sahil Verma, Tzung-Pei Hong. A New Clinical Spectrum for the Assessment of Nonalcoholic Fatty Liver Disease Using Intelligent Methods. IEEE Access. 2020; 8 (99):138470-138480.

Chicago/Turabian Style

Aman Singh; Pinku Nath; Vivek Singhal; Divya Anand; Kavita; Sahil Verma; Tzung-Pei Hong. 2020. "A New Clinical Spectrum for the Assessment of Nonalcoholic Fatty Liver Disease Using Intelligent Methods." IEEE Access 8, no. 99: 138470-138480.

Journal article
Published: 09 July 2020 in Sustainability
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Lately, the Internet of Things (IoT) has opened up new opportunities to business and enterprises; however, the cost of providing security and privacy best practices is preventing numerous organizations from adopting this innovation. With the proliferation of connecting devices in IoT, significant increases have been recorded in energy use, harmful contamination and e-waste. A new paradigm of green IoT is aimed at designing environmentally friendly protocols by reducing the carbon impact and promote efficient techniques for energy use. There is a consistent effort of designing distinctive security structures to address vulnerabilities and attacks. However, most of the existing schemes are not energy efficient. To bridge the gap, we propose the hybrid logical security framework (HLSF), which offers authentication and data confidentiality in IoT. HLSF uses a lightweight cryptographic mechanism for unique authentication. It enhances the level of security and provides better network functionalities using energy-efficient schemes. With extensive simulation, we compare HLSF with two existing popular security schemes, namely, constrained application protocol (CoAP) and object security architecture for IoT (OSCAR). The result shows that HLSF outperforms CoAP and OSCAR in terms of throughput with low computational, storage and energy overhead, even in the presence of attackers.

ACS Style

Isha Batra; Sahil Verma; Arun Malik; Kavita; Uttam Ghosh; Joel J. P. C. Rodrigues; Gia Nhu Nguyen; A. S. M. Sanwar Hosen; Vinayagam Mariappan. Hybrid Logical Security Framework for Privacy Preservation in the Green Internet of Things. Sustainability 2020, 12, 5542 .

AMA Style

Isha Batra, Sahil Verma, Arun Malik, Kavita, Uttam Ghosh, Joel J. P. C. Rodrigues, Gia Nhu Nguyen, A. S. M. Sanwar Hosen, Vinayagam Mariappan. Hybrid Logical Security Framework for Privacy Preservation in the Green Internet of Things. Sustainability. 2020; 12 (14):5542.

Chicago/Turabian Style

Isha Batra; Sahil Verma; Arun Malik; Kavita; Uttam Ghosh; Joel J. P. C. Rodrigues; Gia Nhu Nguyen; A. S. M. Sanwar Hosen; Vinayagam Mariappan. 2020. "Hybrid Logical Security Framework for Privacy Preservation in the Green Internet of Things." Sustainability 12, no. 14: 5542.

Journal article
Published: 01 July 2020 in IEEE Access
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Medical Imaging is the most significant technique that constitutes information needed to diagnose and make the right decisions for treatment. These images suffer from inadequate contrast and noise that occurs during image acquisition. Thus, denoising and contrast enhancement is crucial in increasing the visual quality of the images for obtaining quantitative measures. In this research, an innovative and improvised denoising technique is implemented that applies a sparse aware with convolution neural network (SA_CNN) for investigating various medical modalities. To evaluate and validate, the convolution neural network utilizes patch creation and dictionary methods for obtaining information. The proposed framework is predominant to other current approaches by employing image assessment quantitative measures like peak signal to noise ratio (PSNR), structural similarity index (SSIM), and mean squared error (MSE). The study also optimizes the computational time to achieve increased efficiency and better visual quality of the image. Furthermore, the widespread use of the Internet of Healthcare Things (IoHT) helps to provide security with vault and challenge schemes between IoT devices and servers.

ACS Style

Sujeet More; Jimmy Singla; Sahil Verma; Kavita; Uttam Ghosh; Joel J. P. C. Rodrigues; A. S. M. Sanwar Hosen; In-Ho Ra. Security Assured CNN-Based Model for Reconstruction of Medical Images on the Internet of Healthcare Things. IEEE Access 2020, 8, 126333 -126346.

AMA Style

Sujeet More, Jimmy Singla, Sahil Verma, Kavita, Uttam Ghosh, Joel J. P. C. Rodrigues, A. S. M. Sanwar Hosen, In-Ho Ra. Security Assured CNN-Based Model for Reconstruction of Medical Images on the Internet of Healthcare Things. IEEE Access. 2020; 8 ():126333-126346.

Chicago/Turabian Style

Sujeet More; Jimmy Singla; Sahil Verma; Kavita; Uttam Ghosh; Joel J. P. C. Rodrigues; A. S. M. Sanwar Hosen; In-Ho Ra. 2020. "Security Assured CNN-Based Model for Reconstruction of Medical Images on the Internet of Healthcare Things." IEEE Access 8, no. : 126333-126346.

Journal article
Published: 25 June 2020 in IEEE Access
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Wireless technology and the latest developments in a mobile object, has led to a Mobile Ad Hoc network (MANET), which is a collection of mobile nodes that are communicating with each other without requiring any fixed infrastructure. Due to the dynamic nature with a decentralized system, these networks are susceptible to different attacks such as Black Hole Attack (BHA), Gray Hole Attack (GHA), Sink Hole Attack (SHA) and many more. Several researchers have worked for the detection and mitigation of individual attacks, either GHA or BHA nodes. But the protection of MANET against a dual-threat is scarce. In this paper, the protection against dual attacks has been presented for BHA and GHA by using the concept of Artificial Neural Network (ANN) as a deep learning algorithm along with the swarm-based Artificial Bee Colony (ABC) optimization technique. The performance of the system has been increased by the selection of appropriate and best nodes for data packets transmission which is explained in the result section of this paper. For the network designing and simulation purposes, MATLAB software is used with communication and neural network toolboxes. The examined results show that the presented protocol performs better in contrast to the existing work under black hole as well as gray hole attack condition. A mobile ad hoc network (MANET) is a collection of mobile nodes that dynamically form a temporary network without using any existing network infrastructure.

ACS Style

Pooja Rani; Kavita; Sahil Verma; Gia Nhu Nguyen. Mitigation of Black Hole and Gray Hole Attack Using Swarm Inspired Algorithm With Artificial Neural Network. IEEE Access 2020, 8, 121755 -121764.

AMA Style

Pooja Rani, Kavita, Sahil Verma, Gia Nhu Nguyen. Mitigation of Black Hole and Gray Hole Attack Using Swarm Inspired Algorithm With Artificial Neural Network. IEEE Access. 2020; 8 ():121755-121764.

Chicago/Turabian Style

Pooja Rani; Kavita; Sahil Verma; Gia Nhu Nguyen. 2020. "Mitigation of Black Hole and Gray Hole Attack Using Swarm Inspired Algorithm With Artificial Neural Network." IEEE Access 8, no. : 121755-121764.

Journal article
Published: 15 June 2020 in IEEE Access
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The study focuses on the artificial intelligence empowered road vehicle- train collision risk prediction assessment, which may lead to the development of a road vehicle-train collision avoidance system for unmanned railway level crossings. The study delimits itself around the road vehicle-train collisions at unmanned railway level crossings on single line rail-road sections. The first objective of the study revolves around the rail-road collision risk evaluation by the development of road vehicle-train collision frequency and severity prediction model using Poisson and Gamma-log regression techniques respectively. Another study objective is the collision modification factor implementation on predicted risk factors, to reduce the road vehicle-train collision risk at the crossings. The collision risk has been predicted to be 3.52 times higher and 77% lower in one direction while in other directions it is 2.95 times higher and 80% lower than average risk at all unmanned railway level crossings. With collision modification factor application on higher risk contributing factors i.e. ‘crossing angle’ and ‘train visibility, it predicts to reduce the road vehicle-train collision risk to 85% approximately.

ACS Style

Vivek Singhal; S. S. Jain; Divya Anand; Aman Singh; Sahil Verma; Kavita; Joel J. P. C. Rodrigues; Noor Zaman Jhanjhi; Uttam Ghosh; Ohyun Jo; Celestine Iwendi. Artificial Intelligence Enabled Road Vehicle-Train Collision Risk Assessment Framework for Unmanned Railway Level Crossings. IEEE Access 2020, 8, 113790 -113806.

AMA Style

Vivek Singhal, S. S. Jain, Divya Anand, Aman Singh, Sahil Verma, Kavita, Joel J. P. C. Rodrigues, Noor Zaman Jhanjhi, Uttam Ghosh, Ohyun Jo, Celestine Iwendi. Artificial Intelligence Enabled Road Vehicle-Train Collision Risk Assessment Framework for Unmanned Railway Level Crossings. IEEE Access. 2020; 8 ():113790-113806.

Chicago/Turabian Style

Vivek Singhal; S. S. Jain; Divya Anand; Aman Singh; Sahil Verma; Kavita; Joel J. P. C. Rodrigues; Noor Zaman Jhanjhi; Uttam Ghosh; Ohyun Jo; Celestine Iwendi. 2020. "Artificial Intelligence Enabled Road Vehicle-Train Collision Risk Assessment Framework for Unmanned Railway Level Crossings." IEEE Access 8, no. : 113790-113806.