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Piyush Kumar Shukla
Department of Computer Science and Engineering, UIT-RGPV, Bhopal, India

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Article
Published: 04 August 2021 in Wireless Personal Communications
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Due to the increasing demand for IoMT applications in numerous fields such as healthcare, smart city, smart grids, industrial internet, etc. The privacy and security become a major issue in front of various researchers working in this field. This work proposed a lightweight image encryption algorithm based on a logistic-tent map and crossover operator of a genetic algorithm. Various 1-D chaotic maps are discussed in the literature review, but in some cases, hybrid 1-D chaotic maps have higher performance than simple 1-D chaotic maps. So 1-D chaotic map along with a crossover operator is used in this work. Here logistic-tent map and crossover are used to generate the random session key for each image encryption. Also, a crossover operator is used in encryption rounds for increasing confusion and diffusion. Here in this work, for each image encryption, a new intelligent session key is generated. The strength of the proposed image cryptographic scheme is assessed against resistance to the differential attack (UACI and NPCR), statistical attack (histogram analysis, correlation coefficient and information entropy) and sensitivity to the secret key. The extensive experiments of performance and security assessment show that the proposed cryptographic image scheme is secure enough to withstand all potential cryptanalytic attacks.

ACS Style

Manish Gupta; Kamlesh Kumar Gupta; Mohammad R. Khosravi; Piyush Kumar Shukla; Sandeep Kautish; Achyut Shankar. An Intelligent Session Key-Based Hybrid Lightweight Image Encryption Algorithm Using Logistic-Tent Map and Crossover Operator for Internet of Multimedia Things. Wireless Personal Communications 2021, 1 -22.

AMA Style

Manish Gupta, Kamlesh Kumar Gupta, Mohammad R. Khosravi, Piyush Kumar Shukla, Sandeep Kautish, Achyut Shankar. An Intelligent Session Key-Based Hybrid Lightweight Image Encryption Algorithm Using Logistic-Tent Map and Crossover Operator for Internet of Multimedia Things. Wireless Personal Communications. 2021; ():1-22.

Chicago/Turabian Style

Manish Gupta; Kamlesh Kumar Gupta; Mohammad R. Khosravi; Piyush Kumar Shukla; Sandeep Kautish; Achyut Shankar. 2021. "An Intelligent Session Key-Based Hybrid Lightweight Image Encryption Algorithm Using Logistic-Tent Map and Crossover Operator for Internet of Multimedia Things." Wireless Personal Communications , no. : 1-22.

Conference paper
Published: 05 May 2021 in Advances in Intelligent Systems and Computing
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This work utilizes a novel image encryption technique for block image encryption with the help of nonlinear 2D chaotic map and DNA sequences (NL2DCM-DNA). 2D chaotic map has generated the several key sequences for encryption, and DNA rules are applied to perform fast block encryption of image. Nonlinearity of 2D chaotic map and complexity of DNA sequences are used to perform highly secure image encryption. The experimental outputs illustrate the higher efficiency of NL2DCM-DNA in terms of security, attack resilience, entropy, histogram, running time and diffusion against some previous image encryption algorithms.

ACS Style

Shalini Stalin; Priti Maheshwary; Piyush Kumar Shukla. Nonlinear 2D Chaotic Map and DNA (NL2DCM-DNA) Sequences-Based Fast and Secure Block Image Encryption. Advances in Intelligent Systems and Computing 2021, 69 -76.

AMA Style

Shalini Stalin, Priti Maheshwary, Piyush Kumar Shukla. Nonlinear 2D Chaotic Map and DNA (NL2DCM-DNA) Sequences-Based Fast and Secure Block Image Encryption. Advances in Intelligent Systems and Computing. 2021; ():69-76.

Chicago/Turabian Style

Shalini Stalin; Priti Maheshwary; Piyush Kumar Shukla. 2021. "Nonlinear 2D Chaotic Map and DNA (NL2DCM-DNA) Sequences-Based Fast and Secure Block Image Encryption." Advances in Intelligent Systems and Computing , no. : 69-76.

Research article
Published: 25 February 2021 in Mathematical Problems in Engineering
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COVID-19 is a new disease, caused by the novel coronavirus SARS-CoV-2, that was firstly delineated in humans in 2019. Coronaviruses cause a range of illness in patients varying from common cold to advanced respiratory syndromes such as Severe Acute Respiratory Syndrome (SARS-CoV) and Middle East Respiratory Syndrome (MERS-CoV). The SARS-CoV-2 outbreak has resulted in a global pandemic, and its transmission is increasing at a rapid rate. Diagnostic testing and approaches provide a valuable tool for doctors and support them with the screening process. Automatic COVID-19 identification in chest X-ray images can be useful to test for COVID-19 infection at a good speed. Therefore, in this paper, a framework is designed by using Convolutional Neural Networks (CNN) to diagnose COVID-19 patients using chest X-ray images. A pretrained GoogLeNet is utilized for implementing the transfer learning (i.e., by replacing some sets of final network CNN layers). 20-fold cross-validation is considered to overcome the overfitting quandary. Finally, the multiobjective genetic algorithm is considered to tune the hyperparameters of the proposed COVID-19 identification in chest X-ray images. Extensive experiments show that the proposed COVID-19 identification model obtains remarkably better results and may be utilized for real-time testing of patients.

ACS Style

Prashant Kumar Shukla; Jasminder Kaur Sandhu; Anamika Ahirwar; Deepika Ghai; Priti Maheshwary; Piyush Kumar Shukla. Multiobjective Genetic Algorithm and Convolutional Neural Network Based COVID-19 Identification in Chest X-Ray Images. Mathematical Problems in Engineering 2021, 2021, 1 -9.

AMA Style

Prashant Kumar Shukla, Jasminder Kaur Sandhu, Anamika Ahirwar, Deepika Ghai, Priti Maheshwary, Piyush Kumar Shukla. Multiobjective Genetic Algorithm and Convolutional Neural Network Based COVID-19 Identification in Chest X-Ray Images. Mathematical Problems in Engineering. 2021; 2021 ():1-9.

Chicago/Turabian Style

Prashant Kumar Shukla; Jasminder Kaur Sandhu; Anamika Ahirwar; Deepika Ghai; Priti Maheshwary; Piyush Kumar Shukla. 2021. "Multiobjective Genetic Algorithm and Convolutional Neural Network Based COVID-19 Identification in Chest X-Ray Images." Mathematical Problems in Engineering 2021, no. : 1-9.

Original research
Published: 02 January 2021 in Journal of Ambient Intelligence and Humanized Computing
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In world, patients suffering from chronic and lifestyle diseases are substantially increasing that effects social as well as economic life. In this work, initially a broad survey of ubiquitous, smart and networked healthcare systems for monitoring of patients with chronic and lifestyle diseases is presented. Afterwards, Smart Patient Monitoring and Recommendation, a novel framework based on Deep Learning (DL) and Cloud oriented analytics is proposed. Based on the patients’ vital signs and activity context, generated through Ambient Assisted Living devices, SPMR monitors and predicts the real health status and calls assistive services. The real time processing and intelligence facilitated by both Local Intelligent Processing (LIP) module and cloud oriented analytics devised in SPMR. LIP is based on predictive DL with novel Categorical Cross Entropy (CCE) Optimization. In the experimental study, imbalanced dataset collected through a case study on patients suffering from Chronic Blood Pressure disorder is utilized and real health status of patient is predicted. SPMR offers prevention and care in real time even in the absence of internet and cloud service. It eliminates the drawbacks of existing works, in which Machine Learning models and associated methods are copied to local portion. Our proposed model demonstrates the efficacy when compared with similar and recent models. The highest accuracy improvement with our model ranges from 8–18%. Also, F-score average and F-score for emergency class improved up to 17% and 36% respectively. The results show the effectiveness of SPMR even in case of emergencies.

ACS Style

Anand Motwani; Piyush Kumar Shukla; Mahesh Pawar. Novel framework based on deep learning and cloud analytics for smart patient monitoring and recommendation (SPMR). Journal of Ambient Intelligence and Humanized Computing 2021, 1 -16.

AMA Style

Anand Motwani, Piyush Kumar Shukla, Mahesh Pawar. Novel framework based on deep learning and cloud analytics for smart patient monitoring and recommendation (SPMR). Journal of Ambient Intelligence and Humanized Computing. 2021; ():1-16.

Chicago/Turabian Style

Anand Motwani; Piyush Kumar Shukla; Mahesh Pawar. 2021. "Novel framework based on deep learning and cloud analytics for smart patient monitoring and recommendation (SPMR)." Journal of Ambient Intelligence and Humanized Computing , no. : 1-16.

Article
Published: 22 November 2020 in Multimedia Tools and Applications
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Nowadays, most of the communications in IoT enabled devices are done in the form of images. To protect the images from intruders, there is a need for a secure encryption algorithm. Many encryption algorithms have been proposed, some of the algorithms are based on symmetric-key cryptography and others are based on asymmetric key cryptography. This work proposed a fast, secure, and lightweight symmetric image cryptographic algorithm based on the session key. In this work, for every image encryption, a new session key is generated. Here session keys are generated with the help of crossover and mutation operators of genetic algorithm. This proposed algorithm uses a 64-bit plain text and requires an 80-bit key, where 64-bits of a key is generated via symmetric hexadecimal key and the remaining 16-bits of a key are randomly added, to encrypt the image. Here crossover and mutation operators are used to generate random 64-bits of a key. The proposed algorithm will work for both color and grayscale images. The proposed algorithm is simulated on MATLAB 2017 platform and compared with similar types of the existing algorithm on various parameters.

ACS Style

Manish Gupta; Kamlesh Kumar Gupta; Piyush Kumar Shukla. Session key based fast, secure and lightweight image encryption algorithm. Multimedia Tools and Applications 2020, 80, 10391 -10416.

AMA Style

Manish Gupta, Kamlesh Kumar Gupta, Piyush Kumar Shukla. Session key based fast, secure and lightweight image encryption algorithm. Multimedia Tools and Applications. 2020; 80 (7):10391-10416.

Chicago/Turabian Style

Manish Gupta; Kamlesh Kumar Gupta; Piyush Kumar Shukla. 2020. "Session key based fast, secure and lightweight image encryption algorithm." Multimedia Tools and Applications 80, no. 7: 10391-10416.

Conference paper
Published: 22 October 2020 in Blockchain Technology and Innovations in Business Processes
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Purpose The aim of this study is to propose a smart predictive healthcare framework for patients suffering from chronic diseases and are under observation at home. To appropriately predict the patient’s actual health status and for better recommendation and assistive services, the framework utilizes a novel Deep Learning (DL) model. The DL model utilizes the big data of patients’ vital signs, context data such as activity, medication, and symptoms collected through Ambient Assisted Living (AAL) systems. Method We applied a DL model with novel cost optimization for categorical prediction. Our model is a component part of the Intelligent Module at patient’s end. The experimental study is carried out on patients suffering from Chronic Blood Pressure (BP) disorders. The imbalanced dataset collected over a period of 1 year and sampled every 15 min. Result The highest overall accuracy achieved for the proposed model is 99.97% which is up to 8.8% better than one of the existing models. F-score for emergency cases has been enhanced by 12%, 39%, and 12% for Hypertensive, Hypotensive, and Normotensive patients’, respectively. Conclusion The experimental outcomes reveal that the proposed model can predict patients’ conditions (emergency, warning, alert, and normal) with more accuracy. Also, our model is able to handle imbalanced big data, high variability of vital signs, and all kinds of BP patients. Thus, we consider that the proposed framework is valuable for the management of chronic diseases.

ACS Style

Anand Motwani; Piyush Kumar Shukla; Mahesh Pawar. Smart Predictive Healthcare Framework for Remote Patient Monitoring and Recommendation Using Deep Learning with Novel Cost Optimization. Blockchain Technology and Innovations in Business Processes 2020, 671 -682.

AMA Style

Anand Motwani, Piyush Kumar Shukla, Mahesh Pawar. Smart Predictive Healthcare Framework for Remote Patient Monitoring and Recommendation Using Deep Learning with Novel Cost Optimization. Blockchain Technology and Innovations in Business Processes. 2020; ():671-682.

Chicago/Turabian Style

Anand Motwani; Piyush Kumar Shukla; Mahesh Pawar. 2020. "Smart Predictive Healthcare Framework for Remote Patient Monitoring and Recommendation Using Deep Learning with Novel Cost Optimization." Blockchain Technology and Innovations in Business Processes , no. : 671-682.

Journal article
Published: 22 July 2020 in IRBM
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With an advancement in biomedical applications, many images are communicated over the public networks. Therefore, these medical images are prone to various security threats. Development of end to end secure communication protocol for these medical images is found to be a challenging task. Therefore, many researchers have proposed various image medica image encryption techniques to provide end to end security of medica images. However, the existing approaches of block-based recovery of the secret through progressive sharing paradigm have support for limited threshold value of the chosen blocks out of the total number of the blocks during the communication of the image. Most of the suggested scheme has fixed threshold value for the blocks during recovery of secret; works good for a limited threshold (k) value out of number of blocks (n) in which secret has been divided for security. A novel threshold based (any value of k and n) blockwide recovery of secret in progressive secret sharing has been introduced and analysed for distributed environment. The proposed threshold block wise splitting using progressive visual secret sharing (T-BPVSS) achieves any general higher value of threshold for recovery of secret medical images. Proposed scheme is tested based on various parameters such as varying values of threshold for recovery of secret during enhanced security scenario, as well as changing dimensions of the images and introducing noise in the images. A detailed distributed computing recovery solution is also suggested for the original secret by using distribution technique of shares across the networks of computers. The scheme satisfies for perfect security condition in distributed environment using at least minimum decided threshold numbers of participants(k) before revealing any of the blocks of secret medical image.

ACS Style

Dhiraj Pandey; Umashankar Rawat; Neeraj Kumar Rathore; Kavita Pandey; Piyush Kumar Shukla. Distributed Biomedical Scheme for Controlled Recovery of Medical Encrypted Images. IRBM 2020, 1 .

AMA Style

Dhiraj Pandey, Umashankar Rawat, Neeraj Kumar Rathore, Kavita Pandey, Piyush Kumar Shukla. Distributed Biomedical Scheme for Controlled Recovery of Medical Encrypted Images. IRBM. 2020; ():1.

Chicago/Turabian Style

Dhiraj Pandey; Umashankar Rawat; Neeraj Kumar Rathore; Kavita Pandey; Piyush Kumar Shukla. 2020. "Distributed Biomedical Scheme for Controlled Recovery of Medical Encrypted Images." IRBM , no. : 1.

Research article
Published: 20 May 2020 in IRBM
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The COVID-19 infection is increasing at a rapid rate, with the availability of limited number of testing kits. Therefore, the development of COVID-19 testing kits is still an open area of research. Recently, many studies have shown that chest Computed Tomography (CT) images can be used for COVID-19 testing, as chest CT images show a bilateral change in COVID-19 infected patients. However, the classification of COVID-19 patients from chest CT images is not an easy task as predicting the bilateral change is defined as an ill-posed problem. Therefore, in this paper, a deep transfer learning technique is used to classify COVID-19 infected patients. Additionally, a top-2 smooth loss function with cost-sensitive attributes is also utilized to handle noisy and imbalanced COVID-19 dataset kind of problems. Experimental results reveal that the proposed deep transfer learning-based COVID-19 classification model provides efficient results as compared to the other supervised learning models.

ACS Style

Y. Pathak; P.K. Shukla; A. Tiwari; S. Stalin; S. Singh. Deep Transfer Learning Based Classification Model for COVID-19 Disease. IRBM 2020, 1 .

AMA Style

Y. Pathak, P.K. Shukla, A. Tiwari, S. Stalin, S. Singh. Deep Transfer Learning Based Classification Model for COVID-19 Disease. IRBM. 2020; ():1.

Chicago/Turabian Style

Y. Pathak; P.K. Shukla; A. Tiwari; S. Stalin; S. Singh. 2020. "Deep Transfer Learning Based Classification Model for COVID-19 Disease." IRBM , no. : 1.

Review
Published: 30 March 2020 in International Journal of Modern Physics B
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Data processing with multiple domains is an important concept in any platform; it deals with multimedia and textual information. Where textual data processing focuses on a structured or unstructured way of data processing which computes in less time with no compression over the data, multimedia data are processing deals with a processing requirement algorithm where compression is needed. This involve processing of video and their frames and compression in short forms such that the fast processing of storage as well as the access can be performed. There are different ways of performing compression, such as fractal compression, wavelet transform, compressive sensing, contractive transformation and other ways. One way of performing such a compression is working with the high frequency component of multimedia data. One of the most recent topics is fractal transformation which follows the block symmetry and archives high compression ratio. Yet, there are limitations such as working with speed and its cost while performing proper encoding and decoding using fractal compression. Swarm optimization and other related algorithms make it usable along with fractal compression function. In this paper, we review multiple algorithms in the field of fractal-based video compression and swarm intelligence for problems of optimization.

ACS Style

Shraddha Pandit; Piyush Kumar Shukla; Akhilesh Tiwari; Prashant Kumar Shukla; Manish Maheshwari; Rachana Dubey. Review of video compression techniques based on fractal transform function and swarm intelligence. International Journal of Modern Physics B 2020, 34, 1 .

AMA Style

Shraddha Pandit, Piyush Kumar Shukla, Akhilesh Tiwari, Prashant Kumar Shukla, Manish Maheshwari, Rachana Dubey. Review of video compression techniques based on fractal transform function and swarm intelligence. International Journal of Modern Physics B. 2020; 34 (8):1.

Chicago/Turabian Style

Shraddha Pandit; Piyush Kumar Shukla; Akhilesh Tiwari; Prashant Kumar Shukla; Manish Maheshwari; Rachana Dubey. 2020. "Review of video compression techniques based on fractal transform function and swarm intelligence." International Journal of Modern Physics B 34, no. 8: 1.

Journal article
Published: 27 March 2020 in Modern Physics Letters B
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Flying Ad-hoc Networks (FANETs) and Unmanned Aerial Vehicles (UAVs) are widely utilized in various rescues, disaster management and military operations nowadays. The limited battery power and high mobility of UAVs create problems like small flight duration and unproductive routing. In this paper, these problems will be reduced by using efficient hybrid K-Means-Fruit Fly Optimization Clustering Algorithm (KFFOCA). The performance and efficiency of K-Means clustering is improved by utilizing the Fruit Fly Optimization Algorithm (FFOA) and the results are analyzed against other optimization techniques like CLPSO, CACONET, GWOCNET and ECRNET on the basis of several performance parameters. The simulation results show that the KFFOCA has obtained better performance than CLPSO, CACONET, GWOCNET and ECRNET based on Packet Delivery Ratio (PDR), throughput, cluster building time, cluster head lifetime, number of clusters, end-to-end delay and consumed energy.

ACS Style

Ankur Pandey; Piyush Kumar Shukla; Ratish Agrawal. An adaptive Flying Ad-hoc Network (FANET) for disaster response operations to improve quality of service (QoS). Modern Physics Letters B 2020, 34, 1 .

AMA Style

Ankur Pandey, Piyush Kumar Shukla, Ratish Agrawal. An adaptive Flying Ad-hoc Network (FANET) for disaster response operations to improve quality of service (QoS). Modern Physics Letters B. 2020; 34 (10):1.

Chicago/Turabian Style

Ankur Pandey; Piyush Kumar Shukla; Ratish Agrawal. 2020. "An adaptive Flying Ad-hoc Network (FANET) for disaster response operations to improve quality of service (QoS)." Modern Physics Letters B 34, no. 10: 1.

Article
Published: 27 January 2020 in Multimedia Tools and Applications
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Nowadays, the communication progression is turn out to be easy and effectual than previous days because of the development of science and technology, which is supplying numerous advantages to the consumer. Consequently, the consumer can access and preserve their data in the effectual manner. However, some of the difficulties are presented to transmit the data from one place to another. To overcome the problem in this paper an effective secure data transmission is proposed using optimal discrete wavelet transform (ODWT) and sanitization Algorithm. The proposed work consist of two module namely, embedding and extraction. For embedding process, the secrete image bit is inserted into the original image. Similarly, in extraction process, the inserted bit is extracted from the watermarked image without any information loss. Here, sanitization approach is applied to secrete image to attain the secrete bit. The performance of the proposed scheme is analyzed through various constraints namely peak signal to noise ratio (PSNR) and normalized correlation (NC).

ACS Style

Anoop Kumar Chaturvedi; Piyush Kumar Shukla. Effective watermarking technique using optimal discrete wavelet transform and sanitization technique. Multimedia Tools and Applications 2020, 79, 13161 -13177.

AMA Style

Anoop Kumar Chaturvedi, Piyush Kumar Shukla. Effective watermarking technique using optimal discrete wavelet transform and sanitization technique. Multimedia Tools and Applications. 2020; 79 (19-20):13161-13177.

Chicago/Turabian Style

Anoop Kumar Chaturvedi; Piyush Kumar Shukla. 2020. "Effective watermarking technique using optimal discrete wavelet transform and sanitization technique." Multimedia Tools and Applications 79, no. 19-20: 13161-13177.

Journal article
Published: 12 September 2019 in Computer Communications
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Wireless sensor network (WSN) is a group of a huge number of low price, low control, and self-organizing specialized sensor nodes. WSN is very much vulnerable to different types of physical attacks due to limited resource capacity and screened to external atmosphere for circulating data. The node capture attack is one of the major attacks in WSN in which the intruder physically captures the node and remove the secret information from the node’s memory. We propose a Fruit Fly Optimization Algorithm (FFOA) that is based on multiple objectives node capture attack algorithm which consists of several objectives: maximum node contribution, maximum key contribution, and least resource expenses to discover optimal nodes. It will influence an inclusive tool to demolish maximum part of the network along with effective cost and maximum attacking efficiency. The simulation result illustrates that FFOA obtains a maximum fraction of compromised traffic, lower attacking rounds, and lower energy cost as compared with Genetic Algorithm (GA) and other node capture attack algorithms. Therefore, FFOA gives maximum attacking efficiency than GA and other algorithms by capturing minimum nodes that compromise the whole network.

ACS Style

Ruby Bhatt; Priti Maheshwary; Piyush Shukla; Prashant Shukla; Manish Shrivastava; Soni Changlani. Implementation of Fruit Fly Optimization Algorithm (FFOA) to escalate the attacking efficiency of node capture attack in Wireless Sensor Networks (WSN). Computer Communications 2019, 149, 134 -145.

AMA Style

Ruby Bhatt, Priti Maheshwary, Piyush Shukla, Prashant Shukla, Manish Shrivastava, Soni Changlani. Implementation of Fruit Fly Optimization Algorithm (FFOA) to escalate the attacking efficiency of node capture attack in Wireless Sensor Networks (WSN). Computer Communications. 2019; 149 ():134-145.

Chicago/Turabian Style

Ruby Bhatt; Priti Maheshwary; Piyush Shukla; Prashant Shukla; Manish Shrivastava; Soni Changlani. 2019. "Implementation of Fruit Fly Optimization Algorithm (FFOA) to escalate the attacking efficiency of node capture attack in Wireless Sensor Networks (WSN)." Computer Communications 149, no. : 134-145.

Image and signal processing
Published: 04 July 2019 in Journal of Medical Systems
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This paper proposes an innovative image cryptosystem algorithm using the properties of the block encryption, 4D logistic map and DNA systems. Multiple key sequences are generated and pixel substitution is performed by using nonlinear 4D logistic map, then encryption is performed by using DNA rules to ensure that the different blocks are encrypted securely. The results of the experiment indicate that the proposed Non Linear 4D Logistic Map and DNA (NL4DLM_DNA) sequence based algorithm gives better performance, which is analyzed on the basis of security, quality, attack resilience, diffusion and running time as compared to some previous works.

ACS Style

Shalini Stalin; Priti Maheshwary; Piyush Kumar Shukla; Manish Maheshwari; Bhupesh Gour; Ankur Khare. Fast and Secure Medical Image Encryption Based on Non Linear 4D Logistic Map and DNA Sequences (NL4DLM_DNA). Journal of Medical Systems 2019, 43, 267 .

AMA Style

Shalini Stalin, Priti Maheshwary, Piyush Kumar Shukla, Manish Maheshwari, Bhupesh Gour, Ankur Khare. Fast and Secure Medical Image Encryption Based on Non Linear 4D Logistic Map and DNA Sequences (NL4DLM_DNA). Journal of Medical Systems. 2019; 43 (8):267.

Chicago/Turabian Style

Shalini Stalin; Priti Maheshwary; Piyush Kumar Shukla; Manish Maheshwari; Bhupesh Gour; Ankur Khare. 2019. "Fast and Secure Medical Image Encryption Based on Non Linear 4D Logistic Map and DNA Sequences (NL4DLM_DNA)." Journal of Medical Systems 43, no. 8: 267.

Article
Published: 27 June 2019 in Multimedia Tools and Applications
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In Wireless sensor networks, energy efficiency is the significant attribute to be improved. Clustering is the major technique to enhance energy efficiency. Using this technique, sensor nodes in the network region are grouped as several clusters and cluster head (CH) is chosen for each and every cluster. This CH gathers data packet from the non-CH members inside the cluster and forwards the collected data packet to the base station. However, the CH may drain its energy after a number of transmissions. So, we present the Energy efficient Gravitational search algorithm (GSA) and Fuzzy based clustering with Hop count based routing for WSN in this paper. Initially, CH is selected using Gravitational Search Algorithm (GSA), based on its weight sensor nodes are joined to the CH and thus cluster is formed. Among the selected CHs in the network, supercluster head (SCH) is selected using a fuzzy inference system (FIS). This selected SCH gathers the data packet from all CHs and forwards it to the sink or base station. For transmission, the efficient route is established based on the hop count of the sensor nodes. Simulation results show that the performance of our proposed approach is superior to the existing work in terms of delivery ratio and energy efficiency.

ACS Style

Mohit Singh Tomar; Piyush Kumar Shukla. Energy Efficient Gravitational Search Algorithm and Fuzzy Based Clustering With Hop Count Based Routing For Wireless Sensor Network. Multimedia Tools and Applications 2019, 78, 27849 -27870.

AMA Style

Mohit Singh Tomar, Piyush Kumar Shukla. Energy Efficient Gravitational Search Algorithm and Fuzzy Based Clustering With Hop Count Based Routing For Wireless Sensor Network. Multimedia Tools and Applications. 2019; 78 (19):27849-27870.

Chicago/Turabian Style

Mohit Singh Tomar; Piyush Kumar Shukla. 2019. "Energy Efficient Gravitational Search Algorithm and Fuzzy Based Clustering With Hop Count Based Routing For Wireless Sensor Network." Multimedia Tools and Applications 78, no. 19: 27849-27870.

Conference paper
Published: 26 June 2019 in Advances in Intelligent Systems and Computing
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Agriculture is one of the major revenue producing sectors of India and a source of survival. The number of biological, financial, and environmental factors affects the crop yield. So, unidentifiable changes in these factors lead to failure in agriculture area. This paper focuses on prediction of crop yield where different geospatial features were utilized, such as normalized difference vegetation index, standard precipitation index, vegetation condition index. In order to learn from previous weather condition, a standard error back propagation neural network was used. Here, the training was done in such a way that all set of features were utilized in pair with their yield value as output. For increasing the reliability of the work, whole experiment was done on real geo-spatial dataset from Madhya Pradesh region of India. Result shows that proposed model has overcome various evaluation parameters on different scale as compared to previous approaches adopted by researchers.

ACS Style

Preeti Tiwari; Piyush Shukla. Artificial Neural Network-Based Crop Yield Prediction Using NDVI, SPI, VCI Feature Vectors. Advances in Intelligent Systems and Computing 2019, 585 -594.

AMA Style

Preeti Tiwari, Piyush Shukla. Artificial Neural Network-Based Crop Yield Prediction Using NDVI, SPI, VCI Feature Vectors. Advances in Intelligent Systems and Computing. 2019; ():585-594.

Chicago/Turabian Style

Preeti Tiwari; Piyush Shukla. 2019. "Artificial Neural Network-Based Crop Yield Prediction Using NDVI, SPI, VCI Feature Vectors." Advances in Intelligent Systems and Computing , no. : 585-594.

Conference paper
Published: 03 May 2019 in Inventive Computation and Information Technologies
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With the advent of multimedia technology, video compression has become imperative. The high definition of video required huge amount of storage space and large amount of bandwidth for the transmission of video. The largest part of multimedia is video. There is upsurge in demand of compressed data due to excessive usage of multimedia applications on Internet. Hence in success of multimedia data, video compression and decompression are majorly used. There are already various transform functions such as wavelet transform, DCT transform and fractal transform functions which are used for compression and decompression of video. In all transform function, the fractal transforms function adhere to the rule of block symmetry. It is very proficient process, but the rate of compression is very time-consuming, however, the decompression is very fast. In this paper, we adopted fractal triangular partitioning scheme to compress and decompress the videos. Here in this work for our analysis, we used very short length videos which are of different size. The primary objective of this work is to minimize the encoding time of video and achieve better compression ratio. The process of video compression and decompression methods is simulated in MATLAB software and used some standard parameters for the evaluation of compression and decompression results.

ACS Style

Shraddha Pandit; Piyush Kumar Shukla; Akhilesh Tiwari. Investigating the Effect of Compression and Decompression in Video Using Fractal Technique. Inventive Computation and Information Technologies 2019, 73 -81.

AMA Style

Shraddha Pandit, Piyush Kumar Shukla, Akhilesh Tiwari. Investigating the Effect of Compression and Decompression in Video Using Fractal Technique. Inventive Computation and Information Technologies. 2019; ():73-81.

Chicago/Turabian Style

Shraddha Pandit; Piyush Kumar Shukla; Akhilesh Tiwari. 2019. "Investigating the Effect of Compression and Decompression in Video Using Fractal Technique." Inventive Computation and Information Technologies , no. : 73-81.

Conference paper
Published: 03 May 2019 in Inventive Computation and Information Technologies
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Wireless sensor network (WSN) (Kaur and Kang in Int J Comput Sci Eng IJCSE 6(4):157–162, 2016, [1]) is susceptible to different types of physical attacks. It is collection of tiny-sized sensor nodes. The reason behind these attacks is its limited resource capacity. It is screened to external atmosphere for circulating data. Node capture attack is supposed to be severe attacks in WSN (Butani et al. in Int J Comput Appl IJCA 95(3):32–39, 2014, [2]). In this type, the node is substantially captured by an assailant and eradicates the secret information from the node’s storage. This paper proposes a fruit fly optimization algorithm (FFOA) (Wang et al. in Mob Inf Syst 1–14, 2017, [3]). It is based on multiple objectives (Lin and Wu in J Supercomput 1–19, 2013, [4]) node capture attack algorithm. Proposed algorithm serves these objectives: maximum node contribution (Lin and Wu in J Supercomput 1–19, 2013, [4]), maximum key contribution (Lin and Wu in J Supercomput 1–19, 2013, [4]), and least resource expenses (Lin and Wu in J Supercomput 1–19, 2013, [4]). The simulation result illustrates that FFOA obtains a lower energy cost as compared with matrix algorithm (MA) (Lin et al. in J Supercomput 71:3181–3212, 2015, [5]) and other node capture attack algorithms when it is calculated for single-path routing algorithm. There are two variations in which this could have served. First is single path and the other is multi-path. But, in this paper, it is only the first case that has been discussed and calculated.

ACS Style

Ruby Bhatt; Priti Maheshwary; Piyush Shukla. Application of Fruit Fly Optimization Algorithm for Single-Path Routing in Wireless Sensor Network for Node Capture Attack. Inventive Computation and Information Technologies 2019, 129 -136.

AMA Style

Ruby Bhatt, Priti Maheshwary, Piyush Shukla. Application of Fruit Fly Optimization Algorithm for Single-Path Routing in Wireless Sensor Network for Node Capture Attack. Inventive Computation and Information Technologies. 2019; ():129-136.

Chicago/Turabian Style

Ruby Bhatt; Priti Maheshwary; Piyush Shukla. 2019. "Application of Fruit Fly Optimization Algorithm for Single-Path Routing in Wireless Sensor Network for Node Capture Attack." Inventive Computation and Information Technologies , no. : 129-136.

Conference paper
Published: 01 May 2019 in 2019 International Conference on Intelligent Computing and Control Systems (ICCS)
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Recently, it is one of the concerns to protect multimedia applications with storage of images being an important security issue. The process used to encrypt data and protect these multimedia applications is encryption. Present cryptography provides crucial techniques in order to secure information and these encryption techniques are utilized for protection of private data from the unauthorized user. In this paper, literature reviews based mostly upon numerous secret writing techniques are explained and also the poll taker are going to be ready to get a plan for competent technique to be used.

ACS Style

Shalini Stalin; Priti Maheshwary; Piyush Kumar Shukla. Payback of Image Encryption Techniques: A Quantitative Investigation. 2019 International Conference on Intelligent Computing and Control Systems (ICCS) 2019, 1370 -1380.

AMA Style

Shalini Stalin, Priti Maheshwary, Piyush Kumar Shukla. Payback of Image Encryption Techniques: A Quantitative Investigation. 2019 International Conference on Intelligent Computing and Control Systems (ICCS). 2019; ():1370-1380.

Chicago/Turabian Style

Shalini Stalin; Priti Maheshwary; Piyush Kumar Shukla. 2019. "Payback of Image Encryption Techniques: A Quantitative Investigation." 2019 International Conference on Intelligent Computing and Control Systems (ICCS) , no. : 1370-1380.

Conference paper
Published: 01 December 2018 in 2018 International Conference on Advanced Computation and Telecommunication (ICACAT)
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Sensors are most important concept now-a-days and Wireless sensor network (WSN) [1] is a crucial technology. But, these are susceptible to different types of physical attacks. It is assortment of miniature sized sensor nodes. The reason behind vulnerable attacks is its limited resource capacity. It is screened to external atmosphere for circulating data. Node capture attack is supposed to be severe attacks in WSN [2]. In this type, the node is substantially captured by an assailant and eradicates the secret information from the node’s storage. This paper proposes a Fruit Fly Optimization Algorithm (FFOA) [13]. It is based on multiple objectives [4] node capture attack algorithm. Proposed algorithm serves these objectives: maximum node contribution [4], maximum key contribution [4], and least resource expenses [4]. The simulation result illustrates that FFOA obtains a maximum fraction of compromised traffic, lower attacking rounds, and lower energy cost as compared with matrix algorithm (MA) [5] and other node capture attack algorithms.

ACS Style

Ruby Bhatt; Priti Maheshwary; Piyush Kumar Shukla. Application of Fruit Fly optimization Algorithm for Node Capture Attack in Wireless Sensor Network. 2018 International Conference on Advanced Computation and Telecommunication (ICACAT) 2018, 1 -4.

AMA Style

Ruby Bhatt, Priti Maheshwary, Piyush Kumar Shukla. Application of Fruit Fly optimization Algorithm for Node Capture Attack in Wireless Sensor Network. 2018 International Conference on Advanced Computation and Telecommunication (ICACAT). 2018; ():1-4.

Chicago/Turabian Style

Ruby Bhatt; Priti Maheshwary; Piyush Kumar Shukla. 2018. "Application of Fruit Fly optimization Algorithm for Node Capture Attack in Wireless Sensor Network." 2018 International Conference on Advanced Computation and Telecommunication (ICACAT) , no. : 1-4.

Conference paper
Published: 01 December 2018 in 2018 International Conference on Advanced Computation and Telecommunication (ICACAT)
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Since the foggy images undergo from low contrast and resolution due to diffusion of light and poor visibility conditions. So, Fog elimination is extremely preferred in both estimation picture making and computer vision applications. Proposed technique uses a Dark Channel Prior with contrast stretching to remove fog and improve the contrast of fog free image, respectively. Using Dark Channel Prior method one can directly take away the thickness of the haze and recover a high quality haze free image. Contrast Stretching is applied in the resulted image of dark channel prior method to improve the contrast of image. The noise that affect foggy image can also be ease by using the median low pass filter. By using this technique the visual quality and color of the foggy image can be correct effectively. Experiments are conducted on PSNR and RMSE parameters. Experimental Result shows that proposed method contains least average RMSE values and Higher PSNR values among other methods.

ACS Style

Vijay Kumar Trivedi; Piyush Kumar Shukla; Hitesh Gupta. Dark Channel Prior and Global Contrast Stretching based Hybrid Defogging Image Technique. 2018 International Conference on Advanced Computation and Telecommunication (ICACAT) 2018, 1 -6.

AMA Style

Vijay Kumar Trivedi, Piyush Kumar Shukla, Hitesh Gupta. Dark Channel Prior and Global Contrast Stretching based Hybrid Defogging Image Technique. 2018 International Conference on Advanced Computation and Telecommunication (ICACAT). 2018; ():1-6.

Chicago/Turabian Style

Vijay Kumar Trivedi; Piyush Kumar Shukla; Hitesh Gupta. 2018. "Dark Channel Prior and Global Contrast Stretching based Hybrid Defogging Image Technique." 2018 International Conference on Advanced Computation and Telecommunication (ICACAT) , no. : 1-6.