Dr Anurag Jain
@rgibhopal.com
Professor CSE
EDUCATION
PHD in Information Technology
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Engineering, Computer Science Applications, Information Systems, Artificial Intelligence
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
, Nasib Nasib, , , , , , Santosh Reddy Addula, Anurag Jain, Preeti Gulia,et al.
ASPG Publishing LLC
IoT devices produce a gigantic amount of data and it has grown exponentially in previous years. To get insights from this multi-property data, machine learning has proved its worth across the industry. The present paper provides an overview of the variety of data collected through IoT devices. The conflux of machine learning with IoT is also explained using the bibliometric analysis technique. This paper presents a systematic literature review using bibliometric analysis of the data collected from Scopus and WoS. Academic literature for the last six years is used to explore research insights, patterns, and trends in the field of IoT using machine learning. This study analyses and assesses research for the last six years using machine learning in seven IoT domains like Healthcare, Smart City, Energy systems, Industrial IoT, Security, Climate, and Agriculture. The author’s and country-wise citation analysis is also presented in this study. VOSviewer version 1.6.18 is used to provide a graphical representation of author citation analysis. This study may be quite helpful for researchers and practitioners to develop a blueprint of machine learning techniques in various IoT domains.
Sanjiv Kumar Jain, Shweta Agrawal, Prashant Kumar Shukla, Piyush Kumar Shukla, and Anurag Jain
Inderscience Publishers
Anurag Jain, Ahmed Nadeem, Huda Majdi Altoukhi, Sajjad Shaukat Jamal, Henry kwame Atiglah, and Haitham Elwahsh
Wiley
A technology known as data analytics is a massively parallel processing approach that may be used to forecast a wide range of illnesses. Many scientific research methodologies have the problem of requiring a significant amount of time and processing effort, which has a negative impact on the overall performance of the system. Virtual screening (VS) is a drug discovery approach that makes use of big data techniques and is based on the concept of virtual screening. This approach is utilised for the development of novel drugs, and it is a time‐consuming procedure that includes the docking of ligands in several databases in order to build the protein receptor. The proposed work is divided into two modules: image processing‐based cancer segmentation and analysis using extracted features using big data analytics, and cancer segmentation and analysis using extracted features using image processing. This statistical approach is critical in the development of new drugs for the treatment of liver cancer. Machine learning methods were utilised in the prediction of liver cancer, including the MapReduce and Mahout algorithms, which were used to prefilter the set of ligand filaments before they were used in the prediction of liver cancer. This work proposes the SMRF algorithm, an improved scalable random forest algorithm built on the MapReduce foundation. Using a computer cluster or cloud computing environment, this new method categorises massive datasets. With SMRF, small amounts of data are processed and optimised over a large number of computers, allowing for the highest possible throughput. When compared to the standard random forest method, the testing findings reveal that the SMRF algorithm exhibits the same level of accuracy deterioration but exhibits superior overall performance. The accuracy range of 80 percent using the performance metrics analysis is included in the actual formulation of the medicine that is utilised for liver cancer prediction in this study.
Shalini Stalin, Vandana Roy, Prashant Kumar Shukla, Atef Zaguia, Mohammad Monirujjaman Khan, Piyush Kumar Shukla, and Anurag Jain
Hindawi Limited
The electroencephalogram (EEG) signals are a big data which are frequently corrupted by motion artifacts. As human neural diseases, diagnosis and analysis need a robust neurological signal. Consequently, the EEG artifacts’ eradication is a vital step. In this research paper, the primary motion artifact is detected from a single-channel EEG signal using support vector machine (SVM) and preceded with further artifacts’ suppression. The signal features’ abstraction and further detection are done through ensemble empirical mode decomposition (EEMD) algorithm. Moreover, canonical correlation analysis (CCA) filtering approach is applied for motion artifact removal. Finally, leftover motion artifacts’ unpredictability is removed by applying wavelet transform (WT) algorithm. Finally, results are optimized by using Harris hawks optimization (HHO) algorithm. The results of the assessment confirm that the algorithm recommended is superior to the algorithms currently in use.
Manisha Gupta and Anurag Jain
IEEE
Cloud computing is a reliable computing platform for large computational intensive or data intensive tasks. This has been accepted by many industrial giants of software industry for their software solutions, companies like Microsoft, Accenture, Ericson etc has adopted cloud computing as their first choice for cheap and reliable computing. But which increase in number of clients adopting this there is requirement of much more cost efficient and high performance computing for more trust and reliability among the client and the service provide to guarantee cheap and more efficient solutions. So the tasks in cloud need to be allocated in an efficient manner to provide high resource utilization and least execution time for high performance, at the same time provide least computational cost as cloud follows pay-per use model. Many resource algorithms are been proposed to improve the performance, but are not cost efficient at same time. Algorithms like genetic, particle swarm and ant colony algorithm are efficient solutions but not cost efficient. So this paper presets an study of various existing algorithms.
Manoj Patil, Vinay Sahu, and Anurag Jain
IEEE
Today in the world of globalization mobile communication is one of the fastest growing medium though which one sender can interact with other in short time. During the transmission of data from sender to receiver, size of data is important, since more data takes more time. But one of the limitations of sending data through mobile devices is limited use of bandwidth and number of packets transmitted. Also the security of these data is important. Hence various protocols are implemented which not only provides security to the data but also utilizes bandwidth. Here we proposed an efficient technique of sending SMS text using combination of compression and encryption. The data to be send is first encrypted using Elliptic curve Cryptographic technique, but encryption increases the size of the text data, hence compression is applied to this encrypted data so the data gets compressed and is send in short time. The Compression technique implemented here is an efficient one since it includes an algorithm which compresses the text by 99.9%, hence a great amount of bandwidth gets saved.The hybrid technique of Compression-Encryption of SMS text message is implemented for Android Operating Systems.
Neha Gupta, Anurag Jain, and Harsh Kumar Singh
IEEE
This paper provides on introduction on key strategy for wireless sensor network. Many key Strategy are focus on proposed pair wise based on key strategy technique. Key management Mechanism Provide a security over a Wireless sensor network. Many Scheme are focus on proposing new pair wise based key management strategy. The key management mechanism combines trusted-server and key pre-distribution scheme which meets the security requirement of WSN. Some features of key management strategy we need a lot of energy to communicate a node with each other and increase a network size over a network through a insert a new node. Many Scheme provide a various protocol over a network. The key management Scheme contain a static and dynamic key management Strategy. We use a particular Protocol to select and analysis a accurate performance of sensor network. We use a AODV (ADHOC on demand distance vector routing protocol) Protocol use a network model. AODV Protocol basically use a network layer.
Gaurav Shelke, Anurag Jain, and Shubha Dubey
IEEE
Knowledge extraction is a process of filtering some informative knowledge from the database so that it can be used wide variety of applications and analysis. Due to this highly efficient algorithm is required for data mining and for accessing data from large datasets. Although there are various techniques implemented for the detection of anomalies using frequent item sets using apriori algorithm but the technique applied are not suitable for large database and contains more error rate and also the classification ratio is less. Hence in this paper an efficient technique is implemented using the combinatorial method of Classification and association rule mining. First the fuzzy apriori algorithm is applied to generate frequent item sets and then CART algorithm is applied for the classification of the network anomalies.
Pooja Jain, Anurag Jain, and Chetan Agrawal
IEEE
Effective data compression is the most important concern in data management methods. The LZW Compression method is one of the most popular algorithms in different compression method. There is one more concern i.e. compressed pattern matching. Compressed pattern matching is an emerging research area that addresses the problem in which finding patterns in the compressed data and report all the occurrences of the patterns without decompressing the compressed data. In this thesis has developed, analyzed and test a new data compression technique “Compression data Based on Dictionary” and "Searching Algorithm in Compressed data". This Algorithm generates the compression codes for words and maintains a dictionary for compression as well as decompression. Searching algorithm uses the quick search string matching algorithm for finding the required patterns.
Ayonija Pathre, Chetan Agrawal, and Anurag Jain
IEEE
Vehicular ad hoc network (VANET) has extensively used to enhance protection of the passengers and reduce occasion of traffic congestion. Consistent communication in vehicular ad-hoc networks is important to provide functional and reliable traffic safety and efficiency applications. Security is the major issue in the network due to the mobile nature of the vehicle. In this paper we proposed the novel traffic congestion detection and removal scheme against DDOS attack Here the attacker behavior is broadcast the huge numbers of false information packets in network i.e. the false information about the traffic. The number of nodes or vehicles that receives the false packet information are affected from attack are called Node. Now if the traffic is jammed or congestion occurring and their information goes to Roadside Unit (RSU) then RSU must be detected and excluded permanently from the network after applying proposed effective approach. Proposed scheme against DDOS attack aims to identify and exclude attackers from the network. In the presence of misbehaving in network the false information is transferred in the network by that the vehicles are deciding to do the routing according to false information. Proposed security scheme recovers control information and improves the performance of VANET in the presence of an attacker.
Rohina Ansari, Himanshu Yadav, and Anurag Jain
IEEE
Digital world is a dire need of the current scenario. Now these days equipments take the digital snapshots which may be blur due to bad focus of camera, relative motion between camera and scene to be capture etc. This is an extensive problem to restore this sort of images. This paper proposed a hybrid methodology to recover these images in order to improve the quality. This work uses the basic concept of kernel and padding. This work also applies the Haar wavelet Transform for filtering the image in order to reconstruct image which have the noise and blur. The results of this paper show that the proposed method gives the better result from the previous methods. It seems to be that the PSNR, Mean Square Rate and execution time is better in the proposed scheme.
Vaishali Ahirwar, Himanshu Yadav, and Anurag Jain
IEEE
Digital image processing is versatile research in this era. Many researchers implement different types of organizations like image restoration, image enhancement, color image processing, image segmentation etc. Image enhancement technique is among the simplest and most appealing area of digital image processing. Enhancement techniques like brightness preservation, contrast enhancement highlight certain features means depend which part of the image want to be enhance some application some input image including noise, reduction or removal of noise is also form of image enhancement. Brightness preservation has enhanced visual quality of digital image so that the limitation contained in these images is used for various applications in a better way. A very popular technique for image enhancement is histogram equalization (HE) and curvelet transformation. HE technique is commonly employed for image enhancement because of its simplicity and comparatively better performance on almost all types of images. Another widely used technique is curvelet transformation. This technique is identified and separate bright regions of image but more error rate and low peak signal to noise ratio(PSNR), result of this technique is brightness preservation level is low and output image is gray. This paper design a hybrid model through discrete cosine transformation, discrete wavelet transformation and combine output of both techniques with image fusion. Proposed algorithm enhanced features and removal noise by decomposition of image using DWT and discrete cosine transformation, adaptive histogram equalization is very important part in this algorithm for smooth image. The tested results of different images are comparing with previous method, generating result with different parameters; less mean square error and high PSNR for improve the quality of an image. This paper presents a hybrid model used various parameter for enhance images like satellite images, medical images etc.