Rajesh Kumar Kaushal

Bio: Rajesh Kumar Kaushal is an academic researcher from University Institute of Engineering and Technology, Panjab University. The author has contributed to research in topics: Computer science & Blockchain. The author has an hindex of 4, co-authored 26 publications receiving 38 citations.

Prophetic Analysis of Bitcoin price using Machine Learning Approaches

Abstract: The most significant disturbance now affecting all economies and financial institutions is the digital transformation of economies. The world’s economy and financial institutions are digitizing at an unprecedented rate. Bitcoin is a devolved crypto-currency, or digital asset, that uses blockchain technology to expedite peer-to-peer financial transactions. Price volatility is one of the primary issues with decentralized cryptocurrencies, highlighting the need of examining the underlying price mechanism. Additionally, Bitcoin prices display non-stationary behaviour, meaning that their statistical distribution fluctuates over time. Bitcoin prices are stochastic, and no one set of characteristics can be used to forecast them completely. Nonetheless, academics have demonstrated varying degrees of effectiveness in estimating Bitcoin values using various feature sets. This article explains how to forecast Bitcoin price movements and prices using machine learning approaches. We intend to apply ARIMA, Facebook Prophet and XGBoost techniques for price prediction.

A Systematic Analysis of Applications Of Blockchain in Healthcare

Abstract: advent of the internet and its impact on healthcare domain made it possible to store, access and update medical records anywhere and anytime. The term “Electronic Health Record(EHR)” refers to a digital format in which patient information is kept. Because the records are patient-centered, any authorised user can access them from any location, at any time, and from any device. Blockchain is a new technology, booming in most of the industries. Blockchain contains a number of built-in characteristics, including distributed ledger, decentralized storage, authenticity, confidentiality, and immutability and it has progressed actual applications in industries like healthcare. Blockchain achieves fortifying and averting the modification of electronic health records. Blockchain supports the quality of healthcare services to the benefits of both patients and hospitals. This paper investigates the existing techniques used in healthcare blockchain system in the domain of authentication, data integrity, and confidentiality.

Potato Blight: Deep Learning Model for Binary and Multi-Classification

Abstract: Detection of plant crop diseases has become an active field of research day by day due to increasing the demand for such systems and techniques as crop diseases are now become a common part of agriculture. Focusing on this demand and need, we have developed a Convolutional neural network (CNN)-based Deep learning (DL) multi-classification model which classifies the total of 900 real-time collected images of potato crop plants having healthy and potato blight (PB) disease images based on their PB disease severity level, along with this binary classification has also been done to simply classify the healthy and disease crop leaf. A total of four disease severity levels have been taken into account which resulted in a binary classification accuracy of 90.77% and 94.77% of best multi-classification accuracy. This work will be a great contribution in the field of potato disease recognition and detection using DL approaches.

Using Mobile Computing to Provide a Smart and Secure Internet of Things (IoT) Framework for Medical Applications

Abstract: Mobile computing and technology are becoming more common in many parts of private life and public services, and they are playing an increasingly important role in healthcare, not just for sensory devices but also for communication, recording, and display. They are used for more than only sensory devices but also for communications, recording, and display. Numerous medical indications and postoperative days must be monitored carefully. As a result, the most recent development in Internet of Things- (IoT-) based healthcare communication has been embraced. The Internet of Things (IoT), which is employed in a wide range of applications, is a catalyst for the healthcare industry. Healthcare data is complicated, making it difficult to handle and evaluate in order to derive useful information for decision-making. On the other hand, data security is a vital requirement in a healthcare data systems. Determining the need for a smart and secure IoT platform for healthcare applications, we create one in this study. Here, a cutting-edge encryption algorithm is used to protect the health data. Normalization is first used to preprocess the data and remove any irrelevant information. Using principal component analysis and logistic regression, the data’s features are extracted (LR-PCA). To choose the pertinent features, a feature selection process based on genetic algorithms is used. We have put out a brand-new kernel homomorphism. To increase the security of the IoT network, use the two-fish Encryption algorithm (KHTEA). EBSMO (exponential Boolean spider monkey optimization) is used to further boost the encryption process’ effectiveness. Utilizing the MATLAB simulation tool, the proposed system is assessed, and the metrics are contrasted with the accepted practices. Our suggested solution has been shown to be effective in protecting medical healthcare data. The effectiveness of the proposed and existing approaches is assessed using metrics for encryption time, execution time, and security level. The security precautions we suggested for healthcare data worked well.

A Technical Review Report on Cyber Crimes in India

Abstract: In modern society the role of Internet and computer system is well recognized. People are greatly benefited with the development of networking and cyber space but some people are using this development in unethical way to have some illegal benefits. Recently different types of Social-networking attacks are witnessed by social networking sites user. Internal Revenue Service (IRS) impersonation scams, along with technical support scams are the most common type of tricks used by the attackers on unsuspecting victims in order to achieve financial benefits. The ratio of cyber-crime in India is constantly rising due to various reasons. Cyber-crlminals are very difficult to trace and this advantage is fully utilize by scammers. In this paper an intensive review has been done on cyber-crime in India. The studies shows that fraud cases are increasing and the victims are mostly in the age group of 20 – 29 years. Mostly children and women are affected. Thus, awareness programs are required for preventing or avoiding cyber-crime in India.

A redrawn Vandenverg and Kuse mental rotations test : different versions and factors that affect performance

Abstract: The available versions of the Vandenberg and Kuse (1978) Mental Rotations Test (MRT) have physically deteriorated because only copies of copies are available. We report results from a redrawn version of the MRT and for alternate versions of the test. Males perform better than females, and students drawn from the physical sciences perform better than students drawn from the social sciences and humanities, confirming other reports with the original version of the MRT. Subjects find it very hard to perform the MRT when stimuli require rotation along both the top/bottom axis and the left/right axis. The magnitude of effect sizes for sex (which account, on average, for some 20% of the variance) does not increase with increasing difficulty of the task. Minimal strategy effects were observed and females did not perform differently during the menstrual period as opposed to the days between the menstrual periods. Practice effects are dramatic, confirming other reports with the original MRT, and can also be shown to be powerful in a transfer for practice paradigm, where test and retest involve different versions of the MRT. Main effects of handedness on MRT performance were not found.

Levels of expertise and instructional design

Abstract: Cognitive load theory assumes that information should be structured to eliminate any avoidable load on working memory in order to enhance learning. We hypothesized that the appropriate type of structure may depend on the learner's level of expertise. Less expert learners using a diagram might require the diagram to be physically integrated with related text-based information in order to reduce cognitive load. However, the same diagram might be intelligible in isolation by more experienced learners, who might require the elimination of redundant text to reduce cognitive load. The results of three experiments indicated that as level of expertise increased, the best instructional designs changed from ones in which diagrams and text were physically integrated to ones in which the text was eliminated.

Efficient Bag of Deep Visual Words Based features to classify CRC Images for Colorectal Tumor Diagnosis

Abstract: CRC images are one of the most essential diagnostic techniques for colorectal cancer detection. The bag of deep visual words-based methodology (BoDVW) has proven to be a significant representation of CRC images for its enhanced discriminability when compared to earlier deep learning-based methods. This study presents a new Efficient Bag of Deep Visual Words (EBoDVW) feature that takes advantage of three alternative scales of the VGG16 model's 4th pooling layer's output feature map. Max pooling operation has done with three kernels for EBoDVW-based features. The proposed characteristics are evaluated using the Support Vector Machine (SVM) classification technique on the CRC public dataset, which contains over 1133 CRC pictures. The results of proposed experiments reveal that our strategy generates a consistent and prominent classification accuracy of 98.2 %.

Recognizing Wheat Aphid Disease Using a Novel Parallel Real-Time Technique Based on Mask Scoring RCNN

Abstract: Wheat is one of the most common cereal crops in India. Aphids cause extensive damage to the whole wheat plant and lead to high yields loss. The aphid is transmitted on the summer day. Once the aphid is transmitted over the leave, the whole plant leave is damaged. Due to this, got damage whete plant and it also reduce the quality of wheat grain. Due to which the it is necessary to identify each aphid on wheat leaves. Only manual process is there to identify each aphid on wheat leave. Manually identification is a time-consuming and high laborious process. Therefore, the identification of wheat aphids through the Mask RCNN model can easily identify the location of each aphid in individual leave automatically. With the help of a Canon camera, a total of 6500 wheat images have been captured in the Punjab region with temperature 21–24° temperature. The Labelme software is used for the annotation of wheat leaves and wheat aphid. A total of 2300 and 1000 images have been randomly selected for training and testing purposes. The Mask scoring RCNN model is having a network capacity for learning the quality of predicted instance masks. Among all 1221 wheat leaves, a total number of 1021 wheat aphids have been found. The manually annotated ROI was compared to mask scoring ROI for wheat aphid identification and localization. Thus, the Mask scoring RCNN model has been achieved a high F1-score (96.66%) for wheat aphid detection in single wheat leave.

A Systematic Analysis of Applications Of Blockchain in Healthcare

Abstract: advent of the internet and its impact on healthcare domain made it possible to store, access and update medical records anywhere and anytime. The term “Electronic Health Record(EHR)” refers to a digital format in which patient information is kept. Because the records are patient-centered, any authorised user can access them from any location, at any time, and from any device. Blockchain is a new technology, booming in most of the industries. Blockchain contains a number of built-in characteristics, including distributed ledger, decentralized storage, authenticity, confidentiality, and immutability and it has progressed actual applications in industries like healthcare. Blockchain achieves fortifying and averting the modification of electronic health records. Blockchain supports the quality of healthcare services to the benefits of both patients and hospitals. This paper investigates the existing techniques used in healthcare blockchain system in the domain of authentication, data integrity, and confidentiality.