Gaganjot Kaur

Bio: Gaganjot Kaur is an academic researcher. The author has contributed to research in topics: Computer science & Denial-of-service attack. The author has an hindex of 2, co-authored 3 publications receiving 204 citations.

Improved J48 Classification Algorithm for the Prediction of Diabetes

Abstract: research work deals with efficient data mining procedure for predicting the diabetes from medical records of patients. Diabetes is a very common disease these days in all populations and in all age groups. Diabetes contributes to heart disease, increases the risks of developing kidney disease, nerve damage, blood vessel damage and blindness. So mining the diabetes data in efficient manner is a critical issue. The Pima Indians Diabetes Data Set is used in this paper; which collects the information of patients with and without having diabetes. The modified J48 classifier is used to increase the accuracy rate of the data mining procedure. The data mining tool WEKA has been used as an API of MATLAB for generating the J-48 classifiers. Experimental results showed a significant improvement over the existing J-48 algorithm. KeywordsDecision Tree, MATLAB, Data Mining, Diabetes, WEKA.

A Compressive Sensing based BEEIP Protocol for WANETS

Abstract: Routing Algorithms in the wireless environment are differentiating into different kinds like Geographical, Geo-casting, Hierarchical, Multi-path, Power-aware, and Hybrid routing algorithms. The typical objective of this paper is to explore Swarm Intelligence based routing protocols especially Bee-Inspired based routing protocols for providing multipath routing in Wireless ad hoc networks (WANETs). WANETs influence an agent-based routing protocol that defines a number of rules including that the majority of the participating nodes follow. Using routing technique, nodes are interconnected jointly so as to reduce computational and resource costs. Swarm Intelligence uses agent-like entities from insect's societies becoming a metaphor to fix the routing problem. Various insects interchange details based on their activities been performed along with the surroundings in which they operate to ensure to perform their tasks within an adaptive, efficient and scalable manner. It has been observed that the Bee-Inspired routing has not yet used compression algorithm to apply the bandwidth in more proficient manner. Therefore this paper proposes a LCBEEIP protocol who has utilized BEEIP protocol along with the feature of Loss Less data compression. The experimental results in the proposed technique have clearly shown that the proposed technique outperforms over the available techniques.

Improved Bee-Inspired routing protocol using Lzw based lossless compression

Abstract: This paper has focused entirely on Swarm Intelligence based routing protocols especially Bee-Inspired based routing protocols for providing multipath routing in Wireless ad hoc networks (WANETs). WANETs influence an agent-based routing protocol that defines numerous rules including that most the participating nodes follow. Using routing technique, nodes are interconnected jointly so for that reason reduces computational and resource costs. Swarm Intelligence uses agent-like entities from insect's societies to become metaphor to refurbish the routing problem. But it is been observed the fact that Bee-Inspired routing has not yet used compression algorithm to make use of the bandwidth in more proficient manner. Therefore this paper has proposed a LCBEEIP protocol that is utilized BEEIP protocol with the feature of Loss Less data compression. The experimental brings about the proposed technique have clearly shown the fact that proposed technique outperforms over the current techniques. The proposed method has achieved up to 99.9167 packet delivery ratio.

Contactless Heart Rate Measurement using Image Processing

Abstract: Non-contact methods of determining the human body’s heart rate are of interest for clinical use. This research used a video magnification technique on the individual frames from a 15-second video taken using a digital single-lens reflex (DSLR) camera at 30 frames per second. It was possible to determine the heart rate beats per minute by extracting the green spectrum from a region of interest information from the video frames. In this paper, three methods are presented using this colour change between the frames transform as a signal to find the heart rate. While capturing the video’s using the camera, a commercially available pulse oximeter was used to obtain the pulse rate from the participant’s finger to validate the values calculated from the image processing techniques presented. The results show that it is possible to get a heart rate in terms of pulse rate reading using a camera and the developed MATLAB code.

Swarm Intelligence-Based Feature Selection Algorithm of EEG Classification for Brain Emotion Detection: A Review

Abstract: In recent technology, Brain Computer Interfacing (BCI) plays a prominent role through which a human can interact with the outside world using brain signals. Brain signals are the current reactions in the brain when neurological activity occurs and are closely associated with critical thinking, logical decision-making, recognition, human interaction, and to some extent, Human Intelligence. Brain waves influence most real human body reactions; compared to other types of emotion analysis, brain wave emotion analysis comes out on top. Emotion Detection has a broad perspective in interdisciplinary research for neurologists, psychologists, engineers, etc. This paper aims to represent the latest comprehensive review of emotion detection techniques using Electroencephalography (EEG) Signals, feature extraction, feature selection, and classification, along with pointing out existing problems in the field and potential growth areas.

An AI?based intelligent system for healthcare analysis using Ridge?Adaline Stochastic Gradient Descent Classifier

Abstract: Recent technological advancements in information and communication technologies introduced smart ways of handling various aspects of life. Smart devices and applications are now an integral part of our daily life; however, the use of smart devices also introduced various physical and psychological health issues in modern societies. One of the most common health care issues prevalent among almost all age groups is diabetes mellitus. This work aims to propose an artificial intelligence-based intelligent system for earlier prediction of the disease using Ridge-Adaline Stochastic Gradient Descent Classifier (RASGD). The proposed scheme RASGD improves the regularization of the classification model by using weight decay methods, namely least absolute shrinkage and selection operator and ridge regression methods. To minimize the cost function of the classifier, the RASGD adopts an unconstrained optimization model. Further, to increase the convergence speed of the classifier, the Adaline Stochastic Gradient Descent Classifier is integrated with ridge regression. Finally, to validate the effectiveness of the intelligent system, the results of the proposed scheme have been compared with state-of-the-art machine learning algorithms such as support vector machine and logistic regression methods. The RASGD intelligent system attains an accuracy of 92%, which is better than the other selected classifiers.

Architecting Smart City Digital Twins: Combined Semantic Model and Machine Learning Approach

Abstract: This work was motivated by the premise that next-generation smart city systems will be enabled by widespread adoption of sensing and communication technologies deeply embedded within the ph...

Classification of Diabetes Mellitus Disease (DMD): A Data Mining (DM) Approach

Abstract: The diabetes mellitus disease (DMD) commonly referred as diabetes is a significant public health problem. Predicting the disease at the early stage can save the valuable human resource. Voluminous datasets are available in various medical data repositories in the form of clinical patient records and pathological test reports which can be used for real-world applications to disclose the hidden knowledge. Various data mining (DM) methods can be applied to these datasets, stored in data warehouses for predicting DMD. The aim of this research is to predict diabetes based on some of the DM techniques like classification and clustering. Out of which, classification is one of the most suitable methods for predicting diabetes. In this study, J48 and Naive Bayesian techniques are used for the early detection of diabetes. This research will help to propose a quicker and more efficient technique for diagnosis of disease, leading to timely and proper treatment of patients. We have also proposed a model and elaborated it step-by-step, in order to make medical practitioner to explore and to understand the discovered rules better. The study also shows the algorithm generated on the dataset collected from college medical hospital as well as from online repository. In the end, an article also outlines how an intelligent diagnostic system works. A clinical trial of this proposed method involves local patients, which is still continuing and requires longer research and experimentation.