Best Paper Award

Healthcare data [1] is becoming more complex and voluminous, which makes it difficult to extract valuable insights and improve healthcare services. Data analysis can help solve this challenge by providing a powerful solution. In this paper, the authors introduce the concept of data analysis in healthcare and explain its significance in enhancing patient outcomes, reducing healthcare costs, and improving the quality of care. The authors also discuss the different types of healthcare data, including electronic health records, claims data, medical imaging data, and patient-generated data, and explain the techniques used in data preprocessing, including data cleaning, transformation, and integration. Moreover, the authors describe the techniques used in exploratory data analysis (EDA), such as data visualization, summary statistics, and correlation analysis, which can help identify patterns and trends in healthcare data. They also explain the various predictive modeling techniques used in healthcare data analysis, including regression analysis, decision trees, and neural networks, which can be used for predicting patient outcomes and identifying risk factors. Additionally, the authors discuss the development of clinical decision support systems using data analysis, which can assist healthcare professionals in making informed decisions about patient care. The paper provides real-world examples of how data analysis has been used in healthcare, such as predicting hospital readmissions, identifying high-risk patients, and improving medication adherence. Finally, the authors discuss emerging trends in data analysis in healthcare, such as the use of artificial intelligence and machine learning, and their potential impact on healthcare. Overall, this paper highlights the importance of data analysis in healthcare and its potential to revolutionize the industry.

Data Analysis, Healthcare Data, EHR, Claims Data, Medical Imaging Data, Data Preprocessing, Data Cleaning, Data transformation, Data Integration, EDA, Data Visualization, Clinical Decision Support, AI, ML.

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Sameer Shukla, "Unlocking the Power of Data: An Introduction to Data Analysis in Healthcare", International Journal of Computer Sciences and Engineering, Vol.11, Issue.3, pp.1-9, 2023.

Data Pipeline[1][2] is a series of actions which moves data from the one source to the destination, the complexity of Data Pipeline varies from use-case to use-case. The traditional data pipeline cleanups the data, aggregates the data and move it from one place to another, it sounds simple but it’s very complex as the organization deals with huge and complex data and the expectation from pipeline is that it should be robust, fast, notify about the status and it should do the same task repeatedly without failing. The modern data pipelines are slightly different in nature they are supposed to deal with Petabytes of data, they stores the data in various flavors of the cloud, should provide real-time data analysis. Apache Airflow is one such tool which simplifies the entire Data Pipeline creation to a great extent and the only pre-requisite is the basic Python Knowledge. This paper focuses on the stock-exchange data pipeline creation by using the Airflow concepts such as DAGs and Operators.

Data-Pipeline, Python, Pandas, Seaborn, Apache-Airflow, GCP, Kaggle.

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Sameer Shukla, "Developing Pragmatic Data Pipelines using Apache Airflow on Google Cloud Platform", International Journal of Computer Sciences and Engineering, Vol.10, Issue.8, pp.1-8, 2022.

The “speedy progress in multimedia and imaging technology, the numbers of images uploaded and shared on the internet have increased. It leads to develop the highly effective image retrieval system to satisfy the human needs. The content-Based image retrieval (CBIR) system which retrieves the image based on the Low level features such as color, texture and shape which are not sufficient to describe the user’s high level perception for images. Therefore reducing this semantic gap problem of image retrieval is challenging task. Some of the most important notions in image retrieval are keywords, terms or concepts. Terms are used by humans to describe their information need and it also used by system as a way to represent images. Here in this paper different types of features their advantage and disadvantages are described. We have carried out comparative analysis of different techniques used in our system to determine best suitable technique to be used for our proposed system. We have analyze the our proposed system on large image dataset and our approach gives high precision and required less computations which proves efficiency of our system. In our proposed system we have evaluated the performance of our feature extraction techniques i.e. FCH and GWT using precision and recall metric and compared the result with existing feature extraction approaches i.e. color moment and GWT. Implementation results show that the feature extraction techniques for the proposed system are better than the existing techniques. SVM Classifier also gives good accuracy using these feature extraction” techniques.

CBIR, Color Moment, Fuzzy Color Histrogram, Gabor Wavelate, Support Vector Machine

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A.D. Mahajan, S. Chaudhary, "Hybrid Features For Content Based Image Retrieval System", International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.11-15, 2018.

In the modern society everybody wants to be familiar with people’s characteristics to predict and be aware of their reaction to diverse situation, though it’s hard to understand psychological nature and characteristics of a person. For this reason, researches have been carried out in this direction to predict the characteristics of a person such as maturity, warmth, intelligence, sociality, dominance, as well as the trustworthiness. Here aim is to identify person’s characteristics based on the facial features by using techniques such as SURF, which is going to be used for the extraction of the facial features and K-nearest neighbor classifier for identification of the characteristics of the human being. With the various features mentioned and by using the appropriate techniques, the characteristics of a person can be predicted. The overall performance of the proposed work has been estimated by well established dataset and results show that the proposed work has performed well.

Speed-Up Robust Features, Interest points,Character recognition

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Mahesh U Nagaral, T. Hanumantha Reddy, "Predicting the Characteristics of a Human from Facial Features by Using SURF", International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.9-16, 2018.

Frequency channels are scarce resource and available in a limit. Due to this limitation user may experience call termination and call blocking as the traffic increases. We can limit call termination and call blocking by allocating permanent channel to home (apartment) and office user due to very limited movement. They are almost fixed if they communicate with each other frequently and this is the normal scenario in urban areas. By using this strategy these users will not feel any disconnection due to inaccessibility of frequency channel in cell. To achieve this we have proposed a scheme based on fixed channel and analyze the performance for both normal channel allocation and preserved channel allocation for home and office users. We have performed and compared the results of call blocking probability, handover failure probability, call termination due to successful handover probability and probability for not completed calls.

Channel allocation, handover, call blocking, call termination

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Suyash Kumar, P.V. Suresh, "Performance Analysis of Channel Allocation Scheme for A System Model Based On Urban Structure", International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.1-5, 2018.

Regular-frequent patterns are an important type of regularities that exist in transactional, time-series and any other types of databases. A frequent pattern can be said regular-frequent if it appears at a regular interval given by the user specified threshold in the transactional database. The regularity calculation for every candidate pattern is a computationally expensive process, especially when there exist long patterns. Currently the FP-growth algorithm is one of the most popular and fastest approaches to mining periodic frequent item sets. Therefore, in this paper we introduce a novel concept of mining regular-frequent patterns (RFP) in transactional databases. We introduce two mining techniques based on transaction number and also based on products or itemsets on the vertical data format. The efficiency is achieved by eliminating aperiodic or irregular patterns during execution based on suboptimal solutions. Our tree based structure helps to captures the database contents in highly compact manner. Our experimental results are highly efficient and scalable as well as improve the overall response time.

Frequent patterns, regular patterns, transactional databases, vertical data format

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S. Rana, "Analysis of Regular-Frequent Patterns in Large Transactional Databases", International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1-5, 2018.

Docking process of UAV Autonomous Refueling is a critical issue during the docking phase of autonomous aerial refueling (AAR), and the successful docking between the probe and drogue need higher probability for an aerial refueling system. To cope with this issue, a novel and effective model based on the theory of stochastic process crossing target area is proposed. In order to ensure its accurate and easy application, according to prior information and assumptions for the movements of the probe related to the drogue, the probe-drogue docking success probability is converted to the probability of the probe located in the circle area of drogue. The temporal and spatial characteristics of the pointing error have been considered which makes the model of the docking success probability more accord with the actual situation. simulations were conducted to demonstrate the effectiveness of the proposed method. This model provides theoretical support for the design and verification of AAR’s control system.

Autonomous aerial refueling, UAV, stochastic process, docking success probability

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[17] Xufeng Wang, Jianmin Li, Xingwei Kong, Xinmin Dong, Bo Zhang, “An Approach to Mathematical Modeling and Estimation of Probe-Drogue Docking Success Probability for UAV Autonomous Aerial Refuelin”, International Journal of Aerospace Engineering, Vol.2017, pp.1-14, 2017.
[18] X. F. Wang, X. M. Dong, X. W. Kong, J. M. Li, B. Zhang, “Drogue detection for autonomous aerial refueling based on convolutional neural networks”, Chinese Journal of Aeronautics, vol. 30, no. 1, pp. 380–390, 2017.
[19] H. W. Xie, H. L. Wang, “Binocular vision-based short-range navigation method for autonomous aerial refueling”, Journal of Beijing University of Aeronautics and Astronautics, vol. 37, no. 2, pp. 206–209, 2011.
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Xiangmin Wang, Jun Wang, "Modeling of Probe-Drogue Docking Success Probability for UAV Autonomous Refuelling", International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1-6, 2018.

Offline Handwritten Character Recognition is a very challenging field to work upon, as the handwriting of an individual differs very much from another individual, even the handwriting of an individual may differ on different times. Studies have shown that recognition efficiency of characters depends on the ways the features are extracted and formulated as the feature vector. A lot of techniques have been proposed by the various research scholars for feature extraction. In this paper, a combinational approach of feature extraction is proposed as combinational feature vectors (Gradient features, Zernike complex moment features, and Wave based features) may contribute to improved recognition rate. For training and testing purpose, samples of Hindi numerals from 0 to 9 are taken. A feature vector of directional gradient histogram (DGH), a feature vector of Zernike complex moments (ZCM) and a feature vector of Wave features (WF) are feed to the Back-propagation based Neural Network classifiers for training and recognition rate of approx. 79.7%, 92.7% and 73% are attained respectively. By combining the feature vectors DGH, CZM, and WF, a higher recognition rate of 96.4% is obtained for isolated Hindi Numerals.

Character Recognition, Gradient features, Zernike Moments, Wave features, Backpropagation Neural Network

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[20] S. Arora, D. Bhattacharjee, M. Nasipuri, D. K. Basu and M. Kundu, "Combining multiple feature extraction techniques for handwritten Devanagari character recognition," IEEE Region 10 Colloquium and 3rd International Conference on Industrial and Information Systems, Dec. 2008.
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Ajay Indian, Karamjit Bhatia, "A Combinational Approach of Feature Extraction for Offline Handwritten Hindi Numeral Recognition", International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.1-8, 2018.

Energy awareness is idiopathic task in wireless sensor network. For prolonging lifetime of wireless sensor network, the use of sensors plays a prerequisite role. Saving sensors energy is the main outfit so that network lifetime will improve. So keeping in mind the sensor remaining energy; a new clustering protocol which will work in sleep awake mode is proposed. Along with this mutual exclusion is used in sleep awake mode to fetch cluster head over communication range. In modernistic stint none of the protocol uses mutual exclusion algorithm in sleep awake mode. The proposed protocol Mutual Exclusive Sleep Awake Distributive Clustering (MESADC) chooses a cluster head in such a manner so that sensor lifetime will improve. If sensor lifetime improves then network’s lifetime automatically improve. The performance of MESADC protocol is compared with HEED protocol. Experimental results were obtained with the help of MATLAB. On the groundwork of the comparison between two protocols one finds that the performance of MESADC protocol is prominent in prolonging lifetime of wireless sensor network as compared with HEED protocol.

Sleep Awake, Distributive, Clustering, Sensor, Network

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B. Gupta, S. Rana, "Mutual Exclusive Sleep Awake Distributive Clustering (MESADC): An Energy Efficient Protocol for Prolonging Lifetime of Wireless Sensor Network", International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.1-7, 2018.

Pole-Like Objects (PLOs) in the road environment located nearby the road boundary are important roadway assets. They play vital role in the road safety inspection and road planning. In present study a novel automated method for the detection of PLOs from Mobile Laser Scanner (MLS) point cloud data has been proposed. Proposed method includes four basic steps. Initially ground points are roughly filtered out from the input dataset to reduce the processing of un-necessary points; formerly local window search is performed at non-ground points to find out the concentrated point distribution. Principal Component Analysis (PCA) has been implemented at such concentrated distributed points for the identification of PLOs. In last step of proposed method knowledge based information are used for suppressing the false positives and rectifying the output. Proposed method has been tested on a MLS point cloud data of complex road environment and corresponding PLOs are detected having completeness and correctness of 91.48 % and 86.00 % respectively.

LiDAR, Pole Like Objects, PCA

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[12] S. Pu, M. Rutzinger, G. Vosselman, S.O. Elberink, “Recognizing basic structures from mobile laser scanning data for road inventory studies”, ISPRS Journal of Photogrammetry and Remote Sensing 6(66), S28–S39, 2011.

A. Husain, R.C. Vaishya, Md. Omar Sarif, "A Moving Window Search Method for Detection of Pole Like Objects Using Mobile Laser Scanner Data", International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.1-6, 2018.