http://localhost:8080/xmlui/handle/123456789/212 Sun, 26 Apr 2026 08:07:03 GMT 2026-04-26T08:07:03Z http://localhost:8080/xmlui/handle/123456789/3839 Title: Modelling Renewable Energy-Based Cloud Computing Environment by Harnessing Solar Energy Authors: Nayak, Sanjib Kumar; Padhee, Subhransu; Panda, Sanjaya Kumar Description: NITW Wed, 01 Jan 2025 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/3839 2025-01-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/3838 Title: An Efficient Approach for Energy Conservation in Cloud Computing Environment Authors: Pande, Sohan Kumar; Panda, Sanjaya Kumar; Sahu, Preeti Ranjan Abstract: Recent trends of technology have explored a numerous applications of cloud services, which require a significant amount of energy. In the present scenario, most of the energy sources are limited and have a greenhouse effect on the environment. Therefore, it is the need of the hour that the energy consumed by the cloud service providers must be reduced and it is a great challenge to the research community to develop energy-efficient algorithms. To design the same, some researchers tried to maximize the average resource utilization, whereas some researchers tried to minimize the makespan. However, they have not considered different types of resources that are present in the physical machines. In this paper, we propose a task scheduling algorithm, which tries to improve utilization of resources (like CPU, disk, I/O) explicitly, which in turn increases the utilization of active resources. For this, the proposed algorithm uses a fitness value, which is a function of CPU, disk and I/O utilization, and processing time of the task. To demonstrate the performance of the proposed algorithm, extensive simulations are performed on both proposed algorithm and existing algorithm MaxUtil using synthetic datasets. From the simulation results, it can be observed that the proposed algorithm is a better energy-efficient algorithm and consumes less energy than the MaxUtil algorithm. Description: NITW Wed, 01 Jan 2025 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/3838 2025-01-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/3836 Title: An Item-Oriented Collaborative Filtering Algorithm for Recommender Systems Authors: Panda, Sanjaya Kumar; Senapati, Manas Ranjan; Sahu, Pradip Kumar Abstract: Recommender systems (RSs) are one of the popular, common and widespread applications in today’s business. It predicts the rating that a user is likely to give an item and finds a list of items to recommend the user. In this context, it is primarily categorized into collaborative filtering-based (CF) RS and content-based RS. In the CF, the RS suggests the items that are based on the behavior of similar users and its past behavior. One of the well-known algorithms in the CF is k-nearest neighbor (kNN), which relies on the behavior (rating) of k similar users (items) in oriented to users (items). However, it suffers from the unknown rating problem and the insufficient neighbor problem if the k similar users (items) have not rated the corresponding item (with respect to the corresponding user). Therefore, we develop an item-oriented CF (ICF) algorithm for RSs to overcome the problems associated with the kNN algorithm. The proposed algorithm ICF selects k similar items of a given item by finding the similarity count of the neighbors. We implement both the algorithms and compare with the kNN algorithm using four datasets to show the superiority of the proposed algorithm ICF in terms of two errors, namely mean absolute error (MAE), root mean square error (RMSE), precision, recall and F-score. Description: NITW Wed, 01 Jan 2025 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/3836 2025-01-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/3834 Title: Smart Municipal Services and Predictive Healthcare: A Thermal Imaging Perspective Authors: Jena, Kalyan Kumar; Bhoi, Sourav Kumar; Panda, Sanjaya Kumar Abstract: In the present day, diabetes is viewed as a serious problem. It can be promoted and campaigned as a smart municipal service to create awareness. The heart, nerves, eyes, and other human disorders, among others, might all be negatively impacted by this illness. Thus, early detection of diabetes patients is crucial to implement preventative treatments as soon as possible. In this work, a machine intelligence (MI) based approach is proposed for classifying diabetic and non-diabetic patients from the thermal image analysis of the human foot. This approach is focused on several machine learning (ML) models, such as k-nearest neighbour (KNN), decision tree (DT), AdaBoost (AB), and Naive Bayes (NB), to carry out such classification mechanisms. This study employs a cross-validation mechanism with the number of folds (NFD) set to 3, 5, and 10. By analysing the percentage of classification accuracy (CA) based on the dataset for different ML-based models, KNN achieved superior classification results than DT, AB, and NB, which are 93.30%, 94.60%, and 95.10%, for NFDs 3, 5, and 10, respectively. Description: NITW Wed, 01 Jan 2025 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/3834 2025-01-01T00:00:00Z