Browsing by Author "Gulati, A."

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    Fairness in CPU Scheduling: A Probabilistic Algorithm
    (Institute of Electrical and Electronics Engineers Inc., 2024) Prasanna, S.; Gulati, A.; Anagha, H.C.
    This paper introduces a novel CPU scheduling algorithm for uniprocessor systems that employs a probabilistic function to enhance fair resource allocation. Unlike traditional algorithms, our approach specifically tackles the challenge of equitable resource distribution by integrating a probabilistic methodology whilst also keeping the priority of each process in mind. We detail the implementation and evaluate its performance against established algorithms, assessing metrics such as average turnaround time, average waiting time and the gini index. All the related code, data used for testing and a working webpage to try out the algorithm first hand can be found at GitHub. © 2024 IEEE.
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    Measuring the Severity of the Signs of Eating Disorders Using Machine Learning Techniques
    (CEUR-WS, 2024) Prasanna, S.; Gulati, A.; Karmakar, S.; Hiranmayi, M.Y.; Anand Kumar, M.
    The paper presents the results submitted by Team SCaLAR-NITK for task 3 of eRisk Lab at CLEF 2024 [1]. The dataset provided by the task organizers consisted of 74 subjects for training and 18 for testing. We begin by describing the data cleaning and preprocessing steps. Subsequently, we outline various approaches used to address the problem, such as Word2Vec, TF-IDF, Backtranslation and Dimensionality Reduction, among others. Finally, we summarize the results obtained from each approach. Our solutions demonstrated strong performance, achieving the best results in 7 out of the 8 evaluated metrics. © 2024 Copyright for this paper by its authors.
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    Petri Net-Based Verification of Adaptive Traffic Light Control with AIMD Algorithm
    (Institute of Electrical and Electronics Engineers Inc., 2024) Prasanna, S.; Gulati, A.; Anagha, H.C.; Prabhu, A.; Das, M.; Mohan, B.R.
    This paper introduces and analyses the performance of the Petri net model created to simulate a traffic control system using the Additive Increase Multiplicative Decrease (AIMD) algorithm. The Petri net model was designed using TimeNET [1] tool. The model was evaluated by analysing the Reachability Graph generated by a Depth First Search (DFS) and Backtracking based algorithm. Several criteria such as Stability, Boundedness, Deadlock, etc. were verified by our proposed algorithm. The model was then validated through a C++ code to ensure it performs correctly under different situations. All the related code, images, and tables used in this paper can be found at GitHub 1 © 2024 IEEE.