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Source author record

Debasis Dwibedy

Debasis Dwibedy appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

ResearcherUnclaimed source record
Data Structures and AlgorithmsOperating SystemsArtificial IntelligenceComputational ComplexityDiscrete MathematicsEmerging TechnologiesMachine Learning

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Published work

8 published item(s)

preprint2026arXiv

Smart Profit-Aware Crop Advisory System: Kisan AI

Modern crop advisory systems exhibit a critical limitation termed \textit{economic blindness}. These systems primarily optimize for biological yield, often overlooking market price, which can lead farmers toward agronomically sound yet financially unviable decisions. In this paper, we develop Kisan AI, a smart profit-aware crop advisory system that resolves the above-mentioned limitation through a research-driven, full-stack application. We train the Random Forest(RF) classifier model on a nine-feature benchmark dataset, the standard seven agronomic attributes augmented with a \textit{market\_price} variable, and evaluated against eight baseline models, considering the evaluation matrices, such as, accuracy, precision, recall, F1-score, and Log Loss. The RF model achieves the highest accuracy of 99.3\% and the lowest Log Loss, confirming that the inclusion of market price as a predictive feature is both valid and impactful. We then implement the RF model within a multilingual progressive Web App alongside a Facebook Prophet six-month price forecasting engine and a MobileNetV2 disease detection module. A nine-language AI chatbot powered by the Anthropic Claude API unifies all modules into a single, mobile-installable platform accessible to farmers across India.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Note on Hardness of Multiprocessor Scheduling with Scheduling Solution Space Tree

We study the computational complexity of the non-preemptive scheduling problem of a list of independent jobs on a set of identical parallel processors with a makespan minimization objective. We make a maiden attempt to explore the combinatorial structure showing the exhaustive solution space of the problem by defining the \textit{Scheduling Solution Space Tree (SSST)} data structure. The properties of the \textit{SSST} are formally defined and characterized through our analytical results. We develop a unique technique to show the problem $\mathcal{NP}$ using the SSST and the \textit{Weighted Scheduling Solution Space Tree (WSSST)} data structures. We design the first non-deterministic polynomial-time algorithm named \textit{Magic Scheduling (MS)} for the problem based on the reduction framework. We also define a new variant of multiprocessor scheduling by including the user as an additional input parameter. We formally establish the complexity class of the variant by the reduction principle. Finally, we conclude the article by exploring several interesting open problems for future research investigation.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A 2-Competitive Largest Job on Least Loaded Machine Online Algorithm based on Multi Lists Scheduling Model

Online scheduling in identical machines with makespan minimization has been a well studied research problem in the literature. In online scheduling, the scheduler receives a list of jobs one by one and assigns each incoming job on the fly irrevocably to one of the machines before the arrival of the next job. In this paper, we study a variant of the online scheduling problem, where jobs are requested on the fly from k distinct sources. On simultaneous arrival of jobs from multiple sources, it is a non-trivial research challenge to ensure fairness in scheduling with respect to distinct sources, while optimizing the overall makespan of the schedule. We develop a novel Multi Lists Scheduling Model(MLS) and propose a 2-competitive deterministic online algorithm namely Largest Job on Least Loaded Machine(LJLLM) based on the MLS model to address the above mentioned research challenge.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A New Fairness Model based on User's Objective for Multi-user Multi-processor Online Scheduling

Resources of a multi-user system in multi-processor online scheduling are shared by competing users in which fairness is a major performance criterion for resource allocation. Fairness ensures equality in resource sharing among the users. According to our knowledge, fairness based on the user's objective has neither been comprehensively studied nor a formal fairness model has been well defined in the literature. This motivates us to explore and define a new model to ensure algorithmic fairness with quantitative performance measures based on optimization of the user's objective. In this paper, we propose a new model for fairness in Multi-user Multi-processor Online Scheduling Problem(MUMPOSP). We introduce and formally define quantitative fairness measures based on user's objective by optimizing makespan for individual user in our proposed fairness model. We also define the unfairness of deprived users and absolute fairness of an algorithm. We obtain lower bound results for the absolute fairness for m identical machines with equal length jobs. We show that our proposed fairness model can serve as a framework for measuring algorithmic fairness by considering various optimality criteria such as flow time and sum of completion times.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Online Scheduling with Makespan Minimization: State of the Art Results, Research Challenges and Open Problems

Online scheduling has been a well studied and challenging research problem over the last five decades since the pioneering work of Graham with immense practical significance in various applications such as interactive parallel processing, routing in communication networks, distributed data management, client-server communications, traffic management in transportation, industrial manufacturing and production. In this problem, a sequence of jobs is received one by one in order by the scheduler for scheduling over a number of machines. On arrival of a job, the scheduler assigns the job irrevocably to a machine before the availability of the next job with an objective to minimize the completion time of the scheduled jobs. This paper highlights the state of the art contributions for online scheduling of a sequence of independent jobs on identical and uniform related machines with a special focus on preemptive and non-preemptive processing formats by considering makespan minimization as the optimality criterion. We present the fundamental aspects of online scheduling from a beginner's perspective along with a background of general scheduling framework. Important competitive analysis results obtained by well-known deterministic and randomized online scheduling algorithms in the literature are presented along with research challenges and open problems. Two of the emerging recent trends such as resource augmentation and semi-online scheduling are discussed as a motivation for future research work.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Semi-online Scheduling: A Survey

In online scheduling, jobs are available one by one and each job must be scheduled irrevocably before the availability of the next job. Semi-online scheduling is a relaxed variant of online scheduling, where an additional memory in terms of buffer or an Extra Piece of Information(EPI) is provided along with input data. The EPI may include one or more of the parameter(s) such as size of the largest job, total size of all jobs, arrival sequence of the jobs, optimum makespan value or range of job's processing time. A semi-online scheduling algorithm was first introduced in 1997 by Kellerer et al. They envisioned semi-online scheduling as a practically significant model and obtained improved results for $2$-identical machine setting. This paper surveys scholarly contributions in the design of semi-online scheduling algorithms in parallel machine models such as identical and uniformly related by considering job's processing formats such as preemptive and non-preemptive with the optimality criteria such as Min-Max and Max-Min. The main focus is to present state of the art competitive analysis results of well-known semi-online scheduling algorithms in a chronological overview. The survey first introduces the online and semi-online algorithmic frameworks for the multi-processor scheduling problem with important applications and research motivation, followed by a taxonomy for semi-online scheduling. Fifteen well-known semi-online scheduling algorithms are stated. Important competitive analysis results are presented in a chronological way by highlighting the critical ideas and intuition behind the results. An evolution time-line of semi-online scheduling setups and a classification of the references based on EPI are outlined. Finally, the survey concludes with the exploration of some of the interesting research challenges and open problems.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

New Competitive Analysis Results of Online List Scheduling Algorithm

Online algorithm has been an emerging area of interest for researchers in various domains of computer science. The online $m$-machine list scheduling problem introduced by Graham has gained theoretical as well as practical significance in the development of competitive analysis as a performance measure for online algorithms. In this paper, we study and explore the performance of Graham's online \textit{list scheduling algorithm(LSA)} for independent jobs. In the literature, \textit{LSA} has already been proved to be $2-\frac{1}{m}$ competitive, where $m$ is the number of machines. We present two new upper bound results on competitive analysis of \textit{LSA}. We obtain upper bounds on the competitive ratio of $2-\frac{2}{m}$ and $2-\frac{m^2-m+1}{m^2}$ respectively for practically significant two special classes of input job sequences. Our analytical results can motivate the practitioners to design improved competitive online algorithms for the $m$-machine list scheduling problem by characterization of real life input sequences.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Online Rainbow Coloring In Graphs

Rainbow coloring is a special case of edge coloring, where there must be at least one path between every distinct pair of vertices that consists of different color edges. Here, we may use the same color for the adjacent edges of a graph representing two different paths from a single vertex. In online rainbow coloring, we have no priori knowledge about the vertices and edges of the graph, in fact the edges are available one by one. We have to color an edge as soon as it arrives and before the arrival of the next edge. We can not revoke the coloring decision once it is made. According to our knowledge, there is no study of online rainbow coloring for graphs. In this paper, we make a first attempt to propose an online algorithm named Least Recently Used Color(LRUC) for online rainbow coloring. We analyze the performance of LRUC through competitive analysis. We show that LRUC is optimal for line graph, tree and star graph. For 1-cyclic graph, LRUC is shown to be (2-2/n)-competitive, where n>3. We obtain the competitive ratios of (n-1)/3 and n-1 for wheel and complete graphs respectively, where n is the number of vertices.

0 citations0 reviewsNo code linkedOpen access