Sharmistha Roy
@ushamartinuniversity.com
Associate Professor, Faculty of Computing and Information Technology
Usha Martin University
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Dipan Das, Sharmistha Roy, and Bibhudatta Sahoo
Springer Nature Singapore
Naghma Khatoon, Sharmistha Roy, and Ritushree Narayan
CRC Press
Purnima Kumari Srivastava, Sharmistha Roy, and Prashant Pranav
AIP Publishing
Naghma Khatoon and Sharmistha Roy
AIP Publishing
Pallab Banerjee, Sharmistha Roy, Umar Muhammad Modibbo, Saroj Kumar Pandey, Parul Chaudhary, Anurag Sinha, and Narendra Kumar Singh
MDPI AG
The continuously evolving world of cloud computing presents new challenges in resource allocation as dispersed systems struggle with overloaded conditions. In this regard, we introduce OptiDJS+, a cutting-edge enhanced dynamic Johnson sequencing algorithm made to successfully handle resource scheduling challenges in cloud computing settings. With a solid foundation in the dynamic Johnson sequencing algorithm, OptiDJS+ builds upon it to suit the demands of modern cloud infrastructures. OptiDJS+ makes use of sophisticated optimization algorithms, heuristic approaches, and adaptive mechanisms to improve resource allocation, workload distribution, and task scheduling. To obtain the best performance, this strategy uses historical data, dynamic resource reconfiguration, and adaptation to changing workloads. It accomplishes this by utilizing real-time monitoring and machine learning. It takes factors like load balance and make-up into account. We outline the design philosophies, implementation specifics, and empirical assessments of OptiDJS+ in this work. Through rigorous testing and benchmarking against cutting-edge scheduling algorithms, we show the better performance and resilience of OptiDJS+ in terms of reaction times, resource utilization, and scalability. The outcomes underline its success in reducing resource contention and raising service quality generally in cloud computing environments. In contexts where there is distributed overloading, OptiDJS+ offers a significant advancement in the search for effective resource scheduling solutions. Its versatility, optimization skills, and improved decision-making procedures make it a viable tool for tackling the resource allocation issues that cloud service providers and consumers encounter daily. We think that OptiDJS+ opens the way for more dependable and effective cloud computing ecosystems, assisting in the full realization of cloud technologies’ promises across a range of application areas. In order to use the OptiDJS+ Johnson sequencing algorithm for cloud computing task scheduling, we provide a two-step procedure. After examining the links between the jobs, we generate a Gantt chart. The Gantt chart graph is then changed into a two-machine OptiDJS+ Johnson sequencing problem by assigning tasks to servers. The OptiDJS+ dynamic Johnson sequencing approach is then used to minimize the time span and find the best sequence of operations on each server. Through extensive simulations and testing, we evaluate the performance of our proposed OptiDJS+ dynamic Johnson sequencing approach with two servers to that of current scheduling techniques. The results demonstrate that our technique greatly improves performance in terms of makespan reduction and resource utilization. The recommended approach also demonstrates its ability to scale and is effective at resolving challenging work scheduling problems in cloud computing environments.
Amit Upadhyay, Sandip Kulkarni, Sharmistha Roy, K. Yuvaraj, V. Ashok, and Sunil D. Kale
IEEE
comfy Shell (SSH) is an effective community protocol used to securely connect devices over a computer community. It gives information encryption for relaxed conversation among clients and servers, and can be used to guard information at the same time as being transmitted over a wi-fi community. But, as with every protocol, there are positive obstacles that should be considered when the usage of SSH to relaxed wi-fi networks. Because of the character of wireless networks, in which records is transmitted through the air, SSH is at risk of numerous vulnerabilities. First, SSH is susceptible to man-in-the- center attacks, which can permit an attacker to intercept the network traffic and examine, alter, or block positive packets. Specially, using SSH makes it viable for an attacker to perform password sniffing, for the reason that protocol does not encrypt the user's login credentials. Additionally, the usage of SSH protocols does now not shield against malware or different malicious software, making it feasible for an attacker to monitor or manipulate an affected system.
Pallab Banerjee, Sharmistha Roy, Anurag Sinha, Md. Mehedi Hassan, Shrikant Burje, Anupam Agrawal, Anupam Kumar Bairagi, Samah Alshathri, and Walid El-Shafai
Institute of Electrical and Electronics Engineers (IEEE)
Cloud computing has revolutionized the management and analysis of data for organizations, offering scalability, flexibility, and cost-effectiveness. Effective task scheduling in cloud systems is crucial to optimize resource utilization and ensure timely job completion. This research presents a novel method for job scheduling in cloud computing, employing the Johnson Sequencing algorithm across three servers. Originally developed for scheduling tasks in a manufacturing context, the Johnson Sequencing method has proven successful in resolving task scheduling challenges. Here, we adapt this method to address job scheduling among three servers within a cloud computing environment. The primary objective of the algorithm is to minimize the makespan, representing the total time required to complete all tasks. This study considers a scenario where a diverse set of jobs, each with varying processing durations, needs to be distributed across three servers using the Johnson Sequencing method. The algorithm strategically determines the optimal order for task execution on each server while accounting for job interdependencies and processing times on the individual servers. To put the Johnson Sequencing algorithm into practice for cloud computing job scheduling, we propose a three-step approach. First, we construct a precedence graph by analyzing the relationships among jobs. Subsequently, the precedence graph is transformed into a two-machine Johnson Sequencing problem by allocating jobs to servers. Finally, we employ the Dynamic Heuristic Johnson Sequencing method to determine the best order of jobs on each server, effectively minimizing the makespan. Through comprehensive simulations and testing, we compare the performance of our suggested Dynamic Heuristic Johnson Sequencing technique with existing scheduling algorithms. The results demonstrate significant improvements in terms of makespan reduction and resource utilization when employing our proposed method with three servers. Furthermore, our approach exhibits remarkable scalability and effectiveness in resolving complex job scheduling challenges within cloud computing settings. The outcomes of this research contribute to the optimization of resource allocation and task management in cloud systems, offering potential benefits to a wide range of industries and applications.
Biresh Kumar, Sharmistha Roy, Anurag Sinha, Celestine Iwendi, and Ľubomíra Strážovská
MDPI AG
The overall effectiveness of a website as an e-commerce platform is influenced by how usable it is. This study aimed to find out if advanced web metrics, derived from Google Analytics software, could be used to evaluate the overall usability of e-commerce sites and identify potential usability issues. It is simple to gather web indicators, but processing and interpretation take time. This data is produced through several digital channels, including mobile. Big data has proven to be very helpful in a variety of online platforms, including social networking and e-commerce websites, etc. The sheer amount of data that needs to be processed and assessed to be useful is one of the main issues with e-commerce today as a result of the digital revolution. Additionally, on social media a crucial growth strategy for e-commerce is the usage of BDA capabilities as a guideline to boost sales and draw clients for suppliers. In this paper, we have used the KMP algorithm-based multivariate pruning method for web-based web index searching and different web analytics algorithm with machine learning classifiers to achieve patterns from transactional data gathered from e-commerce websites. Moreover, through the use of log-based transactional data, the research presented in this paper suggests a new machine learning-based evaluation method for evaluating the usability of e-commerce websites. To identify the underlying relationship between the overall usability of the eLearning system and its predictor factors, three machine learning techniques and multiple linear regressions are used to create prediction models. This strategy will lead the e-commerce industry to an economically profitable stage. This capability can assist a vendor in keeping track of customers and items they have viewed, as well as categorizing how customers use their e-commerce emporium so the vendor can cater to their specific needs. It has been proposed that machine learning models, by offering trustworthy prognoses, can aid in excellent usability. Such models might be incorporated into an online prognostic calculator or tool to help with treatment selection and possibly increase visibility. However, none of these models have been recommended for use in reusability because of concerns about the deployment of machine learning in e-commerce and technical issues. One problem with machine learning science that needs to be solved is explainability. For instance, let us say B is 10 and all the people in our population are even. The hash function’s behavior is not random since only buckets 0, 2, 4, 6, and 8 can be the value of h(x). However, if B = 11, we would find that 1/11th of the even integers is transmitted to each of the 11 buckets. The hash function would work well in this situation.
Kamaldeep Gupta and Sharmistha Roy
Springer Nature Singapore
Kamaldeep Gupta, Sharmistha Roy, Ayman Altameem, Raghvendra Kumar, Abdul Khader Jilani Saudagar, and Ramesh Chandra Poonia
MDPI AG
The rapid growth of mHealth applications for Type 2 Diabetes Mellitus (T2DM) patients’ self-management has motivated the evaluation of these applications from both the usability and user point of view. The objective of this study was to identify mHealth applications that focus on T2DM from the Android store and rate them from the usability perspective using the MARS tool. Additionally, a classification of these mHealth applications was conducted using the ID3 algorithm to identify the most preferred application. The usability of the applications was assessed by two experts using MARS. A total of 11 mHealth applications were identified from the initial search, which fulfilled our inclusion criteria. The usability of the applications was rated using the MARS scale, from 1 (inadequate) to 5 (excellent). The Functionality (3.23) and Aesthetics (3.22) attributes had the highest score, whereas Information (3.1) had the lowest score. Among the 11 applications, “mySugr” had the highest average MARS score for both Application Quality (4.1/5) as well as Application Subjective Quality (4.5/5). Moreover, from the classification conducted using the ID3 algorithm, it was observed that 6 out of 11 mHealth applications were preferred for the self-management of T2DM.
Kamaldeep Gupta, Sharmistha Roy, Ramesh Chandra Poonia, Raghvendra Kumar, Soumya Ranjan Nayak, Ayman Altameem, and Abdul Khader Jilani Saudagar
MDPI AG
People use mHealth applications to help manage and keep track of their health conditions more effectively. With the increase of mHealth applications, it has become more difficult to choose the best applications that are user-friendly and provide user satisfaction. The best techniques for any decision-making challenge are multi-criteria decision-making (MCDM) methodologies. However, traditional MCDM methods cannot provide accurate results in complex situations. Currently, researchers are focusing on the use of hybrid MCDM methods to provide accurate decisions for complex problems. Thus, the authors in this paper proposed two hybrid MCDM methods, CODAS-FAHP and MOORA-FAHP, to assess the usability of the five most familiar mHealth applications that focus on type 2 diabetes mellitus (T2DM), based on ten criteria. The fuzzy Analytic Hierarchy Process (FAHP) is applied for efficient weight estimation by removing the vagueness and ambiguity of expert judgment. The CODAS and MOORA MCDM methods are used to rank the mHealth applications, depending on the usability parameter, and to select the best application. The resulting analysis shows that the ranking from both hybrid models is sufficiently consistent. To assess the proposed framework’s stability and validity, a sensitivity analysis was performed. It showed that the result is consistent with the proposed hybrid model.
Dipan Das, Sharmistha Roy, Kamaldeep Gupta, and Bibhudatta Sahoo
IEEE
Data and information are found to be stored, processed or transferred with the support of modern technological tools and hardware specifications. Due to the increase in the usage of digital communication and higher dependency on the internet technology platform, the risk factor also increased. Thus, it is essential to use safety and precautionary measures while storing and transferring data in order to prevent unauthorized access. One of the way of achieving this is through confidentiality, which uses various encryption algorithms to convert a readable message (plain text) into unreadable form (cipher text). Cryptographic algorithms are of two types- symmetric encryption algorithms and asymmetric encryption algorithms. This paper focused on few symmetric encryption algorithms namely 3DES, AES, Blowfish and IDEA. The objective of this work is to evaluate the performance of these encryption algorithms on the basis of three criteria- time, resource and privacy. The evaluation is done using MCDM methods such as PROMETHEE II, COPRAS and ARAS, which determine the ranking of these encryption algorithms based on the score obtained. The simulation has been performed in C/C++ compiler and the result analysis shows that Blowfish algorithm is considered as the best symmetric encryption algorithm in comparison to the others for COPRAS and ARAS methods whereas, IDEA is the best ranked in PROMETHEE II method.
Kamaldeep Kamaldeep, Sharmistha Roy, Ramesh Chandra Poonia, Soumya Ranjan Nayak, Raghvendra Kumar, Khalid J. Alzahrani, Mrim M. Alnfiai, and Fahd N. Al-Wesabi
MDPI AG
The recent developments in the IT world have brought several changes in the medical industry. This research work focuses on few mHealth applications that work on the management of type 2 diabetes mellitus (T2DM) by the patients on their own. Looking into the present doctor-to-patient ratio in our country (1:1700 as per a Times of India report in 2021), it is very essential to develop self-management mHealth applications. Thus, there is a need to ensure simple and user-friendly mHealth applications to improve customer satisfaction. The goal of this study is to assess and appraise the usability and effectiveness of existing T2DM-focused mHealth applications. TOPSIS, VIKOR, and PROMETHEE II are three multi-criteria decision-making (MCDM) approaches considered in the proposed work for the evaluation of the usability of five existing T2DM mHealth applications, which include Glucose Buddy, mySugr, Diabetes: M, Blood Glucose Tracker, and OneTouch Reveal. The methodology used in the research work is a questionnaire-based evaluation that focuses on certain attributes and sub-attributes, identified based on the features of mHealth applications. CRITIC methodology is used for obtaining the attribute weights, which give the priority of the attributes. The resulting analysis signifies our proposed research by ranking the mHealth applications based on usability and customer satisfaction.
Pallab Banerjee and Sharmistha Roy
IEEE
The ever increasing demand for computing in every field of the modern era leads to the emergence of the cloud computing environment which has become a boon to the world of communication and the Internet. The huge pool of resources, on-demand services, and pay per use access are some of the features which made cloud computing one of the demanding platforms in this virtual world. Because of its huge number of user access and task requests, efficient resource management is a challenging issue. Thus scheduling tasks in an efficient manner to obtain maximum utilization of resources will help in improving performance and profit. In this research work, we have highlighted job allocation techniques namely heuristic and hybrid algorithms. A comparative analysis of various heuristic scheduling algorithms and hybrid scheduling algorithms have been carried out on two parameters namely makespan and time flow as they are the beneficial parameters for job scheduling. This research presents a systematic study of existing job allocation algorithms to show their advantages and limitations.
Naghma Khatoon, Prashant Pranav, Sharmistha Roy, and Amritanjali
Hindawi Limited
Different schemes have been proposed for increasing network lifetime in mobile ad hoc networks (MANETs) where nodes move uncertainly in any direction. Mobility awareness and energy efficiency are two inescapable optimization problems in such networks. Clustering is an important technique to improve scalability and network lifetime, as it relies on grouping mobile nodes into logical subgroups, called clusters, to facilitate network management. One of the challenging issues in this domain is to design a real-time routing protocol that efficiently prolongs the network lifetime in MANET. In this paper, a novel fuzzy-based Q-learning approach for mobility-aware energy-efficient clustering (FQMEC) is proposed that relies on deciding the behavioral pattern of the nodes based on their stability and residual energy. Also, Chebyshev’s inequality principle is applied to get node connectivity for load balancing by taking history from the monitoring phase to increase the learning accuracy. Extensive simulations are performed using the NS-2 network simulator, and the proposed scheme is compared with reinforcement learning (RL). The obtained results show the effectiveness of the proposed protocol regarding network lifetime, packet delivery ratio, average end-to-end delay, and energy consumption.
Sharmistha Roy, Prashant Pranav, and Vandana Bhattacharjee
Springer International Publishing
Internet of Things (IoT) is an emerging area of research and has attracted many researchers because of its potential use in diversified fields of social life viz. healthcare, agriculture, home and industrial automation, etc. Internet of Things is a connection of devices having sensors for collection of data from physical world, sharing information through reliable communication channel for making devices act smartly in real life system. With the increase in the number of IoT devices there is a proportional growth of data which leads to numerous security and privacy risks. Thus, assuring security features and protection mechanism is of high concern and talk of the scientific world. This paper aims to present state-of-the-art research relating to various IoT features, its architecture, security features and mechanisms, the security threats that have emerged from IoT, different mechanisms to provide a secure working environment to an IoT system, the research work in progress in the domains of security and challenges that are faced in IoT systems. Since, healthcare system is a very sensitive field, thus failure of IoT applications in medical field is of utmost importance and highly significant. Thus, this paper aims to address security issues, requirements and various secure mechanisms that can be adopted to provide a secure healthcare system.
Prashant Pranav, Naela Rizvi, Naghma Khatoon, and Sharmistha Roy
Springer Singapore
Traditional IT infrastructures are gradually becoming obsolete and an alternative way to store, manipulate, and retrieve data. Namely, cloud computing is gaining momentum to replace the traditional computing environment. Sharing of resources over a distributed network is the main motive of cloud computing in order to provide consistency and reliability of the shared resources while keeping a check on the monetary factor involved. The resources available in cloud can not only be shared by multiple users, but are also be facilitated to reallocate with every demand. So, there has been always a focus on best techniques to provision the available resources in the cloud. Cloud resource provisioning mechanisms must follow some service-level agreements (SLAs) in order to abide by customers demand properly. This paper focuses on various research works undertaken on cloud computing resource provisioning techniques by taking SLA into account.
Naghma Khatoon, Sharmistha Roy, and Prashant Pranav
Springer International Publishing
Internet of Things (IoT) in healthcare is a revolution in patient’s care with improved diagnosis, real examining and preventive as well as real treatments. The IoT in healthcare consists of sensors enabled smart devices that accurately gather data for further analysis and actions. By using real time data, the devices permit monitoring, tracking and management in order to enhance healthcare. Due to the efficiency of IoT, the information gathered imparts improved judgment and decreases risks of committing mistakes. IoT can also be employed for preventing machines failures which are pros as this can enhance the reliability and quality when it comes to the patient’s supply chain responsiveness. This paper presents a relative survey of applications of IoT in healthcare system.
Satarupa Mohanty, Suneeta Mohanty, and Sharmistha Roy
IEEE
The detection of motifs, the meaningful patterns from computational biological data has been studied considerably by virtue of its predominant significance in the biological subjects like gene function, drug design, human disease etc. Many different approaches have been experimented and explored by the researchers towards the development of motif discovery algorithms and tools and the progress achieved is very encouraging. This paper is an attempt towards exploring the effectiveness of exact approach from the domain of planted ($l$, d) motif search.
Sharmistha Roy, Suneeta Mohanty, and Satarupa Mohanty
IEEE
In this competitive market, productivity and success story of automobile industry mostly depends on the car models and their acceptance by the customers. Nowadays, new car models are introduced in the market at an increasing rate with new technological advancement by discarding and outdating the old models. However., customer satisfaction is very essential for improving the growth of any automobile industry. Thus., automobile industry should know the consumer demands and needs, which directly or indirectly relate to the improvement of the car models. This paper proposes an efficient hybrid model which will help the customers for selection of best car models. The hybrid model constitutes Fuzzy AHP and PROMETHEE II techniques as both are best MCDM techniques for solving decision problem efficiently and economically.
Sharmistha Roy, Suneeta Mohanty, and Sataruna Mohanty
IEEE
In this competitive market, productivity and success story of automobile industry mostly depends on the car models and their acceptance by the customers. Nowadays, new car models are introduced in the market at an increasing rate with new technological advancement by discarding and outdating the old models. However, customer satisfaction is very essential for improving the growth of any automobile industry. Thus, automobile industry should know the consumer demands and needs, which directly or indirectly relate to the improvement of the car models. This paper proposes an efficient hybrid model which will help the customers for selection of best car models. The hybrid model constitutes Fuzzy AHP and PROMETHEE II techniques as both are best MCDM techniques for solving decision problem efficiently and economically.
Sharmistha Roy, Prasant Kumar Pattnaik, and Rajib Mall
Springer Science and Business Media LLC
Considering the competitive business scenario of the IT world, websites are the key aspect for satisfying the user’s demands. Thus, usability evaluation is the hidden measure to succeed in this competitive environment. Measuring user satisfaction is a hindering job because of lack of appropriate methodologies. This paper addresses these issues by considering AHP based usability evaluation technique to measure the usability score of a website. The proposed methodology evaluates user satisfaction for three different websites based on feedback mechanism for different usability attributes. Feedback is collected using Questionnaire methodology. AHP is a multi-criteria decision making algorithm that proves to be the reliable way for the user to make a decision for choosing the best website that fulfills user satisfaction.
Sharmistha Roy, Prasant Kumar Pattnaik, and Rajib Mall
Institute of Advanced Engineering and Science
Cloud computing is a style of computing which thrives users requirements by delivering scalable, on-demand and pay-per-use IT services. It offers different service models, out of which Storage as a Service (StaaS) is the fundamental block of Infrastructure cloud that fulfills user’s excess demand of elastic computing resources. But considering the competitive business scenario choosing the best cloud storage provider is a difficult task. Thus, usability is considered to be the key performance indicator which evaluates the better cloud storage based on user’s satisfaction. This paper aims to focus on the usability evaluation of StaaS providers namely Google drive, Drop box and One drive. This paper proposed a fuzzy based AHP model for measuring user satisfaction. Usability evaluation is carried out based on user feedback through Interview and Questionnaire method. Analysis of user feedback is done based on the fuzzy approach in order to remove vaguness. Whereas, AHP model is used for measuring satisfaction degree of the different cloud storage services and it solves the problem of selecting best cloud storage.