Mısra, Sanjay
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M.,Sanjay Misra, Sanjay Mısra,S. Mısra, Sanjay Misra,S. S.,Misra Sanjay, Mısra Sanjay, Misra S., Misra S.,Mısra M., Sanjay Misra, S.
Job Title
Profesör Doktor
Email Address
sanjay.misra@atilim.edu.tr
Main Affiliation
Computer Engineering
Status
Former Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
No research topics data found.
Sustainable Development Goals
1NO POVERTY
3
Research Products
2ZERO HUNGER
5
Research Products
3GOOD HEALTH AND WELL-BEING
2
Research Products
4QUALITY EDUCATION
6
Research Products
5GENDER EQUALITY
2
Research Products
6CLEAN WATER AND SANITATION
1
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7AFFORDABLE AND CLEAN ENERGY
8
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8DECENT WORK AND ECONOMIC GROWTH
11
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9INDUSTRY, INNOVATION AND INFRASTRUCTURE
11
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10REDUCED INEQUALITIES
2
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11SUSTAINABLE CITIES AND COMMUNITIES
1
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12RESPONSIBLE CONSUMPTION AND PRODUCTION
3
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13CLIMATE ACTION
7
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14LIFE BELOW WATER
10
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15LIFE ON LAND
0
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16PEACE, JUSTICE AND STRONG INSTITUTIONS
4
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17PARTNERSHIPS FOR THE GOALS
9
Research Products
This researcher does not have a Scopus ID.
This researcher does not have a WoS ID.
No records found in other affiliations.
Scholarly Output
199
Articles
133
Views / Downloads
155/33
Supervised MSc Theses
3
Supervised PhD Theses
0
WoS Citation Count
2838
Scopus Citation Count
4174
Patents
0
Projects
0
WoS Citations per Publication
14.26
Scopus Citations per Publication
20.97
Open Access Source
53
Supervised Theses
3
| Journal | Count |
|---|---|
| Acta Polytechnica Hungarica | 12 |
| Tehnicki Vjesnik | 8 |
| Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -- 2011 International Conference on Computational Science and Its Applications, ICCSA 2011 -- 20 June 2011 through 23 June 2011 -- Santander -- 85480 | 5 |
| Journal of Physics: Conference Series -- 3rd International Conference on Computing and Applied Informatics 2018, ICCAI 2018 -- 18 September 2018 through 19 September 2018 -- Medan, Sumatera Utara -- 149865 | 4 |
| Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -- 12th International Conference on Computational Science and Its Applications, ICCSA 2012 -- 18 June 2012 through 21 June 2012 -- Salvador de Bahia -- 90945 | 4 |
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Scopus Quartile Distribution
Competency Cloud
199 results
Scholarly Output Search Results
Now showing 1 - 10 of 199
Conference Object Comparative Study of Cognitive Complexity Measures(Ieee, 2008) Misra, Sanjay; Mısra, Sanjay; Akman, Ibrahim; Akman, Kamil İbrahim; Mısra, Sanjay; Akman, Kamil İbrahim; Computer Engineering; Computer EngineeringComplexity metrics are used to predict critical information about reliability and maintainability of software systems. Cognitive complexity measure based on cognitive informatics, plays an important role in understanding the fundamental characteristics of software, therefore directly affects the understandability and maintainability of software systems. In this paper, we compared available cognitive complexity measures and evaluated cognitive weight complexity measure in terms of Weyuker's properties.Conference Object Embryo Spatial Model Reconstruction(Springer international Publishing Ag, 2020) Dirvanauskas, Darius; Maskeliunas, Rytis; Raudonis, Vidas; Misra, SanjayTime lapse microscopy offered new solutions to study embryo development process. It allows embryologist to monitor embryo growth in real time and evaluate them without interfering into their growth environment. Embryo evaluation during growth process is one of the key criteria in embryo selection for fertilization. Live embryo monitoring is time consuming and new tools are offered to automate part of process. Our proposed algorithm gives new possibilities for embryo monitoring. It uses embryo images which are taken from different embryo layers, extracts embryo cell features and returns metrical evaluation to compare different embryos. High number of extracted features shows embryo fragmentation. Other tool whichwe present is spatial embryo model. Features extracted from embryo layers are combined together to spatial model. It allows embryologist to examine embryo model and compare different layers in one space. The obtained spatial embryo model will be later used to develop new algorithms for embryo analysis tasks.Conference Object A Model for Measuring Cognitive Complexity of Software(Springer Verlag, 2008) Misra,S.; Akman,I.This paper proposes a model for calculating cognitive complexity of a code. This model considers all major factors responsible for (cognitive) complexity. The practical applicability of the measure is evaluated through experimentation, test cases and comparative study. © 2008 Springer-Verlag Berlin Heidelberg.Article Citation - WoS: 38Citation - Scopus: 60Career Abandonment Intentions among Software Workers(Wiley, 2012-05-09) Colomo-Palacios, Ricardo; Casado-Lumbreras, Cristina; Misra, Sanjay; Soto-Acosta, PedroWithin the software development industry, human resources have been recognized as one of the most decisive and scarce resources. Today, the retention of skilled IT (information technology) personnel is a major issue for employers and recruiters as well, since IT career abandonment is a common practice and means not only the loss of personnel, knowledge, and skills, but also the loss of business opportunities. This article seeks to discover the main motivations young practitioners abandon the software career. To achieve this objective, two studies were conducted. The first study was qualitative (performed through semistructured interviews) and intended to discover the main variables affecting software career abandonment. The second study was quantitative, consisting of a Web-based survey developed from the output of the first study and administered to a sample of 148 IT practitioners. Results show that work-related, psychological, and emotional variable are the most relevant group of variables explaining IT career abandonment. More specifically, the three most important variables that motivate employees to abandon the career are effort-reward imbalance, perceived workload, and emotional exhaustion. In contrast, variables such as politics and infighting, uncool work, and insufficient resources influence to a lesser extent the decision to leave the career. (c) 2012 Wiley Periodicals, Inc.Article Citation - WoS: 107Citation - Scopus: 168Cassava Disease Recognition From Low-Quality Images Using Enhanced Data Augmentation Model and Deep Learning(Wiley, 2021-06-14) Abayomi-Alli, Olusola Oluwakemi; Damasevicius, Robertas; Misra, Sanjay; Maskeliunas, RytisImprovement of deep learning algorithms in smart agriculture is important to support the early detection of plant diseases, thereby improving crop yields. Data acquisition for machine learning applications is an expensive task due to the requirements of expert knowledge and professional equipment. The usability of any application in a real-world setting is often limited by unskilled users and the limitations of devices used for acquiring images for classification. We aim to improve the accuracy of deep learning models on low-quality test images using data augmentation techniques for neural network training. We generate synthetic images with a modified colour value distribution to expand the trainable image colour space and to train the neural network to recognize important colour-based features, which are less sensitive to the deficiencies of low-quality images such as those affected by blurring or motion. This paper introduces a novel image colour histogram transformation technique for generating synthetic images for data augmentation in image classification tasks. The approach is based on the convolution of the Chebyshev orthogonal functions with the probability distribution functions of image colour histograms. To validate our proposed model, we used four methods (resolution down-sampling, Gaussian blurring, motion blur, and overexposure) for reducing image quality from the Cassava leaf disease dataset. The results based on the modified MobileNetV2 neural network showed a statistically significant improvement of cassava leaf disease recognition accuracy on lower-quality testing images when compared with the baseline network. The model can be easily deployed for recognizing and detecting cassava leaf diseases in lower quality images, which is a major factor in practical data acquisition.Article Citation - Scopus: 48Identifying Phishing Attacks in Communication Networks Using Url Consistency Features(Inderscience Publishers, 2020) Azeez,N.A.; Salaudeen,B.B.; Misra,S.; Damasevicius,R.; Maskeliunas,R.Phishing is a fraudulent attempt by cybercriminals, where the target audience is addressed by a text message, phone call or e-mail, requesting classified and sensitive information after presenting himself/herself as a legitimate agent. Successful phishing attack may result into financial loss and identity theft. Identifying forensic characteristics of phishing attack can help to detect the attack and its perpetuators and as well as to enable defence against it. To shield internet users from phishing assaults, numerous anti-phishing models have been proposed. Currently employed techniques to handle these challenges are not sufficient and capable enough. We aim at identifying phishing sites in order to guard internet users from being vulnerable to any form of phishing attacks by verifying the conceptual and literal consistency between the uniform resource locator (URL) and the web content. The implementation of the proposed PhishDetect method achieves an accuracy of 99.1%; indicating that it is effective in detecting various forms of phishing attacks. © 2020 Inderscience Enterprises Ltd.. All rights reserved.Article Citation - WoS: 31Citation - Scopus: 52Neural Network and Classification Approach in Identifying Customer Behavior in the Banking Sector: a Case Study of an International Bank(Wiley, 2012) Ogwueleka, Francisca Nonyelum; Misra, Sanjay; Colomo-Palacios, Ricardo; Fernandez, LuisThe customer relationship focus for banks is in development of main competencies and strategies of building strong profitable customer relationships through considering and managing the customer impression, influence on the culture of the bank, satisfactory treatment, and assessment of valued relationship building. Artificial neural networks (ANNs) are used after data segmentation and classification, where the designed model register records into two class sets, that is, the training and testing sets. ANN predicts new customer behavior from previously observed customer behavior after executing the process of learning from existing data. This article proposes an ANN model, which is developed using a six-step procedure. The back-propagation algorithm is used to train the ANN by adjusting its weights to minimize the difference between the current ANN output and the desired output. An evaluation process is conducted to determine whether the ANN has learned how to perform. The training process is halted periodically, and its performance is tested until an acceptable result is obtained. The principles underlying detection software are grounded in classical statistical decision theory.Conference Object Citation - Scopus: 3Conflict resolution via emerging technologies?(Institute of Physics Publishing, 2019-06-01) Yinka-Banjo,C.; Ugot,O.-A.; Misra,S.; Adewumi,A.; Damasevicius,R.; Maskeliunas,R.This paper presents a review of the current techniques and approaches adopted in conflict resolution in Multi-Agent Systems (MAS). The review highlights the strength and weaknesses, and thus, their success in fostering cooperation and collaboration in multi-agent systems. We survey alternative approaches to conflict resolution that rely on emerging technologies such as deep learning. From the survey, we discuss the benefits of using these emerging technologies in the conflict resolution process. © 2019 Published under licence by IOP Publishing Ltd.Article Citation - WoS: 20Citation - Scopus: 28Bug Severity Assessment in Cross Project Context and Identifying Training Candidates(World Scientific Publ Co Pte Ltd, 2017-03) Singh, V. B.; Misra, Sanjay; Sharma, MeeraThe automatic bug severity prediction will be useful in prioritising the development efforts, allocating resources and bug fixer. It needs historical data on which classifiers can be trained. In the absence of such historical data cross project prediction provides a good solution. In this paper, our objective is to automate the bug severity prediction by using a bug metric summary and to identify best training candidates in cross project context. The text mining technique has been used to extract the summary terms and trained the classifiers using these terms. About 63 training candidates have been designed by combining seven datasets of Eclipse projects to develop the severity prediction models. To deal with the imbalance bug data problem, we employed two approaches of ensemble by using two operators available in RapidMiner: Vote and Bagging. Results show that k-Nearest Neighbour (k-NN) performance is better than the Support Vector Machine (SVM) performance. Naive Bayes f-measure performance is poor, i.e. below 34.25%. In case of k-NN, developing training candidates by combining more than one training datasets helps in improving the performances (f-measure and accuracy). The two ensemble approaches have improved the f-measure performance up to 5% and 10% respectively for the severity levels having less number of bug reports in comparison of major severity level. We have further motivated the paper with a cross project bug severity prediction between Eclipse and Mozilla products. Results show that Mozilla products can be used to build reliable prediction models for Eclipse products and vice versa in case of SVM and k-NN classifiers.Conference Object Citation - WoS: 8Citation - Scopus: 10A Model for Measuring Cognitive Complexity of Software(Springer Verlag, 2008) Misra,S.; Akman,I.This paper proposes a model for calculating cognitive complexity of a code. This model considers all major factors responsible for (cognitive) complexity. The practical applicability of the measure is evaluated through experimentation, test cases and comparative study. © 2008 Springer-Verlag Berlin Heidelberg.