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20. Predictive Model for Agriculture Using Markov Model

Author

Gupta, Punit, Bharadwaj, Sumit, Singh, Arjun, Saini, Dinesh Kumar, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Singh, Pradeep Kumar, editor, Wierzchoń, Sławomir T., editor, Tanwar, Sudeep, editor, Rodrigues, Joel J. P. C., editor, and Ganzha, Maria, editor

Published
2023
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31. A Simulation Study of Social-Networking-Driven Smart Recommendations for Internet of Vehicles

Author

Zia, Kashif, Muhammad, Arshad, and Saini, Dinesh Kumar

Subjects
Computer Science - Social and Information Networks, Computer Science - Artificial Intelligence, Computer Science - Multiagent Systems
Abstract

Social aspects of connectivity and information dispersion are often ignored while weighing the potential of Internet of Things (IoT). In the specialized domain of Internet of Vehicles (IoV), Social IoV (SIoV) is introduced realization its importance. Assuming a more commonly acceptable standardization of Big Data generated by IoV, the social dimensions enabling its fruitful usage remains a challenge. In this paper, an agent-based model of information sharing between vehicles for context-aware recommendations is presented. The model adheres to social dimensions as that of human society. Some important hypotheses are tested under reasonable connectivity and data constraints. The simulation results reveal that closure of social ties and its timing impacts dispersion of novel information (necessary for a recommender system) substantially. It was also observed that as the network evolves as a result of incremental interactions, recommendations guaranteeing a fair distribution of vehicles across equally good competitors is not possible., Comment: A concise version of the paper appeared in the proceeding of the 16th International Conference on Practical Applications of Agents and Multi-Agent Systems 2018 held in Toledo, Spain

Published
2019

32. Nature-Inspired Computational Model of Population Desegregation under Group Leaders Influence

Author

Zia, Kashif, Saini, Dinesh Kumar, Muhammad, Arshad, and Ferscha, Alois

Subjects
Computer Science - Multiagent Systems, Computer Science - Social and Information Networks, 65Cxx
Abstract

This paper presents an agent-based model of population desegregation and provides a thorough analysis of the social behavior leading to it, namely, the contact hypothesis. Based on the parameters of frequency and intensity of influence of group leaders on the population, the proposed model is constituted by two layers: 1) a physical layer of the population that is influenced by and 2) a virtual layer of group leaders. The model of negotiation and survival of group leaders are governed by the nature-inspired evolutionary process of queen ants, also known as Foundress Dilemma. The motivation of using a virtual grouping concept (instead of taking a subset of population as the group leaders) is to stay focused on finding the conditions leading individuals in a society tolerating a significantly diversified (desegregated) neighborhood, rather than, indulging into complex details, which would be more relevant to studies targeting the evolution of societal group and leaders. A geographic information system-driven simulation is performed, which reveals that: 1) desegregation is directly proportional to the frequency of group leaders' contact with the population and 2) mostly, it remains ineffective with an increase in the intensity of group leaders' contact with the population. The mechanism of group selection (the conflict resolution model resolving the Foundress Dilemma) reveals an exciting result concerning negative influence of cooperative group leaders. Most of the time, desegregation decreases with increase in cooperative leaders (the leaders enforcing desegregation) when compared with fierce leaders (the leaders enforcing segregation)., Comment: 12 pages

Published
2019
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44. WheatQTLdb V2.0: a supplement to the database for wheat QTL

Author

Singh, Kalpana, Saini, Dinesh Kumar, Saripalli, Gautam, Batra, Ritu, Gautam, Tinku, Singh, Rakhi, Pal, Sunita, Kumar, Manoj, Jan, Irfat, Singh, Sahadev, Kumar, Anuj, Sharma, Hemant, Chaudhary, Jyoti, Kumar, Kuldeep, Kumar, Sourabh, Singh, Vikas Kumar, Singh, Vivudh Pratap, Kumar, Deepak, Sharma, Shiveta, Kumar, Sachin, Kumar, Rahul, Sharma, Shailendra, Gaurav, Shailendra Singh, Sharma, Pradeep Kumar, Balyan, Harindra Singh, and Gupta, Pushpendra Kumar

Published
2022
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45. Emerging Technologies for Pandemic and Its Impact

Author

Saini, Dinesh Kumar, Bala, Sandhiya, Sharma, Akhilesh Kumar, Zia, Kashif, Kacprzyk, Janusz, Series Editor, Jain, Lakhmi C., Series Editor, Kumar Bhoi, Akash, editor, Mallick, Pradeep Kumar, editor, Narayana Mohanty, Mihir, editor, and Albuquerque, Victor Hugo C. de, editor

Published
2021
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46. Exploring the genetic architecture of powdery mildew resistance in wheat through QTL meta-analysis.

Author

Sharma, Divya, Budhlakoti, Neeraj, Kumari, Anita, Saini, Dinesh Kumar, Sharma, Anshu, Yadav, Aakash, Mir, Reyazul Rouf, Singh, Amit Kumar, Vikas, V. K., Singh, Gyanendra Pratap, and Kumar, Sundeep

Subjects
POWDERY mildew diseases, WHEAT, FUNCTIONAL genomics, ERYSIPHE graminis, CONFIDENCE intervals
Abstract

Powdery mildew (PM), caused by Blumeria graminis f. sp. tritici, poses a significant threat to wheat production, necessitating the development of genetically resistant varieties for long-term control. Therefore, exploring genetic architecture of PM in wheat to uncover important genomic regions is an important area of wheat research. In recent years, the utilization of meta-QTL (MQTL) analysis has gained prominence as an essential tool for unraveling the complex genetic architecture underlying complex quantitative traits. The aim of this research was to conduct a QTL meta-analysis to pinpoint the specific genomic regions in wheat responsible for governing PM resistance. This study integrated 222 QTLs from 33 linkage-based studies using a consensus map with 54,672 markers. The analysis revealed 39 MQTLs, refined to 9 high-confidence MQTLs (hcMQTLs) with confidence intervals of 0.49 to 12.94 cM. The MQTLs had an average physical interval of 41.00 Mb, ranging from 0.000048 Mb to 380.71 Mb per MQTL. Importantly, 18 MQTLs co-localized with known resistance genes like Pm2, Pm3, Pm8, Pm21, Pm38, and Pm41. The study identified 256 gene models within hcMQTLs, providing potential targets for marker-assisted breeding and genomic prediction programs to enhance PM resistance. These MQTLs would serve as a foundation for fine mapping, gene isolation, and functional genomics studies, facilitating a deeper understanding of molecular mechanisms. The identification of candidate genes opens up exciting possibilities for the development of PM-resistant wheat varieties after validation. [ABSTRACT FROM AUTHOR]

Published
2024
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49. Smart City and Challenges

Author

Saini, Dinesh Kumar, Sandhiyaa, B. Y., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Tuba, Milan, editor, Akashe, Shyam, editor, and Joshi, Amit, editor

Published
2020
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