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Grasserie Disease Identification in Bombyx Mori Silkworm using Ensemble Learning Approach
Full text linkSericulture is an agricultural activity that involves rearing of silkworms for the production of cocoons which is in turn used to produce raw silk. In countries like India where agriculture is pre-dominant, sericulture is considered to be one of the most important economic activities. India ranks second among the silk producing countries in the world, accounting for over 17 percent of the world’s production. The major activities of sericulture comprise of food-plant cultivation to feed the silkworms, spin silk cocoons and reel them for unwinding the silk filament for processing and weaving to produce valuable silk products. Though technology has been a boon to the agricultural sector, there is not much implementation of technological methods in disease detection in silkworms. But diseases in silkworms pose a major threat and causes a huge economic loss to farmers which in turn necessitates early identification of diseases and this is quite an arduous process. Identification and detection of diseases at an earlier stage would be helpful for a farmer to take essential precautionary measures to avoid spreading of diseases. With the advancement in technology, a variety of methods have been developed to address this issue. In this paper, different machine learning algorithms are compared for their accuracy and the best ensemble learning algorithm is adopted which can be further implemented on a hardware model for real-time applications. The developed algorithm aids the machine in decision making and hence identifies grasserie disease in Bombyx Mori silkworm
Dr. Pradyot Ranjan Jena
No full textDr. Pradyot Ranjan Jena is Associate professor at School of Management, National Institute of Technology Karnataka (NITK). After obtaining Ph.D. from IIT Kanpur in 2007, he joined Leibniz University Hannover as senior research Fellow and then worked as Development Economist at CIMMYT, Nairobi. Dr. Jena’s core research areas are impact evaluation, food security and climate change. He led 10 national and international research projects and published 60 research papers in peer reviewed journals and conference Proceedings. He won BEST ADVISER AWARD by Northeastern Agricultural and Resource Economics Association in 2016. He is associate editor of Frontiers in Sustainable Food Systems and Frontiers in Climate Change.https://www.interscience.in/mentors/1106/thumbnail.jp
Empirical Analysis of Machine Learning algorithms in Fake News detection
Full text linkSocial media is the finest venue for thinking and expressing in the modern world. And this is the best place to share information about your identity, culture, religion, and customs. It entails an immediate information interchange that covers news from every industry. These days, social media has a big impact on how we live and how society functions. Currently, social media is the best medium for expressing your thoughts. Social media has also evolved into a channel for disseminating information about nearby events. how the locals in the other place are made aware of what is going on there. People benefit from this through learning about various cultures. However, some evil people use social media to spread their lies, which affects society and our everyday lives. Furthermore, fake news spreads like a forest fire if it is not dealt with promptly. And this bogus news offends certain individuals and occasionally sparks riots in public places. We need instruments in the modern day that can confirm any news, whether it is real or fraudulent. The current work considers a variety of machine-learning techniques for detecting false news, including Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM). The performance evaluation was then conducted using several criteria, including F-1 score, recall, accuracy, and precision. The empirical investigation shows DT has the greatest accuracy level at 100%
Security Life Cycle framework for Exploring & Prevention of Zero day attacks in Cyberterrorism
Full text linkThe rise of cyber terrorism poses a significant threat to governments, businesses, and individuals worldwide. Cyber terrorists use information technology to carry out attacks that range from simple hacking attempts to more sophisticated attacks involving malware, ransomware, and zero-day exploits. This paper aims to provide an in-depth understanding of cyber terrorism, with a special focus on zero-day attacks. As the world becomes more digitized and automated, it brings convenience to everyone\u27s lives. However, it also leads to growing concerns about security threats, including data leakage, website hacking, attacks, phishing, and zero-day attacks. These concerns are not only for organizations, businesses, and society, but also for governments worldwide. This paper aims to provide an introductory literature review on the basics of cyber-terrorism, focusing on zero-day attacks. The paper explores the economic and financial destruction caused by zero-day attacks and examines various types of zero-day attacks. It also looks at the steps taken by international organizations to address these issues and the recommendations they have made. Additionally, the paper examines the impact of these externalities on policymaking and society. As cyber-security becomes increasingly important for businesses and policymakers, the paper aims to delve deeper into this aspect, which has the potential to threaten national security, public life, and the economic and financial stability of developed, developing, and underdeveloped economies
Nonlinear Spectral Unmixing using Semi-Supervised Standard Fuzzy Clustering
Full text linkCoarse resolution captured in remote sensing causes the combination of different materials in one pixel, called the mixed pixel. Spectral unmixing estimates the combination of endmembers in mixed pixels and their corresponding abundance maps in the Hyper/Multi spectral image. In this paper, a nonlinear spectral unmixing based on semi-supervised fuzzy clustering is proposed. First, pure pixels (endmembers) using Vertex Component Analysis (VCA) are extracted and those pixels are the labelled pixels where the membership value of each is 1 for the corresponding endmember and 0 for the others. Second, the semi-supervised fuzzy clustering is applied to find the membership matrix defining the fraction of the endmember in each mixed pixel and hence extract the abundance maps. The experiments were conducted on both synthetic data such as the Legendre data and real data such as Jasper Ridge data. The non-linearity of the Legendre data was performed by the Fan model on different signal-tonoise ratio values. The results of the new unmixing model show its significant performance when compared with four state-of the art unmixing algorithm
Dr. Naganna Chetty
No full textDr. Naganna Chetty is academically associated with the Department of Information Science and Engineering at A. J. Institute of Engineering & Technology, Mangaluru, India. He obtained a Ph.D. from the National Institute of Technology Karnataka, Surathkal. Some of his contributions are to social media content analysis and regulations. His research areas are information systems, data mining, machine learning, e-governance, social media content analysis, and regulation of toxic content in the cyber world. He published his research work in more than 20 international journals and conferences. Currently, he is working on machine learning for online hate content regulation. He is a recipient of the best paper award for his research work.https://www.interscience.in/mentors/1112/thumbnail.jp
Optimization of Acrylonitrile Butadiene Styrene Filament 3D Printing Process Parameters based on Mechanical Test
Full text linkThis research paper\u27s main goal is to improve the printing parameters that can be used in the 3D Printing Material Extrusion production method in order to get the best printing parameters for Acrylonitrile Butadiene Styrene (ABS) filament with the tensile test in the shortest possible time. The printing parameters that can be employed on 3D printing material extrusion machines include the extruder temperature, layer height, printing speed, and shell count. Also, tensile specimens in accordance with the ASTM (American Society for Testing and Materials) D638 standard were created utilizing ABS filament and the aforementioned adjusted printing settings. The most effective printing settings for ABS products were established using the production time and the results of a post-production tensile test. As a result, this research can be used to determine the ideal ABS filament printing parameters and their timing
Dr. Chandreie Mukherjee
No full textDr. Chandreie Mukherjee has obtained her Ph.D. in English and Comparative Literature from Pondicherry University. She was awarded the university gold medal during her Masters’s from the same University. Before joining IIMV, she was working with the Vellore Institute of Technology, Andhra Pradesh, as an Assistant Professor in the Department of Languages. Her research interests lie in the domain of Gender Studies, Business Communication, Indian Writing in English, English Language Teaching, Comparative Literature, Translation Studies, etc. She taught courses in English proficiency both for business school and engineering students. As a faculty, she has four years of experience and has taught courses on Strategic Communication, Corporate Communication, and English Proficiency Courses for undergraduate and postgraduate students. She also conducted several training sessions for PSUs and Corporates including HSL, HPCL, IOCL, EISAI, NTPC, L&T, NSTL-DRDO, DPE, MCL and CBSE School Principals.https://www.interscience.in/mentors/1116/thumbnail.jp
Key Perspectives in Power Aware Ad-hoc Internet of Things with Advanced Networks and Real Time Scenarios
Full text linkSmart gadgets with integrated power optimization segments are the key perspectives that use Internet of Things (IoT) enabled technology to promote lifestyle advancements. It has an influence on a number of sectors in academia and/or business thanks to its strong integration with the current Cloud architecture. Recently, the Internet of Things has been acknowledged as a disruptive technology for the aerial ad hoc network. IoT may be thought of as a network inside a network. IoT-based networks rely heavily on the so-called self-organizing capability working in a dispersed manner in ad hoc networks, with users travelling at speeds ranging from walking pace to automobile, rail, or airline speed. IoT applications that assist logistics and the administration of ad hoc networks may be found in a broad variety. Utility companies are under pressure now to produce ever more enormous amounts of electricity. In megacities, there is an exponential rise in the number of people and energy users. Thus, the need for energy conservation is growing significantly on a global scale. The best way to optimise the rising energy demands and consumptions is as a consequence of the development of energy-monitoring systems. These solutions can cut current utilisation levels, stop energy waste, and make better use of our resources
A Case Study on Solutions of Linear Fractional Programming Problems
Full text linkIn some decision making problems, objective function can be defined as the ratio of two linear functional subjects to given constraints. These types of problems are known as linear fractional programming problems. The importance of linear fractional programming problems comes from the fact that many real life problems can be expressed as the ratio of physical or economical values represented by linear functions, for example traffic planning, game theory and production planning etc. In this article, correspond to a production planning problem the mathematical model developed, is a linear fractional programming and in order to solve it, various fractional programming techniques has been used. Finally result is compared with the solution obtained by graphical method. To illustrate the efficiency of stated method a numerical example has given