Dr. E Murali
@sistk.org
Associate Professor/ Computer Science and Engineering
Siddartha Institute of Science and Technology
Scopus Publications
Scholar Citations
Scholar h-index
Scopus Publications
K. Sudharson, K. R. Mohan Raj, K. Selvi, A. Suresh Kumar, Dhakshunhaamoorthiy, and E. Murali
IEEE
This research pioneers SmartTask, an innovative paradigm for intelligent task scheduling in project management, leveraging the novel technique of Reinforcement Learning with Proximal Policy Optimization (PPO). Departing from traditional methods, SmartTask utilizes PPO to dynamically adapt to evolving project requirements, resulting in a significant 30% improvement in task scheduling accuracy. Through meticulous analysis of historical project data using PPO, the system achieves an impressive 25% increase in overall project efficiency, surpassing existing techniques. The implementation of SmartTask with PPO leads to a substantial reduction in scheduling errors, marking a distinct departure from conventional methods. The technology’s exceptional proficiency in resource utilization minimizes idle time and enhances task completion rates. In extensive testing, SmartTask demonstrates unprecedented precision in task scheduling with an outstanding 92% accuracy, establishing a new benchmark for efficiency in project management. This research positions SmartTask as a technological breakthrough and underscores its novelty with the introduction of PPO, reshaping intelligent task scheduling in project management.
Azween Abdullah, E. Murali, Sreeji S, Balamurugan Balusamy, and S. Rajashree
Auricle Technologies, Pvt., Ltd.
Farming is regarded as a major industry in India, accounting for 17% of the country's GDP growth. Agriculture employs 60% of the population hence it is considered an important sector in India. The important factors for agriculture are pest management, disease prevention, irrigation management, soil mineral composition, crop management, location, and the season in which the crop is grown. Hence all this information along with the techniques are well known only by the experienced farmers. Hence it is important to create an agro knowledge management system. As a result, this work makes an attempt to develop a multiple ontology-based agro knowledge management system. The designed system consists of agriculture information related to attributes of soil mineral, moisture, season, location, crop type, and temperature. It consists of multiple ontologies such as soil ontology, crop ontology, location ontology, and crop season ontology to provide agronomy knowledge. Soil ontology is premeditated to classify the soil type in a hierarchical order while crop ontology classifies the crop type, location ontology classifies locations suitable for different crop types and finally, crop season ontology classifies the season that is suitable for different crops. A rule base is built to develop the knowledge base and to validate the truthfulness of the knowledge base. Visualization of a knowledge base is carried out for better understanding and decision-making.
A. Jemshia Mirriam, S. Rajashree, M. Nafees Muneera, V. Saranya, and E. Murali
Springer Nature Switzerland
E. Murali and S. Margret Anouncia
Informa UK Limited
E. Murali and S. Margret Anouncia
World Scientific Pub Co Pte Ltd
Agriculture is an important sector which contributes to 17% of the total GDP of the Indian economy. Soil, crop type, location and season play a major role in agriculture. Quality seed, water, soil, chemical composition, disease prevention are important parameters in Quality crops. Growth in agriculture and modern techniques has given out a new dimension to modern agriculture processes which differ from traditional agriculture. This in turn reduces the workload of farmers and increases productivity. Experienced farmers have great knowledge about farming techniques, crop selection, disease prevention, soil composition and crop management techniques and their composition. Due to less productivity, water, labor and pest management knowledge transfer is not done to the next generation. This system attempts to provide a visualization of knowledge management systems. Data visualization is one of the modern techniques for data representation. Agriculture yield, crop selection, soil composition can be represented in a visualization technique which will help the farmers for better understanding than representing the data in table or text. In this paper, a visualization of an agro knowledge mining approach which extracts knowledge from multiple ontology is proposed.
K. Anguraju, R. Krishna Prakash, V. Sowjanya, P. Ranjith, E. Murali, and P. Ashok
IEEE
The cryptography and steganography can be employed as an appropriate tool for improving the confidentiality of transmissions over the cloud environment. Several of the prevailing steganographic systems are based on the public key steganographic systems such as RSA and ECC where the privacy is based on the complexities in addressing the numerical factorization issues and distinct log-based issues. Moreover, the schemes for addressing the numerical factorization issues and distinct log-based issues enhances statistically. Hence the presence of quantum computing within the extent of 1000 bits can be real-world risks to the system with these issues. The goal is to propose a fresh Quadratic Polynomial Based Post Quantum steganography system that aggregates the post-quantum cryptography with steganography for assuring safety during cloud communication that will be preserved both for traditional computing and post-quantum computing age.
S. Rajashree, A. Jemshia Miriam, Nafees Muneera, V. Saranya, and E. Murali
IEEE
The development of Internet has brought a lot of conveniences to people and facilitated people's life greatly. However, with the gradual development of network, a variety of computer viruses such as Trojan virus are threating our network security and our use of accounts. Any careless move will cause the loss of the account or even our properties, which sounded the alarm on network security. In previous network security defence practices, simple firewall was used as an important defence method against virus. However, in recent virus attacks, it is found that simple firewall can no longer meet the demands of defence against computer virus. Hence, the combination of firewall and IPS is surely an important method for future network security protection. Regarding the researches on firewall and IPS interaction technology, the research levels abroad are prior to that of domestic researches. Especially, Network ICE Company possesses a world-class research level in both intrusion detection system (IDS) and intrusion prevention system (IPS). Intrusion detection is the most common way of recognizing assaults on PC frameworks and making ready for interruptions, interruptions, and other PC related takes advantage of. Be that as it may, the development of Internet-based gadgets confounds the location interaction and requires computerized frameworks to recognize assaults. In light of this, this paper proposes a programmed interruption location strategy utilizing the Brainstorm-Crow Search-based Actor Critic Neural Network (BCS-ACNN) classifier. Information grouping bunches the information utilizing the proposed scanty fluffy C-implies (Probabilistic Sparse FCM) grouping calculation. The group is exposed to a two-level characterization performed utilizing the proposed streamlining calculation, and the second degree of order identifies information interruptions. The Brain Storm-Crow Search (BCS) calculation, which is a reconciliation of Brain Storm Optimization (BSO) and Crow Search Algorithm (CSA), ideally changes the loads of the Actor Critic Neural Network. Also, the probabilistic inadequate FCM calculation incorporates likelihood hypothesis into scanty FCM. Exploring different avenues regarding the technique proposed in the KDD-Cup dataset yields a precision of 0.7682, a genuine positive pace of 0.7984 (TPR), and a bogus positive pace of 0.4580 (FPR).
Murali E, Vignesh R, Deepa D, Priyanka N, Hemalatha S, and Rajashree S
IEEE
In India, 60.6% of the land has been used for agricultural purposes. Farmer invests much in chemical fertilizers, manual labor, weed collection, pesticide management, and at last, they gain low productivity. In this paper, different data mining techniques are going to be analyzed for improving crop productivity and the use of organic farming. Here nutrient content of different food products is also going to be analyzed by comparing organic and inorganic food products. Ill effects of Inorganic farming are listed. Food produced using organic farming will bring out the health and well-being of human beings for a better future. Seasonal crop recommendations will also be done by making a complete study on an attribute such as PH, water holding capacity, erosion, etc. Weather data are collected to bring out the prediction of rainfall in a particular region. The accuracy of different data mining algorithm are also listed
E. Murali and S.Margret Anouncia
IEEE
Precision agriculture is a modern agriculture implementation technique in which analysis of numerous source data takes place for decision-making and operation in the management of crop production. The data for precision agriculture are collected through robots, sensors, satellites, and drones. The two approaches of precision agriculture are the predictive approach which is used for representing the static indicator during the crop cycle whereas the control approach is an updating of information Ontology is a demonstration of concepts and their shared association. It can be used in a wide range of contexts, including the classification of agricultural information and the development of knowledge bases. The basic steps involved in precision farming are assessing variation, managing variability, and evaluation. The various tools used in precision farming are the internet of things (IoT), a global positioning system (GPS), geographic information system (GPS), remote sensor, proximate sensor technology, grid sampling, etc. With the increase in information technology in the field of agriculture. Consequently, data mining become much essential for decision-making. This paper attempts to emphasize the coherence of data mining approaches toward helping precision agriculture as a valuable venture.
Murali Elumalai and S. Margret Anouncia
Springer Science and Business Media LLC
E Murali and S Margret Anouncia
Diva Enterprises Private Limited