An analysis of online marketing strategies used by maggi during the 2015 crisis /
Online marketing has become an integral part of branding, advertising and marketing communication for any brand today. Communicating with the consumers at the time of crisis using the right marketing mix is essential and that is exactly what Maggi did during the crisis that the brand faced in 2015. Maggi had used online marketing strategies to reach out to consumers belonging to various sections of the society during the crisis.
Master of Arts in Media and Communication Studies
Filmic afterlives: Considerations on the uncanny
Indian Cinema Today and Tomorrow: Infrastructure, Aesthetics, Audiences, pp. 242-255.
Rajadhyaksha A.; Sawhney R., Christ University, Bangalore, India
A novel approach in prediction of crop production using recurrent cuckoo search optimization neural networks
Crop production; Data mining; Discrete DBN?VGG Net classifier; Recurrent cuckoo search optimization algorithm
Data mining is an information exploration methodology with fascinating and understand-able patterns and informative models for vast volumes of data. Agricultural productivity growth is the key to poverty alleviation. However, due to a lack of proper technical guidance in the agriculture field, crop yield differs over different years. Mining techniques were implemented in different applications, such as soil classification, rainfall prediction, and weather forecast, separately. It is proposed that an Artificial Intelligence system can combine the mined extracts of various factors such as soil, rainfall, and crop production to predict the market value to be developed. Smart analysis and a comprehensive prediction model in agriculture helps the farmer to yield the right crops at the right time. The main benefits of the proposed system are as follows: Yielding the right crop at the right time, balancing crop production, economy growth, and planning to reduce crop scarcity. Initially, the database is collected, and the input dataset is preprocessed. Feature selection is carried out followed by feature extraction techniques. The best features were then optimized using the recurrent cuckoo search optimization algorithm, then the optimized output can be given as an input for the process of classification. The classification process is conducted using the Discrete DBN? VGGNet classifier. The performance estimation is made to prove the effectiveness of the proposed scheme. 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Rajagopal A.; Jha S.; Khari M.; Ahmad S.; Alouffi B.; Alharbi A.
Applied Sciences (Switzerland), Vol-11, No. 21
https://doi.org/10.3390/app11219816 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117689718&doi=10.3390%2fapp11219816&partnerID=40&md5=47e62179a726d4af8bde18057f196412
Rajagopal A., Department of Computer Science and Business Systems, Sethu Institute of Technology, Virudhunagar, Kariapatti, 626115, India; Jha S., School of Sciences, Christ (Deemed to be University), NCR?New Delhi Campus, Ghaziabad, 201003, India; Khari M., School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, 110067, India; Ahmad S., Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, 11942, Saudi Arabia; Alouffi B., Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia; Alharbi A., Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia
Power and Area Efficient Decimation Filter Architectures of Wireless Receivers
Digital; FPGA; Hardware implementation; Low power design; Multi-rate design; Signal processing; Wireless communication
This paper reports on the synthesis and implementation of a digital decimation filter suitable for multi-standard transceivers. Decimation filter architectures used in transceivers must be capable of providing low power and less area. In this paper, three different architecture designs namely Decimation Filter with Conventional MAC Unit, Cascaded Multi-Standard decimation Chain and Hybrid structure are proposed to meet the demand of low power and area efficient digital decimation filter. The filter architectures are implemented using FPGA and its performances are tested. The architectures are tested using conventional number system and with two different encoding schemes of filter coefficients called canonic signed digit and minimum signed digit. The implementation results reflect that considerable reduction in area of 47.9% and power reduction of 28.6% are achieved using hybrid architecture, when compared with conventional MAC and cascaded chain architectures. 2016, The National Academy of Sciences, India.
Proceedings of the National Academy of Sciences India Section A - Physical Sciences, Vol-87, No. 1, pp. 83-96.
Rajagopal L., Department of Electronics & Communication Engineering, Christ University-Faculty of Engineering, Bangalore, 560074, India
Machine Learning Methods for Online Education Case
Education; etc.; Machine Learning; Online class
Online education has become a popular choice for learners of all ages and backgrounds due to its accessibility and flexibility. However, providing personalized learning experiences for a diverse range of students in online education can be challenging. Machine learning methods can be used to provide personalized learning experiences and improve student engagement in online education. In this case study, We're going to do some research on machine learning. methods in an online education platform. The platform provides courses in various subjects and is designed to be accessible to students from all over the world. The platform collects data on student behavior, such as the courses they enroll in, the time they spend on each course, and their performance on assignments and quizzes. We will explore several machine learning methods that can be applied to this data, including clustering, classification, and recommendation systems. Clustering algorithms can be used to group students based on their learning behavior and preferences, allowing instructors to provide personalized feedback and course recommendations. Classification algorithms can be used to predict student success in a particular course, allowing instructors to intervene and provide additional support if needed. Recommendation systems can be used to suggest courses to students based on their interests and past behavior. We will also discuss the potential benefits and challenges of using machine learning methods in online education. Benefits include increased student engagement, improved learning outcomes, and more efficient use of resources. Challenges include ensuring data privacy and security, preventing algorithmic bias, and maintaining transparency and fairness in the decision-making process. Overall, machine learning methods have the potential to transform online education by providing personalized learning experiences and improving student outcomes. By leveraging the vast amounts of data generated by online education platforms, we can create more effective and efficient learning experiences that meet the needs of students from diverse backgrounds and learning styles. 2023 IEEE.
Rajagopal M.; Ali B.; Sharon Priya S.; Aisha Banu W.; Madhavi M.G.; Punamkumar
Proceedings of 8th IEEE International Conference on Science, Technology, Engineering and Mathematics, ICONSTEM 2023
Institute of Electrical and Electronics Engineers Inc.
Rajagopal M., CHRIST(Deemed to Be University), Lean Operations and Systems, School of Business and Management, Bangalore, 560029, India; Ali B., Maulana Azad National Urdu University, College of Teacher Education, Hyderabad, 500032, India; Sharon Priya S., B.S.Abdur Rahman Crescent Institute of Science and Technology, Chennai, 600100, India; Aisha Banu W., B.S.Abdur Rahman Crescent Institute of Science and Technology, Chennai, 600100, India; Madhavi M.G., SreeVidyanikethan Engineering College, Department of Basic Sciences and Humanities, Tirupati, 517 502, India; Punamkumar, Christuniversity, Bangalore, 560029, India
Artificial Intelligence & Data Warehouse Regional Human Resource Management Decision Support System
Artificial intelligence (AI); Data warehouse; Decision support system; Regional human resource management
High-quality data is utilized to make informed decisions that effectively help to successfully safeguard our environment. When there is an abundance of information that is both heterogeneous in nature (coming from a wide variety of fields or sources) and of unknown quality, various problems may occur. Furthermore, the problem's dynamic nature also imposes some other complications. In order to deal with such complications, the central role played by supercomputers in the modern environment is to promote protection initiatives like monitoring, data analysis, communication, and information storage and retrieval. In current days, the higher dependency on the data management process forced the developers to integrate and enhance all these initiatives with Artificial Intelligence knowledge-based techniques so that smart systems can be utilized by a vast number of people. In this context, this study has illustrated how Artificial Intelligence methods have changed the nature of Environmental Decision Support Systems (EDSS) over the course of the last two decades. The strengths that an EDSS should exhibit have been emphasized in this review. In the final section, we look at some of the more innovative solutions used for various environmental issues. 2022 IEEE.
Rajagopal M.; Hinge P.; Srinivas K.; Palav M.R.; Balaji P.; Muda I.
Proceedings of 5th International Conference on Contemporary Computing and Informatics, IC3I 2022, pp. 2110-2113.
Rajagopal M., School of Business and Management, Christ Deemed to Be University, India; Hinge P., Christ University, India; Srinivas K., KL University, KLEF, India; Palav M.R., Global Business School & Research Centre Dr. D.Y.Patil Vidyapeeth, India; Balaji P., School of Commerce, Guru Nanak College, P.G & Research Department of Commerce, India; Muda I., Universitas Sumatera Utara, Indonesia
Optimal Management of Resources in Cloud Infrastructure through Energy Aware Collaborative Model
Decentralized EnergyAware Collaborative Model (DEACM); Destination based VM Migration Algorithm; Resource Allocation; Virtual Machine Selection
As the infrastructures of cloud computing provides paramount services to worldwide users, persistent applications are congregated using large scale data centres at the customer sides. For such wide platforms, virtualization technique has been incorporated for multiplexing the essential sources available. Due to the extensive application variations in the workloads, it is significant to handle the resource allocation methodologies of the virtual machines (VM) for assuring the Quality of Service (QoS) of cloud. On concentrating this, the paper proposed a Decentralized Energy-Aware Collaborative Model (DEACM) for effectively managing the data centres in cloud infrastructures. Initially, the optimal model for system management and power management are declared. Then, functions of workload vectors and data collection about workloads has been carried out for optimal selection of virtual machines to migrate for balancing loads efficiently. This can be further applied for Target-based VM Migration Algorithm for determining the migrating target for VM. Moreover, the algorithm involved in energy utilization with managed QoS. The developed DEACM is evaluated using CloudSim platform and the results are discussed. The results exemplify that the DEACM can balance the workload across variety of machines optimally and provide reduced energy consumption to the complete system efficiently. 2024 IEEE.
Rajagopal M.; Karuppasamy S.K.; Hemalatha S.; Sivasakthivel R.
Proceedings - 3rd International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2024
Rajagopal M., Christ (Deemed to Be University), School of Business and Management, Karnataka, Bengaluru, India; Karuppasamy S.K., Alliance University - Central Campus, Computer Science and Engineering, Alliance College of Engineering and Design, Anekal Main Road,Karnataka, Bangalore, India; Hemalatha S., Karpagam Academy of Higher Education, Department of Computer Science, Coimbatore, India; Sivasakthivel R., Christ (Deemed to Be University), School of Sciences, Department of Computer, Science, Karnataka, Bengaluru, India
A novel technique for leaf disease classification using Legion Kernels with parallel support vector machine (LK-PSVM) and fuzzy C means image segmentation
Image segmentation; Leaf disease; Machine learning; Particle swarm optimization and edge feature detection
Detection of plant disease and classificationare being investigated in many parts of the worldto save precious medical plants from becoming extinct.Major problem in this task, include the lack of advanced and technology driven solution. Manual identification is often time-consuming and prone to inaccuracies. Therefore, there is an urgent need for an automated and efficient method that can accurately identify and classify plant diseases. This article focuses on detecting the disease through classificationthrough a new technique using leaf images for automatic classification. This paper proposes a novel segmentation technique using Fuzzy C means and Particle Swarm Optimization for effective segmentation of leaf images and feature extraction that can help in classification of disease.The approach emphasizes on the integration of techniques such as image processing, segmentation and feature extraction and finally the classification, which offers a comprehensive solution for the disease detection. The work leverages on the advantages of Legion Kernels and Parallal support vector Machine (LK-PSVM) clubbed with fuzzy C means Image segmentation to offer a framework that can handle diverse leaf images and which can effectively differentiate the type of the disease.The proposed method LK-PSVM combined with Fuzzy C means presents a novel approach that is significantly deviated from the conventional methods of leaf disease classification.The proposed wok brings an integrated framework which can synergistically combine the Legion Kernels with the PSVM technique coupled with Fuzzy C Means Image segmentation which can handle the issue of overlapped data sets and support vector machines are used to handle the situation where the number of dimensions are more than the number of samples, which is more probable in the classification problem under consideration.By integrating these components, the proposed method achieves more accuracy and robustness when compared to the existing methods in the literature. The segmentation is carried out using PSO after pre-processing of images. The Gaussian functions are used to eliminate the background subtraction. Different features of the images are then computed. A total of 55,400 images were used for the experiment consisting of various plants leaves spreading across 38 labels. A classifier is then proposed using Machine learning methods for the detection of disease in apple fruit leaves. The experiments prove that the proposed method have high degree of classification accuracy when compared to existing methods. The proposed method not only cater to the need in terms of accuracy but also making it scalable for different types of leaves. 2024 The Authors
Rajagopal M.; Kayikci S.; Abbas M.; Sivasakthivel R.
https://doi.org/10.1016/j.heliyon.2024.e32707 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196307387&doi=10.1016%2fj.heliyon.2024.e32707&partnerID=40&md5=a31af7d764bfbfe9d8c0fcb35ce7cdab
Rajagopal M., School of Business and Management, Christ (Deemed to be University), Karnataka, Bengaluru, India; Kayikci S., Department of Computer Engineering, Bolu Abant Izzet Baysal University, Bolu, Turkey; Abbas M., Electrical Engineering Department, College of Engineering, King Khalid University, Abha, 61421, Saudi Arabia; Sivasakthivel R., Department of Computer Science, School of Sciences, Christ (Deemed to be University), Karnataka, Bengaluru, India
Application of Artificial Intelligence in the Supply Chain Finance
Artificial Intelligence; Cost efficient; etc; Supply Chain Management
Artificial intelligence (AI) has numerous applications in supply chain finance, including the ability to streamline processes, improve decision-making, and reduce costs. This abstract will discuss some of the key ways in which AI is being used in supply chain finance. One major Using AI in the Supply Chain finance is in risk management. By analyzing data from a variety of sources, including historical transaction data and external market data, AI can identify potential risks and suggest strategies for managing them. For example, AI can be used to predict which suppliers are at the greatest risk of financial distress, allowing companies to take proactive measures to minimize the impact of any disruptions. Another key Using AI in the Supply Chain finance is in fraud detection. By analyzing large volumes of data in real-time, AI can spot deviations from the norm that may point to fraud. This can help companies to prevent fraud and minimize losses. AI can also be used to optimize working capital management. By analyzing data on inventory levels, order volumes, and payment terms, AI can help companies to optimize their cash flow and improve their working capital position. For example, AI can help companies to identify opportunities to negotiate more favorable payment terms with suppliers or to optimize their inventory levels to minimize the amount of cash tied up in inventory. Finally, AI can be used to improve supply chain efficiency and reduce costs. By analyzing data on order volumes, shipping times, and other factors, A.I. may aid businesses in identify opportunities to their supply network needs improvement processes and reduce costs. For example, AI can aid businesses in determining opportunities to consolidate shipments or to optimize their routes to reduce transportation costs. Now a days AI has numerous applications in supply chain finance, including risk management, fraud detection, working capital management, and supply chain optimization. By leveraging the power of AI, companies can improve their financial performance, reduce costs, and enhance their overall competitiveness. 2023 IEEE.
Rajagopal M.; Nayak K.M.; Balasubramanian K.; Abdul Karim Shaikh I.; Adhav S.; Gupta M.
https://doi.org/10.1109/ICONSTEM56934.2023.10142286 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163211997&doi=10.1109%2fICONSTEM56934.2023.10142286&partnerID=40&md5=0cdf2aa882f3fb56d5712fbed3cf07a8
Rajagopal M., CHRIST(Deemed to Be University), Lean Operations and Systems, School of Business and Management, Bangalore, 560029, India; Nayak K.M., University of Mumbai's, Garware Institute of Career Education and Development, India; Balasubramanian K., Kalasalingam Academy of Research and Education, Kalasalingam Business School, Tamilnadu, Srivilliputhur, India; Abdul Karim Shaikh I., Bahrain Polytechnic, Faculty of Business, Bahrain; Adhav S., Dr. Vishwanath Karad MIT-World Peace University, School of Management (PG), Pune, India; Gupta M., Chitkara University, Chitkara Business School, Punjab, India
A Gradational Approach for Auditing IoT Security Vulnerability: Case Study of Smart Home Devices
The world is experiencing a rapid convergence of physical and cyber systems, as objects used in day-to-day life are connected over the Internet. These Internet of Things (IoT) devices are mass produced, but ensure its usage in routine life. The impact of IoT in human life ranges from simple household equipment to life-critical devices. Owing to the diversity, both in application and nature, the security on these devices and their applications has become a major concern. In spite of having many security frameworks and compliance regulations, attacks on IoTs are exponentially growing. A handful of security frameworks are available for ensuring the security, there are very few frameworks proposed for auditing the security. Confidentiality, Integrity and Availability, which are the pillars of security in IoT, are found missing or found to have been implemented with flaws. An IoT security audit is one good solution that has proven a success in the literature but challenging as the high-level standards cannot be applied to low-level devices and applications. In addition, the challenges of audits include heterogeneity of IoT and lack of expert resources. IoT and related products reached market very quickly before it could be subjected to the complete audit procedures or, in other words, the time taken for a new IoT device or application to be developed is much less than the time taken for developing a security audit mechanism. Hence, to enable an efficient security auditing of IoT devices, a definite and dynamic framework is needed that can propose feasible policies, automatic collection and analysis of audit data and tailor-made procedures for risk assessment, risk control and risk mitigation. This chapter focuses on the auditing of security vulnerability in IoT devices. A gradational methodology is proposed for extracting the feasible security checks from leading standards and guidelines in the IoT domain. To exploit its efficiency, the proposed method is applied to a smart home with IoT enabled devices. Performance metrics such as efficiency, accuracy, and scalability are evaluated. The experiments were carried out in a simulated environment with IoT devices. The results were highly satisfying as the proposed method could do efficient and accurate auditing for seven hundred smart homes in a time of less than fifteen minutes. 2025 Taylor & Francis Group, LLC.
Internet of Things Vulnerabilities and Recovery Strategies, pp. 87-101.
Rajagopal M., Lean Operations and Systems, School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Ramkumar S., Department of Computer Science, School of Sciences, CHRIST (Deemed to be University), Karnataka, Bangalore, India
A Multilayered Feed-Forward Neural Network Architecture for Rainfall Forecasting
The amount of rain received in a particular demographic region in a given time interval is called the rainfall. Rainfall is a natural and complex process and has significance in different domains including agriculture, transport, disaster management, and natural calamities resilience [1]. Abnormal rainfall affects every facet of humans and all other living beings of the world and also has a great impact in wellbeing and financial disruptions of a country. Accurate rainfall predictions at regular time intervals are always important to issue warnings about likelihood of any disaster about to happen. This also provides people a time for strategic planning in their work and precautions at time of adversity [2]. It is worth noting that rainfall forecasting does not only have an impact in day-to-day life, but more importantly for tropical countries like India where the chief occupation being agriculture and also for various other industries. It largely helps in disaster management and recovery process as well. The rainfall being a variable over time, geography and atmospheric conditions makes the forecasting considerably difficult [3]. Rainfall forecasting keeps a person informed about the likelihood of rainfall the forthcoming day, week, or month which enable long-time planning and on the other way; hourly prediction helps for shortterm planning such as enforcing traffic measures. Literature has seen various studies in this domain using predictive machine learning (ML) algorithms such as neural networks (NNs), Genetic algorithms, and Fuzzy-based systems [4]. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar.
Artificial Intelligence in Forecasting: Tools and Techniques, pp. 31-48.
Rajagopal M., School of Business and Management, CHRIST (Deemed to be University), Bangalore, India; Ramkumar S., Department of Computer Science, School of Sciences, CHRIST (Deemed to be University), Bangalore, India
Trust Model for Cloud Using Weighted KNN Classification for Better User Access Control
Access control; Cloud environment and trust value; Nearest neighbor
The majority of the time, cloud computing is a service-based technology that provides Internet-based technological services. Cloud computing has had explosive growth since its debut, and it is now integrated into a wide variety of online services. These have the primary benefit of allowing thin clients to access the resources and services. Even while it could appear favorable, there are a lot of potential weak points for various types of assaults and cyber threats. Access control is one of the several protection layers that are available as part of cloud security solutions. In order to improve cloud security, this research introduces a unique access control mechanism. For granting users access to various resources, the suggested approach applies the trust concept. For the purpose of predicting trust, the KNN model was recently proposed, however the current approach for categorizing options is sensitive and unstable, particularly when an unbalanced data scenario occurs. Furthermore, it has been discovered that using the exponent distance as a weighting system improves classification performance and lowers variance. The prediction of the users trust levels using weighted K-means closest neighbors is presented in this research. According to the findings, the suggested approach is more effective in terms of throughput, cost, and delay. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Rajagopal M.; Ramkumar S.; Gobinath R.; Thimmiraja J.
Lecture Notes in Networks and Systems, Vol-673 LNNS, pp. 237-248.
Springer Science and Business Media Deutschland GmbH
Rajagopal M., Lean Operations and Systems, School of Business and Management, CHRIST (Deemed to be University), Bangalore, India; Ramkumar S., Department of Computer Science, School of Sciences, CHRIST (Deemed to be University), Bangalore, India; Gobinath R., Department of Computer Science, School of Sciences, CHRIST (Deemed to be University), Bangalore, India; Thimmiraja J., Department of Information Technology, Dr. Mahalingam College of Engineering and Technology, Tamil Nadu, Pollachi, India
Auto-encoder Convolut?onal Neural Network (AECNN) for Apple Fruit Flower Detection
Auto-encoder convolutional neural network (AECNN); Fully convolutional network (FCN); Graphics processing unit (GPU); Local binary pattern (LBP)
The yield estimation task altogether relies upon the way toward identifying and checking the quantity of fruits on trees. In production of fruit, basic yield the board choices are guided through the bloom frequency, i.e., the quantity of the flowers that are present in a plantation. The intensity of bloom technique is still commonly assessed by methods for human visual investigation. Mechanized PC vision frameworks for flower recognizable proof depend closely on designed procedures which function just under explicit conditions and with restricted execution. This work comprises four significant advances, (I) system preparing for Fully Convolutional Network (FCN), (ii) preprocessing, (iii) component extraction, (iv) division. Initially, a strategy for assessing high-resolution pictures with deep FCN on Graphics Processing Unit (GPU). Then, non-linear and linear algorithms are presented for lessening the image noise, so the exact flower identification can be ensured. The next phase of the work handles the highlight extraction for diminishing the quality of the prime assets which are needed for handling without compromising on data applicable. By applying Local Binary Pattern (LBP), surface example likelihood can be summed up into a histogram. At last, isolate an image with high resolution into sub patches, assess all patches with the help of AECNN, at that point apply the refinement calculation on acquired score maps to figure out the final version of the mask segmentation. Trial results are led utilizing two datasets on flower pictures of AppleA and AppleB. Results are estimated regarding the measurements like Precision (P) and Recall (R). The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Rajagopal M.; Sathesh Kumar K.; Nagaraja P.; Sivasakthivel R.; Sivaraman G.
Smart Innovation, Systems and Technologies, Vol-395 SIST, pp. 95-104.
Rajagopal M., Lean Operations and Systems, School of Business and Management, Christ (Deemed to be University), Bangalore, India; Sathesh Kumar K., Computer Science and Engineering, Alliance College of Engineering and Design, Alliance University, Central Campus, Anekal, Main Road, Karnataka, Bangalore, India; Nagaraja P., Department of Computer Science, GITAM School of Sciences, GITAM (Deemed to be University), Bangalore, India; Sivasakthivel R., Department of Computer Science, School of Sciences, Chr?st (Deemed to be University), Bangalore, India; Sivaraman G., Department of Computer Science, M.G.R. College, Tamil Nadu, Hosur, India
An Empirical Framework Using Weighted Feed Forward Neural Network for Supply Chain Resilience (SCR) Strategy Selection
Artificial Intelligence (AI); Fuzzy Programming and Multi-Level Decision Support (MLDS) framework; Supply Chain Management (SCM); Supply Chain Resilience (SCR)
Artificial intelligence (AI)-based systems are normally data driven applications, where the model is trained to think on its own based on the external circumstances. The power of AI has reached every facet of business and common life and is even being largely explored to be adopted in life sciences and medical domains. It supports the human in decision-making through the cognitive utilities which arises out of self-learning capabilities of a model. With the exponential growth of data, supply chain management and analytics have attracted a large community of researchers to build intelligent systems which can lead to re-invention of data-driven decision systems powered by AI. Systems and literature of the past shows that AI-based technologies are promising in intelligent supply chain management (SCM) and building resilient SCMs. There is a gap in literature which addresses on the framework for decision support systems in SCM and application of AI methods for building a robust supply chain resilience (SCR) leading to more exploration on the topic. In this paper, a decision framework is proposed by incorporating fuzzy logic and recurrent neural networks (RNN) for disclosing the patterns of various AI-enabled techniques for SCRs. The proposed analysis involved data from leading literatures to determine the most adoptable and significant applications of AI in SCRs. The analysis shows that techniques such as fuzzy programing, network based algorithms, and genetic algorithms have large impact on building SCRs. The results help in decision-making by exhibiting an integrated framework which can help the AI practitioners for developing SCRs. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.
Rajagopal M., School of Business and Management, Lean Operations and Systems, CHRIST (Deemed to be University), Bangalore, India; Sivasakthivel R., Department of Computer Science, School of Sciences, CHRIST (Deemed to be University), Bangalore, India
Deep learning for intelligent transportation: A method to detect traffic violation
Artificial Intelligence; Computer Vision; Smart Transport; YOLOV
Smart transportation is being envisaged as an important parameter in building smart cities. Although conceptualized to have major advantages, lack of intelligent systems makes more vulnerable for disasters. The number of fatality due to road accident has increased up to 12% in 2022 as that of previous year says the WHO report. There are large number of new vehicles plying on roads which makes space constraint for the commuters. This makes a large number of traffic violations happening in urban areas. The smart cities insist and tries to adopt AI based methods for identifying traffic violations. Computer Vision are predominant solution in detecting traffic violation. This paper proposes a Deep learning method using famous YOLOV technique for object detection for effectively determining the traffic violation. The violations such as signal cross are concentrated in this research. The experimental results prove that the proposed technique has 95.1% of classification accuracy in detecting signal crosses. 2023 Author(s).
AIP Conference Proceedings, Vol-2763, No. 1
https://doi.org/10.1063/5.0158376 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176796498&doi=10.1063%2f5.0158376&partnerID=40&md5=19d160efacd7975278503784ce24f9f4
Rajagopal M., CHRIST (Deemed to Be University), Bangalore, India; Sivasakthivel R., CHRIST (Deemed to Be University), Bangalore, India
An Automated Path-Focused Test Case Generation with Dynamic Parameterization Using Adaptive Genetic Algorithm (AGA) for Structural Program Testing
genetic algorithm; mutation and cross-over; SDLC; software testing; test cases
Various software engineering paradigms and real-time projects have proved that software testing is the most critical and highly important phase in the SDLC. In general, software testing takes approximately 4060% of the total effort and time involved in project development. Generating test cases is the most important process in software testing. There are many techniques involved in the automatic generation of these test cases which aim to find a smaller group of cases that could allow for an adequacy level to be achieved which will hence reduce the effort and cost involved in software testing. In the structural testing of a product, the auto-generation of test cases that are path focused in an efficient manner is a challenging process. These are often considered optimization problems and hence search-based methods such as genetic algorithm (GA) and swarm optimizations have been proposed to handle this issue. The significance of the study is to address the optimization problem of automatic test case generation in search-based software engineering. The proposed methodology aims to close the gap of genetic algorithms acquiring local minimum due to poor diversity. Here, dynamic adjustment of cross-over and mutation rate is achieved by calculating the individual measure of similarity and fitness and searching for the more global optimum. The proposed method is applied and experimented on a benchmark of five industrial projects. The results of the experiments have confirmed the efficiency of generating test cases that have optimum path coverage. 2023 by the authors.
Rajagopal M.; Sivasakthivel R.; Loganathan K.; Sarris L.E.
Information (Switzerland), Vol-14, No. 3
https://doi.org/10.3390/info14030166 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151097054&doi=10.3390%2finfo14030166&partnerID=40&md5=1ff3d4b241bf563b8c52f808bfa00fe1
Rajagopal M., Department of Lean Operations and Systems, School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Sivasakthivel R., Department of Computer Science, School of Sciences, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Loganathan K., Department of Mathematics and Statistics, Manipal University Jaipur, Rajasthan, Jaipur, 303007, India; Sarris L.E., Department of Mechanical Engineering, University of West Attica, 250 Thivon & P. Ralli Str, Athens, 12244, Greece
Smart Agriculture: Machine Learning Approach for Tea Leaf Disease Detection
Agriculture; Convolutional neural network; Machine learning; Pattern identification; Tea leafs
Across the globe, plant infections from pathogens such as fungi, bacteria and viruses are the major issues in the agricultural sector. Agricultural productivity is one of the most important things on which the nations economy highly depends. The detection of diseases in plants plays a major role in the agricultural field. This study proposes a multi-stage network involving Convolutional neural network, Pattern identification and Classification using Siamese network. The main objective behind this study is to enhance the disease detection technique performance. The image data of Tea leaves chosen for this study will be gathered. The algorithms based on techniques of image processing would be designed. The proposed algorithm was tested on the following diseases namely Red rust, Blister blight, Twig dieback, Stem canker, Grey Blight, Brown Blight, Brown root rot disease and Red root rot disease in Tea leaves. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Rajagopal M.; Sivasakthivel R.; Pandey M.
Lecture Notes in Networks and Systems, Vol-967 LNNS, pp. 199-209.
Rajagopal M., School of Business and Management, Christ University, Bangalore, India; Sivasakthivel R., Department of Computer Science, School of Sciences, Christ University, Bangalore, India; Pandey M., School of Business and Management, Christ University, Bangalore, India
A Conceptual Framework for AI Governance in Public Administration - A Smart Governance Perspective
Artificial Intelligence; Ethics; Public Administration; Regulation Theory; Smart Governance
With the public governance lagging behind the fast evolving of AI in their attempts to yield sufficient governance, corresponding principles are necessary to be in par with this dynamic advancement. As AI becomes more pervasive and integrated into various domains, there is a growing need for AI governance models that can ensure that the development and deployment of AI systems align with ethical, legal, and social standards. There are some answers that literature puts forward to the question onthe way the government and public administration has to react to the huge concerns related to AI and usage of policies to avoid the emerging challenges. In this survey, AI problems and the prior AI regulation techniques are analyzed. In this research study, a governance model for AI is proposed by combining all the facets and also implements a new procedure for governing AI. This study will help the decision makers to make smart government a reality by using AI governance framework. 2023 IEEE.
Rajagopal M.; Sivasakthivel R.; Ramar G.; Mansurali A.; Karuppasamy S.K.
7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023 - Proceedings, pp. 488-495.
Rajagopal M., Christ (Deemed to Be University), Lean Operations and Systems, School of Business and Management, Bangalore, India; Sivasakthivel R., Christ (Deemed to Be University), School of Sciences, Department of Computer Science, Bangalore, India; Ramar G., Christ (Deemed to Be University), School of Sciences, Department of Computer Science, Bangalore, India; Mansurali A., Central University of Tamilnadu, Department of Commerce and Management, India; Karuppasamy S.K., Kalasalingam Academy of Research and Education, School of Computing, Department of Computer Science & Information Technology, Krishnankoil, India
Minimizing Energy Depletion Using Extended Lifespan: QoS Satisfied Multiple Learned Rate (ELQSSM-ML) for Increased Lifespan of Mobile Adhoc Networks (MANET)
energy depletion; MANET; multicast communication; network lifetime path stability; QSSM-ML algorithm
Mobile Adhoc Networks (MANETs) typically employ with the aid of new technology to increase Quality-of-Service (QoS) when forwarding multiple data rates. This kind of network causes high forwarding delays and improper data transfer rates because of the changes in the nodes vicinity. Although an optimized routing technique to transfer energy has been used to lessen the delay and improve the throughput by assigning a proper data rate, it does not consider the objective of minimizing the energy use, which results in less network lifetime. The goal of the proposed work is to minimize the energy depletion in a MANET, which results in an extended Lifespan of the network. In this research paper, an Extended Life span and QSSM-ML routing algorithm is proposed, which minimizes energy use and enhances the network lifetime. First, an optimization problem is formulated with the purpose of increasing the networks lifetime while limiting the energy utilization and stability of the path along with residual. Second, an adaptive policy is applied for the asymmetric distribution of energy at both origin and intermediate nodes. In order to achieve maximum network lifespan and minimal energy depletion, the optimization problem was framed when power usage is a constraint by allowing the network to make use of the leftover power. An asymmetric energy transmission strategy was also designed for the adaptive allocation of maximum transmission energy in the origin. This made the network lifespan extended with the help of reducing the nodes energy use for broadcasting the data from the origin to the target. Moreover, the nodes energy use during packet forwarding is reduced to recover the network lifetime. The overall benefit of the proposed work is that it can achieve both minimal energy depletion and maximizes the lifetime of the network. Finally, the simulation findings reveal that the ELQSSM-ML algorithm accomplishes a better network performance than the classical algorithms. 2023 by the authors.
Rajagopal M.; Sivasakthivel R.; Venugopal J.; Sarris I.E.; Loganathan K.
Information (Switzerland), Vol-14, No. 4
https://doi.org/10.3390/info14040244 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153675883&doi=10.3390%2finfo14040244&partnerID=40&md5=7cbcb1aedffbdbe4b7eaa4fffff795c8
Rajagopal M., Department of Lean Operations and Systems, School of Business and Management, CHRIST, Karnataka, Bengaluru, 560001, India; Sivasakthivel R., Department of Computer Science, School of Sciences, CHRIST, Karnataka, Bengaluru, 560001, India; Venugopal J., Department of Computer Science and Engineering, Manipal University Jaipur, Rajasthan, Jaipur, 303007, India; Sarris I.E., Department of Mechanical Engineering, University of West Attica, 250 Thivon & P. Ralli Str, Athens, Egaleo, 12244, Greece; Loganathan K., Department of Mathematics and Statistics, Manipal University Jaipur, Rajasthan, Jaipur, 303007, India
Attachment to God: Narratives of Roman Catholic Priests
attachment; attachment to God; Roman Catholic clergy
This narrative analysis was aimed at exploring the attachment to God narratives of 28 middle-aged Roman Catholic Religious priests rendering their service in various settings in South India. The study found that majority of the Roman Catholic priests had developed representations of a secure attachment to God. Twenty-six priests had developed representations of a secure attachment to God, and two priests of an insecure attachment to God. The Majority of the Roman Catholic priests had developed representations of a secure attachment to more than one spiritual attachment figures. Along with God, most priests had also developed representations of a secure attachment to the Virgin Mary. All the major themes related to attachment to God were found in the narratives of the Roman Catholic Priests. Author(s) 2020.
Journal of Psychology and Theology, Vol-49, No. 2, pp. 192-204.
Rajagopalreddy D., CHRIST (Deemed to be University), India; Varghese K., CHRIST (Deemed to be University), India