On Automatic Target Recognition (ATR) using Inverse Synthetic Aperture Radar Images
Automatic Target Recognition; Deep learning; Feature extraction; Image Classification; ISAR Image Detection; Ship Classification
Inverse Synthetic Aperture Radar (ISAR) is used to image sea surface targets during day/night and all-weather capabilities for applications such as coastal surveillance, ship self-defense, suppression of drug trafficking etc. Hence automating classification of ships by means of machine learning methods has become more significant. Typical classification approaches consist of pre-processing, feature extraction and processing by classifiers. Image processing techniques are applied for pre-processing ISAR images. Transformation invariant features are then extracted to which classifiers such as SVM, Neural Networks (NNs) are applied. The performance of these algorithms depend on the manually chosen features and is trained to perform well for different target profiles. The target image (profile of target) varies depending on the target type, aspect angle and motion introduced due to different sea states. In addition, Deep learning methods are also being explored for classification of ships. The challenge is to classify ships for different sea conditions and image acquisition parameters with limited database and processing resource. 2023 IEEE.
Sudarson Rama Perumal T.; Gaurav G.S.; Helen Josephine V.L.; Joshua Samuel Raj R.
6th International Conference on Inventive Computation Technologies, ICICT 2023 - Proceedings, pp. 953-956.
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/ICICT57646.2023.10134428 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163540791&doi=10.1109%2fICICT57646.2023.10134428&partnerID=40&md5=1d810dbce0359215bca2aca7ef444f55
Sudarson Rama Perumal T., Dept. of CSE, Rohini College of Engineering and Technology, TamilNadu, India; Gaurav G.S., Dept. of. ISE, CMR Institute of Technology, Bengaluru, India; Helen Josephine V.L., Business Analytics, School of Business and Management, Christ University, Bangalore, India; Joshua Samuel Raj R., Faculty of EDICT, Bahrain Polytechnic, Isa Town, Bahrain
Characteristic Mode Analysis of Metallic Automobile Logo Geometry
5G; Automobile logos; multi-band; Theory of characteristic mode (TCM)
This paper presents a characteristic mode analysis of a few popular automobile logo geometries. It is performed to get an insight into the physical behavior of those geometries which can be employed as a radiating element, such as an antenna. Such an analysis helps design multi-band and multi-mode antennas suitable for 5G sub-6 GHz bands. The resonant behavior, bandwidth capability, and modal current distribution analysis are presented for various modes of different automobile logo geometries, demonstrating that Audi, Suzuki, and Volkswagen logos show multi-band performance. Moreover, due to having symmetric modes, the BMW logo was found to be suitable for designing a circularly polarized antenna. 2023 IEEE.
Kumar N.; Seetharamdoo D.; Koohestani M.
2023 IEEE Radio and Antenna Days of the Indian Ocean, RADIO 2023
Kumar N., Christ University, Eced, Bengaluru, India; Seetharamdoo D., Univ Gustave Eiffel, COSYS-LEOST, Villeneuve d'Ascq, F-59650, France; Koohestani M., Eseo School of Engineering, Eeed, Angers, France
Innovative Method for Alzheimer Disease Prediction using GP-ELM-RNN
Alzheimer Disease (AD); GP (Garson-Purned); Grey Level Co-occurrence Matrix (GLCM)
Brain illnesses are notoriously challenging because of their fragility, surgical complexity, and high treatment costs. Contrarily, it is not obligatory to carry out the operation, as the outcomes of the procedure may fall short of expectations. Adult-onset Alzheimer's disease, which causes memory loss and losing information to varied degrees, is one of the most common brain diseases. This will vary from person to person based on their current health situation. This highlights the need of using CT brain scans to classify the extent of memory loss and determine the patient's risk for Alzheimer's disease. The four main goals of Alzheimer's disease detection are preprocessing the data, extracting features, selecting features, and training the model with GP-ELM-RNN. The Replicator Neural Network has been utilized earlier for AD detection, however this study offers an improved version of the network, modified with ELM learning and the Garson algorithm. From this study, it is deduced that the proposed method is not only efficient, but also quite precise. In this research, GP-ELM-RNN network is built to four groups of images representing different stages of Alzheimer's disease: very mildly demented, mildly demented, averagely demented, and non-demented. The class of very mildly demented patients was found to have the highest accuracy (99.1%) and specificity (0.984%). As compared to the ELM and RNN models, this technique achieves superior accuracy (around 99.23%). 2023 IEEE.
Abd Algani Y.M.; Vidhya S.; Ghai B.; Acharjee P.B.; Kathiravan M.N.; Dwivedi V.K.
Proceedings of the 2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023, pp. 723-728.
Abd Algani Y.M., The Arab Academic College for Education in Israel-Haifa, Department of Mathematics, Israel; Vidhya S., Rmk College of Engineering and Technology, S&H(Mathematics), Tamilnadu, Chennai, India; Ghai B., Ccsit Teerthankar Mahaveer University, Uttar Pradesh, Moradabad, India; Acharjee P.B., Christ University, Faculty Member, Maharashtra, Pune, India; Kathiravan M.N., Dr. N.G.P Arts and Science College, Department of Biotechnology, Tamil Nadu, Coimbatore, India; Dwivedi V.K., Vishwavidyalaya Engineering College, Department of Mathematics, Chhattisgarh, Surguja, India
Enhancing red wine quality prediction through Machine Learning approaches with Hyperparameters optimization technique
Grid search; Hyperparameter tuning; Machine learning classifiers; Red wine; Regressors
In light of the intricacy of the winemaking process and the wide variety of elements that could affect the taste and quality of the finished product, predicting red wine quality is difficult. ML methods have been widely used in forecasting red wine quality from its chemical characteristics in recent years. This Paper evaluated the comparison of classification and regression methods to predict the quality of red wine and performed the initial data analysis and exploratory data analysis on the data. This study implemented different Classifiers and Regressors that were trained and tested. Contrasted and Comparative analysis of the accuracies of eight models with hyperparameter tuning optimization, including Logistic Regression, Gradient Boosting, Extra Tree, Ada Boost, Random Forest, Support Vector Classifier, and Decision Tree, Knn and measured the classification report with F1, Accuracy, and Recall Scores. For Imbalance data, SMOTE Classifier was used. This study performed the Cross-validation technique, such as Grid search and with the best hyperparameters tuning. The study's findings demonstrated that the Gradient Boosting technique accurately predicted red wine quality. This research shows the promising results of Gradient Boosting for predicting red wine quality and adds important context to the usage of machine learning classifiers for this challenge. 2023 IEEE.
Amzad Basha M.S.; Desai K.; Christina S.; Sucharitha M.M.; Maheshwari A.
2023 2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023
Amzad Basha M.S., GITAM School of Business, Gandhi Institute of Technology and Management (Deemed to Be University), Bengaluru, India; Desai K., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Christina S., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Sucharitha M.M., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Maheshwari A., School of Commerce, Finance, Christ (Deemed to Be University), Department of Commerce, Ghaziabad, India
A Deep Convolutional Kernel Neural Network based Approach for Stock Market Prediction using Social Media Data
Convolutional Neural Network (CNN); Extreme Learning Machine (ELM); Kernel Extreme Learning Machine (KELM)
Several economists and social scientists have held a longstanding fascination with the practice of stock market prediction. As the stock market is essentially uncontrollable chaos, many experts believe that trying to predict it is futile. Due to the complexity of the numerous factors, accurate stock price predictions are notoriously difficult to achieve. While the market behaves more like a scale than a voting machine over the long run, its behavior may be predicted with some certainty. Information from Twitter is used into the algorithm. In this proposed method, a convolutional extreme learning machine model with kernel support was introduced (CKELM). To improve feature extraction and data classification, the CKELM model builds on the KELM's hidden layer by adding convolutional and subsampling layers. The convolutional layer and the subsampling layer do not employ the gradient technique to fine-tune their parameters because some designs worked well with random weights. When compared to popular models like CNN and KELM, The proposed model fares quite well, with an accuracy of around 98.3 percent. 2023 IEEE.
Agarwal V.; Ravi Kumar P.; Shankar S.; Praveena S.; Dubey V.; Chauhan A.
Proceedings of the 7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023, pp. 78-82.
Agarwal V., ATLAS SkillTech University, ISME, Maharashtra, Mumbai, India; Ravi Kumar P., Kristu Jayanti College (Autonomous), K.Narayanapura, Department of Media Studies, Karnataka, Bangalore, India; Shankar S., Computational Intelligence Srmist Kattankulathur, Tamil Nadu, Chengalpattu, India; Praveena S., Mahatma Gandhi Institute of Technology (MGIT), Department of Electronics and Communication Engineering, Telangana, Hyderabad, India; Dubey V., Computer Science and Engineering, Bharat Institute of Engineering and Technology, Telangana, Hyderabad, India; Chauhan A., CHRIST (Deemed to Be University), Department of Life Sciences, Karnataka, Bengaluru, India
Tumor Infiltration of Microrobot using Magnetic torque and AI Technique
ANFIS; CNN; Disease Prediction; Machine learning
Because of their surroundings and lifestyle alternatives, human beings, these days be afflicted by a huge style of illnesses. thus, early contamination prediction will become crucial. on the other hand, primarily based just on signs, docs warfare to make correct forecasts. The most challenging issue is accurately forecasting illnesses, which is why machine learning is essential to accomplish this task. To identify concealed patterns within vast amounts of medical data, disease information is processed using data mining techniques. We evolved a extensive contamination prediction primarily based on the affected person's signs. We rent the device getting to know techniques Convolutional Neural network (CNN) and ANFIS to exactly count on sickness (adaptive community-based totally fuzzy inference machine). For an correct forecast, this trendy illness prediction considers the character's way of life picks and fitness history. ANFIS outperforms CNN's set of rules in phrases of popular infection prediction, with an accuracy price of 96.7%. additionally, CNN consumes extra memory and processing energy than ANFIS because it trains and assessments on facts from the UCI repository. The Anaconda notebook is a suitable tool for implementing Python programming as it contains a range of libraries and header files that enhance the accuracy and precision of the process. 2023 IEEE.
Shalini R.; Mishra L.; Athulya S.; Chimankar A.G.; Kandavalli S.R.; Keshav Kumar K.; Selvan R.S.
ViTECoN 2023 - 2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, Proceedings
https://doi.org/10.1109/ViTECoN58111.2023.10157336 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165534220&doi=10.1109%2fViTECoN58111.2023.10157336&partnerID=40&md5=ecb0e66b1c15e33dea8bbf8b83f3e250
Shalini R., School of Computing Sciences, Vels Institute of Science Technology and Advanced Studies, Department of BCA & IT, Chennai, India; Mishra L., Khwaja Moinuddin Chishti Language University, Department of Electronics and Communication Engineering, Uttar Pradesh, India; Athulya S., Christ Deemed to Be University, Faculty of Engineering, Department of Computer Science and Engineering, Bangalore, India; Chimankar A.G., Chatrapati Shivaji BCA and BCS College, Department of B.Sc. Computer Science, Maharashtra, India; Kandavalli S.R., Sri Padmavati Mahila Visva Vidyalayam, Department of Electronics and Communication Engineering, Tirupati, India; Keshav Kumar K., G. Narayanamma Institute of Technology and Science, Department of Humanities and Mathematics, Hyderabad, India; Selvan R.S., Annamacharya Institute of Technology & Sciences, Department of Electronics and Communication Engineering, Tirupati, India
Financial Big Data Analysis Using Anti-tampering Blockchain-Based Deep Learning
Anti-Tampering Model; Blockchain; Deep Learning; Financial Big Data
This study recommends using blockchains to track and verify data in financial service chains. The financial industry may increase its core competitiveness and value by using a deep learning-based blockchain network to improve financial transaction security and capital flow stability. Future trading processes will benefit from blockchain knowledge. In this paper, we develop a blockchain model with a deep learning framework to prevent tampering with distributed databases by considering the limitations of current supply-chain finance research methodologies. The proposed model had 90.2% accuracy, 89.6% precision, 91.8% recall, 90.5% F1 Score, and 29% MAPE. Choosing distributed data properties and minimizing the process can improve accuracy. Using code merging and monitoring encryption, critical blockchain data can be obtained. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Praghash K.; Yuvaraj N.; Peter G.; Stonier A.A.; Priya R.D.
Lecture Notes in Networks and Systems, Vol-647 LNNS, pp. 1031-1040.
Springer Science and Business Media Deutschland GmbH
Praghash K., Department of Electronics and Communication Engineering, Christ University, Bengaluru, India; Yuvaraj N., Department of Research and Publications, ICT Academy, IIT Madras Research Park, ManagerChennai, India; Peter G., CRISD, School of Engineering and Technology, University of Technology Sarawak, Sibu, Malaysia; Stonier A.A., School of Electrical Engineering, Vellore Institute of Technology, Tamil Nadu, Vellore, India; Priya R.D., Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Tamil Nadu, Coimbatore, India
Characteristic Mode Analysis of Fashion Brands Conductive Logos as Potential Radiators
bandwidth capabilities; Characteristic modes analysis; fashion brands; propagation modes
A few popular fashion brand logos, which can be employed as potential radiating elements, are investigated in this paper based on the theory of characteristic mode (TCM). Such an analysis would further help design multi-band wearable antennas within the frequency range from 1 to 6 GHz. The resonant behavior and bandwidth capability for various modes are presented and discussed. It is observed that all the studied logos demonstrate a first resonant frequency around 1.5 GHz, while both Lacoste and Louis Vuitton logos show wider modal bandwidths capabilities. 2023 IEEE.
Kumar N., Christ University, Eced, Bengaluru, India; Koohestani M., Ietr, ole Supieure d'ectronique de l'Ouest, Cnrs 6164, Angers, Paris, France; Khanna R., Thapar Institute of Engineering and Technology, Punjab, India
An Advanced and Ideal Method for Tumor Detection and Classification from MRI Image Using Gamma Distribution and Support Vector Machine
Diagnosis process; Gamma distribution; Tumor
As indicated by a measurable report distributed by the registry of central brain tumor at United States (CBTRUS), roughly 59,550 individuals were recently diagnosed to have essential benign and essential harmful brain tumors in 2017. Besides, in excess of 91,000 individuals, in the United States alone, were living with an essential harmful cerebrum tumor and 367,000 were living with an essential kind brain tumor. The task of detecting the position of the tumor in the body of the patient is the starting point for a medical treatment in the diagnosis process. The main aim of this study is to design a computer system, which is able to detect the tumor presence in the digital images of the brain in the patient and to accurately define its borderline. In this proposed model, gamma distribution method is used for training, testing, and for the feature extraction process, while SVM, support vector machine is used for the classification process. Most of the algorithms find it difficult to segment the tumors that were present in the edges. But with the help of gamma distribution along with the use of edge analysis, it is easier to identify those tumor areas that are present in the edges, thus making it easier for the preprocessing process. Gamma distribution also provides us with high accuracy, and it can also point the exact location of the tumor than compared to other algorithms. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Aruna S.K.; Sambandam R.K.; Thaiyalnayaki S.; Vetriveeran D.
Smart Innovation, Systems and Technologies, Vol-313, pp. 439-447.
Aruna S.K., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University)Kengeri Campus, Karnataka, Bangalore, India; Sambandam R.K., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University)Kengeri Campus, Karnataka, Bangalore, India; Thaiyalnayaki S., Department of Computer Science and Engineering, School of Computing, Bharath Institute of Higher Education and Research (Deemed to be University), Tamilnadu, Chennai, India; Vetriveeran D., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University)Kengeri Campus, Karnataka, Bangalore, India
Tracking and Localization of Devices - An IoT Review
Internet of Things; Localization; Tracking; Wireless Sensor Networks
S everal IoT applications have immediate impacts on daily lives. The notion of "connected life, which includes IoT has been discussed. Apps that rely on localization are also featured. IoT is originally used to determine the precise position of things, animals, and people. The second tracks everyone and everything that's on the move, including pets, kids, and the elderly people. Localization and tracking are integral parts of security and surveillance systems in interconnected homes. This study reviews the state-of-the-art IoT-based localization and tracking approaches and outlines the key technical aspects, and contrast localization initiatives based on Internet of Things (IoT) with those that do not show how they might be used in a variety of contexts. It is now well established that localization and tracking methods based on the Internet of Things (IoT) are more pervasive and accurate than their predecessors. 2023 IEEE.
6th International Conference on Inventive Computation Technologies, ICICT 2023 - Proceedings, pp. 1321-1325.
https://doi.org/10.1109/ICICT57646.2023.10134278 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163485509&doi=10.1109%2fICICT57646.2023.10134278&partnerID=40&md5=c87cc7a79ab413078cfba05ee16e6c84
Sivadevuni S.S., Department of Computer Science and Engineering, Christ University, Bengaluru, India; Ravichandran S.K., Department of Computer Science and Engineering, Christ University, Bengaluru, India
ANN Based MPPT Using Boost Converter for Solar Water Pumping Using DC Motor
ANN; BLDC; Boost Converter; MPPT; PID; SPV
The solar DC pump system is simple to set up and run completely on its own without the need for human intervention. Solar DC pumps require fewer solar panels to operate than AC pumps. Solar PV Arrays, a solar DC regulator, and a DC pump make up the Solar DC Pump system. The nonlinear I-V characteristics of solar cells, PV modules have average efficiency compare to other forms of energy, and output power is affected by solar isolation and ambient temperature. The prominent factor to remember is that there will be a significant power loss owing to a failure to correspond between the source and the load. In order to get the most power to load from the PV panel, MPPT is implemented in the converter circuit using PWM and a microcontroller. In order to give the most power to load from the source, the solar power system should be designed to its full potential. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Jurme T.; Phelgay T.; Gyeltshen P.; Dorji S.; Tobgay T.; Praghash K.; Chidambaram S.
Lecture Notes in Networks and Systems, Vol-647 LNNS, pp. 841-851.
Jurme T., Department of Electrical Engineering, Jigme Namgyel Engineering College, Dewathang, Bhutan; Phelgay T., Department of Electrical Engineering, Jigme Namgyel Engineering College, Dewathang, Bhutan; Gyeltshen P., Department of Electrical Engineering, Jigme Namgyel Engineering College, Dewathang, Bhutan; Dorji S., Department of Electrical Engineering, Jigme Namgyel Engineering College, Dewathang, Bhutan; Tobgay T., Department of Electrical Engineering, Jigme Namgyel Engineering College, Dewathang, Bhutan; Praghash K., Department of Electronics and Communication Engineering, Christ University, Bengaluru, India; Chidambaram S., Department of Electronics and Communication Engineering, Christ University, Bengaluru, India
Building Robust FinTech Applications and Reducing Strain on Strategic Data Centers using the LoTus Model
Agile; Architecture; FinTech; LoTus Model; Scrum; Software Development; Software Engineering
Agile is a well-known project management approach that has been used for many years. It places a strong emphasis on client satisfaction, adaptability, and teamwork. Agile was first developed as a software development approach, but it has now been modified for application in other sectors including marketing and finance. The Agile Manifesto, which was released in 2001 and explains the principles and ideals of Agile development, is the foundation of the Agile ideology. One or more of the guiding principles is to adapt to change instead of following a plan, prioritize functional software over thorough documentation, and collaborate with customers over negotiating contracts. Agile has gained popularity over time as businesses try to be adaptable and responsive to their customers' constantly changing business demands. The lack of predictability in Agile is one of its key drawbacks. Agile stresses client cooperation and adaptation, therefore the finished product could differ somewhat from what was originally planned. For businesses that depend on meticulous planning and a rigid schedule, this lack of predictability can be problematic. It faced a serious problem during the process of building a finance application called JazzFinance. This has led to build another robust and systematic software development method called as LoTus model. The proposed LoTus is an acronym for two abbreviations. Those are lean optimization TypeFace for Unified Systems (LoTus) and Locate dependencies, optimize for reusability, Test-Driven environment, Unify Design and Scalability. This article goes through the development of LoTus and how it has helped us build a stable finance application within a small amount of stipulated time. 2023 IEEE.
Lakshmikanth Rajath Mohan T.; Jayapandian N.
Proceedings of the 7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023, pp. 1176-1181.
Lakshmikanth Rajath Mohan T., CHRIST (Deemed to Be University), School of Engineering and Technology, India; Jayapandian N., CHRIST (Deemed to Be University), School of Engineering and Technology, India
A study of CNN models for re-identification of vehicles
Convolutional Neural Networks; Datasets; Deep Learning; Object Character Recognition; Vehicle Re-identification
Vehicle Re-identification has evolved in recent times. Initially, clicking a single picture of a vehicle or a car was done manually, inviting the workforce to complete a specified task. With the growth in technology, the method and techniques in Vehicle Re-Id also have advanced, transforming from manual to automation. Surveillance cameras were used to capture vehicle images and retrieve information about a specific vehicle. Re-trieving and identifying the images of the vehicle is done using computer vision, the most important branch of computer science and artificial intelligence. Earlier, Vehicle Re-Id implemented a single algorithm on a dataset, making the corresponding result insufficient to determine its effects. This paper proposes a brief survey of multi-modal techniques and methods for vehicle re-identification and fingerprinting. The different attributes of the vehicle are considered for ANPR (Automatic number plate recognition) for identifying the number plate, focusing on the vehicle's details or features as the initial phase of identification, and then the vehicle number plate. 2023 IEEE.
https://doi.org/10.1109/ICEEICT56924.2023.10157194 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165733997&doi=10.1109%2fICEEICT56924.2023.10157194&partnerID=40&md5=2f1ffe8650742ff3ffd4c5f01c404fee
Mathews M., CHRIST (Deemed to Be University), Bangalore, India; Sethilnathan T., CHRIST (Deemed to Be University), Bangalore, India
KMetaTagger: A Knowledge Centric Metadata Driven Hybrid Tag Recommendation Model Encompassing Machine Intelligence
Color Harmony; GRU; LSI; Tag Recommendation; Tagging; TF IDF
The emergence of Web 3.0 has left very few tag recommendation structures compliant with its complex structure. There is a critical need for newer novel methods with improved accuracy and reduced complexity for tag recommendation, which complies with the Web 3.0 standard. In this paper, we propose KMetaTagger, a knowledge-centric metadata-driven hybrid tag recommendation framework. We consider the CISI dataset as the input, from which we identify the most informative terms by applying the Term Frequency - Inverse Document Frequency (TF-IDF) model. Topic modeling is done by Latent Semantic Indexing (LSI). A heterogeneous information network is formalized. Apart from this, the Metadata generation quantifies the exponential aggregation of real-world knowledge and is classified using Gated recurrent units(GRU). The Color Harmony algorithm filters out the initial feasible solutions into optimal solutions. This advanced solution set is finalized into the tag space. These tags are recommended along with the document keywords. When the suggested KMetaTagger's performance is compared to that of baseline techniques and models, it is found to be far superior. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Ashvanth R.; Deepak G.; Sheeba Priyadarshini J.; Santhanavijayan A.
Lecture Notes in Networks and Systems, Vol-646 LNNS, pp. 1-11.
Ashvanth R., Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, India; Deepak G., Department of Computer Science and Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Bengaluru, India; Sheeba Priyadarshini J., Deparment of Data Science, CHRIST (Deemed to Be University), Bangalore, India; Santhanavijayan A., Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, 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.
Proceedings of 8th IEEE International Conference on Science, Technology, Engineering and Mathematics, ICONSTEM 2023
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
An Efficient Deep Learning Framework FPR Detecting and Classifying Depression Using Electroencephalogram Signals
Autoencoder; Classification; Convolutional Neural Network; Depression; Electroencephalogram
Depression is a common and real clinical disease that has a negative impact on how you feel, how you think, and how you behave. It is a significant burdensome problem. Fortunately, it can also be treated. Feelings of self-pity and a lack of interest in activities you once enjoyed are symptoms of depression. It can cause a variety of serious problems that are real, and it can make it harder for you to work both at home and at work. The main causes include family history, illness, medications, and personality, all of which are linked to electroencephalogram (EEG) signals, which are thought of as the most reliable tools for diagnosing depression because they reflect the state of the human cerebrum's functioning. Deep learning (DL), which has been extensively used in this field, is one of the new emerging technologies that is revolutionizing it. In order to classify depression using EEG signals, this paper presents an efficient deep learning model that allows for the following steps: (a) acquisition of data from the psychiatry department at the Government Medical College in Kozhikode, Kerala, India, totaling 4200 files; (b) preprocessing of these raw EEG signals to avoid line noise without committing filtering; (c) feature extraction using Stacked Denoising Autoevolution; and (d) reference of the signal to estimate true and all. According to experimental findings, The proposed model outperforms other cutting-edge models in a number of ways (accuracy: 0.96, sensitivity: 0.97, specificity: 0.97, detection rate: 0.94). 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Aswathy S.U.; Vincent B.; Jacob P.M.; Aniyan N.; Daniel D.; Thomas J.
Lecture Notes in Networks and Systems, Vol-647 LNNS, pp. 1179-1188.
Aswathy S.U., Department of Computer Science and Engineering, Marian Engineering College, Kerala, Thiruvananthapuram, India; Vincent B., Department of Computer Science and Engineering, Providence College of Engineering, Kerala, Alappuzha, India; Jacob P.M., Department of Computer Science and Engineering, Providence College of Engineering, Kerala, Alappuzha, India; Aniyan N., Department of Computer Science and Engineering, Providence College of Engineering, Kerala, Alappuzha, India; Daniel D., Department of Computer Science and Engineering, Providence College of Engineering, Kerala, Alappuzha, India; Thomas J., Department of Computer Science and Engineering, Christ University, Bangalore, India
TAMIL- NLP: Roles and Impact of Machine Learning and Deep Learning with Natural Language Processing for Tamil
Deep learning; Machine learning; Morphological analysis; NLP; Parser
Reading information in your mother tongue gives the feeling of enjoying juice of fruit. Researchers are working on regional languages to provide convenient and perfect automated tools to convert the content of knowledge from other languages. There exist many challenges based on the grammar of language. One of the classic regional languages, Tamil which is rich in Morphology, contains more processing challenges. The Natural Language Processing (NLP) technique along with Machine Learning (ML) and Deep Learning (DL) algorithms have been used to overcome those challenges. The accuracy of work is depending on the corpus provided to train the model. Among the reviewed papers using Support Vector Machine (SVM) of ML produced higher accuracy then other ML techniques. As DL techniques for NLP are booming one the researchers are working with different DL algorithms. Most of the NLP with Review Discussion in this paper will direct the researchers doing NLP in Tamil language to move further and to choose the right Machine Learning and Deep Learning algorithm to come out with accurate outcomes. 2023 IEEE.
Gokila S., Hindustan Institute of Technology and Science, Department of Computer Applications, Chennai, India; Rajeswari S., Shrimathi DevkunvarNanalal Bhatt Vaishnav College for Women, Pg Department of Computer Science, Chennai, India; Deepa S., Christ University, Department of Computer Science, Bangalore, India
Innovative Method for Detecting Liver Cancer using Auto Encoder and Single Feed Forward Neural Network
Auto Encoder (AE); Convolutional Neural Network (CNN); Extreme Learning Machine; Support Vector Machine (SVM)
Liver cancer ranks sixth among all cancers in frequency of incidence. A CT scan is the gold standard for diagnosis. These days, CT scan images of the liver and its tumor can be segmented using deep learning and Neural Network techniques. In this proposed approach to identifying cancer cells, it's focus on four important areas: To enhance a photo by taking out imperfections and unwanted details. An ostu method is used for this purpose. Specifically, this proposed approach to use the watershed segmentation technique for image segmentation, followed by feature extraction, in an effort to isolate the offending cancer cell. After finishing the model training with AE-ELM. To do this, Extreme Learning Machine incorporates an auto encoder. To achieve effective and supervised recognition, the network's strengths of Extreme Learning Machine (ELM) are thoroughly leveraged, including its few training parameters, quick learning speed, and robust generalization ability. The auto encoder-extreme learning machine (AE-ELM) network has been shown to have a respectable recognition impact when the sigmoid activation function is used and the number of hidden layer neurons is set to 1200. According to the results of this investigation, a method based on AE-ELM can be utilized to detect the liver tumor. As compared to the CNN and ELM models, this technique achieves superior accuracy (around 99.23%). 2023 IEEE.
Sowparnika B.; Yamini K.; Walid M.A.A.; Prasad J.; Aparna N.; Chauhan A.
Proceedings of the 2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023, pp. 156-161.
Sowparnika B., Nandha Engineering College, Department of Biomedical Engineering, Tamilnadu, Erode, India; Yamini K., Sriher University, Cyber Security in Computer Science and Engineering, Sri Ramachandra Faculty of Engineering, Tamilnadu, Chennai, India; Walid M.A.A., Khulna University of Engineering & Technology (KUET), Department of Computer Science and Engineering, Bangladesh; Prasad J., Shri Shankaracharya College of Pharmaceutical Sciences, Chhattisgarh, Junwani, Bhilai, India; Aparna N., Dhaanish Ahmed Institute of Technology, Department of Bio Medical Engineering, Tamilnadu, Coimbatore, India; Chauhan A., Christ (Deemed to Be University), Department of Life Sciences, Karnataka, Bengaluru, India
Weighted Mask Recurrent-Convolutional Neural Network based Plant Disease Detection using Leaf Images
Convolutional Neural Network (CNN); Grey Level Cooccurence Matrix (GLCM); Recurrent Neural Network (RNN)
Large losses in output, money, and quality/quantity of agricultural goods are incurred due to plant diseases. Seventy percent of India's GDP is tied to the agricultural sector, thus protecting plants from diseases is crucial. For this reason, it is important to keep an eye on plants from the moment they sprout. The usual approach for this omission is naked eye inspection, which is more time-consuming, costly, and requires significant skill. Thus, automating the method for detecting diseases is necessary to speed up this process. It is imperative that image processing methods be used in the creation of the illness detection system. Disease detection involves a number of processes, including Weighted Mask R-CNN, GLCM feature extraction, Multi-thresholding image pre-processing, and K means image segmentation classification. The weighted Mask R-CNN outperforms the standard RNN, the Mask R-CNN, and the CNN in terms of accuracy and recall in analytical trials by a significant margin. 2023 IEEE.
Sharmila R.; Kamalitta R.; Moorthy; Singh D.P.; Chauhan A.; Acharjee P.B.
Proceedings of the 7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023, pp. 681-687.
Sharmila R., Dhanalakshmi Srinivasan Engineering College, Department of MCA, Tamilnadu, Perambalur, India; Kamalitta R., Information Technology, K.Ramakrishnan College of Engineering, Tamilnadu, Trichy, India; Moorthy, Electronics Engineer, Production, Dhaksha Unmanned System Pvt Ltd, Tamilnadu, Chennai, India; Singh D.P., Graphic Era Deemed to Be University, Department of Computer Science & Engineering, Uttrakhand, Dehradun, India; Chauhan A., CHRIST (Deemed to Be University), Department of Life Sciences, Karnataka, Bengaluru, India; Acharjee P.B., CHRIST University, Faculty Member, Computer Science, Maharashtra, Pune, India
Maximum Decision Support Regression-Based Advance Secure Data Encrypt Transmission for Healthcare Data Sharing in the Cloud Computing
ASDET; Cloud computing; Delay; Energy; Health care; Machine learning; Malicious node; MDSR; NEHR; Security
The recent growth of cloud computing has led to most companies storing their data in the cloud and sharing it efficiently with authorized users. Health care is one of the initiatives to adopt cloud computing for services. Both patients and healthcare providers need to have access to patient health information. Healthcare data must be shared and maintained more securely. While transmitting health data from sender to receiver through intermediate nodes, intruders can create falsified data at intermediate nodes. Therefore, security is a primary concern when sharing sensitive medical data. It is thus challenging to share sensitive data in the cloud because of limitations in resource availability and concerns about data privacy. Healthcare records struggle to meet the needs of security, privacy, and other regulatory constraints. To address these difficulties, this novel proposes a machine learning-based Maximum Decision Support Regression (MDSR)-based Advanced Secure Data Encrypt Transmission (ASDET) approach for efficient data communication in cloud storage. Initially, the proposed method analyzed the node's trust, energy, delay, and mobility using Node Efficiency Hit Rate (NEHR) method. Then identify the efficient route using an Efficient Spider Optimization Scheme (ESOS) for healthcare data sharing. After that, MDSR analyzes the malicious node for efficient data transmission in the cloud. The proposed Advanced Secure Data Encrypt Transmission (ASDET) algorithm is used to encrypt the data. ASDET achieved 92% in security performance. The proposed simulation result produces better performance compared with PPDT and FAHP methods. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Anusuya V.; Bejoy B.J.; Ramkumar M.; Shanmugaraja P.; Dhiyanesh B.; Kiruthiga G.
Lecture Notes in Networks and Systems, Vol-672 LNNS, pp. 39-51.
Anusuya V., Associate Professor/CSE, Ramco Institute of Technology, Rajapalayam, India; Bejoy B.J., Assistant Professor/CSE, CHRIST (Deemed to Be University), Bangalore, India; Ramkumar M., Associate Professor/CSBS, Knowledge Institute of Technology, Salem, India; Shanmugaraja P., Associate Professor/IT, Sona College of Technology, Salem, India; Dhiyanesh B., Associate Professor/CSE, Dr. N.G.P. Institute of Technology, Coimbatore, India; Kiruthiga G., Associate Professor/CSE, IES College of Engineering, Thrissur, India