A Unified Approach to Predict and Understand Acute Myeloid Leukemia Diagnosis
Acute myeloid leukemia; Classification; Explainable AI; LIME; SHAP feature importance
Acute myeloid leukemia (AML) is a rapidly progressing disease that affects myeloid cells in blood and bone marrow. These abnormal cancerous cells called blast cells are non-functional cells that increase rapidly in bone marrow and are released into blood stream which crowd out the healthy functional cells leading to weak immune system. This life-threatening disease needs to be diagnosed at early stage and hence requires fully automated system for early detection of leukemia to aid pathologists and doctors. Most of the automated machine learning and AI models are not transparent and require techniques to explain model prediction. This paper presents methods to classify blood microscopic images into healthy or acute myeloid leukemia. Among all the methods implemented, Gradient Boosting outperforms with an accuracy of 96.67%. This paper also focuses on explainable AI to interpret model prediction and feature importance which further helps in understanding decision-making process of classification model and optimize it. 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Lecture Notes in Electrical Engineering, Vol-1098, pp. 233-242.
Springer Science and Business Media Deutschland GmbH
Patil A.P., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Hiremath M., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Pawar V., Department of Computer Applications, CMR Institute of Technology, Bengaluru, India
Quantitative assessment of blockchain applications for Industry 4.0 in manufacturing sector
Blockchain; Industry 4.0; Manufacturing; Technology
Blockchain is one of the emerging digital technologies that will play a role in the breakthroughs of the fourth industrial revolution. The use of blockchain technology has the potential to greatly benefit businesses of all sizes by increasing their data's integrity, privacy, and openness. The term Industry 4.0? refers to the amalgamation of recent advances in manufacturing technology that have helped businesses cut production times significantly. The industrial and supply chain industries can benefit from these technological advancements in a number of ways. Increased efficiency in production and a more stable supply chain are just two of the many benefits that blockchain promises to bring to the manufacturing industry. The study focuses on Blockchain's huge potential in the context of Industry 4.0. Understanding the role of Blockchain technology in Industry 4.0 is examined, along with its various drivers, enablers, and associated capabilities. The several sub-domains of Industry 4.0 that can benefit from the implementation of Blockchain technology are also covered. The present research is primary and exploratory in nature. The sample size of the study is 256. The responses obtained from workers working in manufacturing sector in Delhi/NCR. The responses from workers obtained through structured questionnaire. The several sub-domains of Industry 4.0 found that can benefit from the implementation of Blockchain technology. At last, the existing study found the most important uses of Blockchain technology in the fourth industrial revolution. 2023
Patil B.S.; .K. Sharma M.; Soubhari T.; Ashok J.; Pandey V.; Joshi G.
https://doi.org/10.1016/j.matpr.2023.04.366 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153929728&doi=10.1016%2fj.matpr.2023.04.366&partnerID=40&md5=e958c626505b1a841877c7278707fae5
Patil B.S., Dayananda Sagar Business School, Karnataka, Bengaluru, India; .K. Sharma M., Department of Mathematics, Chaudhary Charan Singh University, U.P, Meerut, India; Soubhari T., N.S.S. College, Manjeri, University of Calicut, Kerala, India; Ashok J., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Pandey V., Department of Management, Amity University, Bihar, Patna, India; Joshi G., Lal Bahadur Shastri Institute of Management, New Delhi, India
A Comparison of Similarity Measures in an Online Book Recommendation System
Books; Collaborative filtering; Machine learning; Recommendation system; Similarity; User
To assist users in identifying the right book, recommendation systems are crucial to e-commerce websites. Methodologies that recommend data can lead to the collection of irrelevant data, thus losing the ability to attract users and complete their work in a swift and consistent manner. Using the proposed method, information can be used to offer useful information to the user to help enable him or her to make informed decisions. Training, feedback, management, reporting, and configuration are all included. Our research evaluated user-based collaborative filtering (UBCF) and estimated the performance of similarity measures (distance) in recommending books, music, and goods. Several years have passed since recommendation systems were first developed. Many people struggle with figuring out what book to read next. When students do not have a solid understanding of a topic, it can be difficult determining which textbook or reference they should read. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Lecture Notes on Data Engineering and Communications Technologies, Vol-131, pp. 341-349.
Patil D., Christ University, Bengaluru, India; Preethi N., Christ University, Bengaluru, India
Investigation on AI-Based Techniques in Applications for Detecting Fatal Traffic Accidents
IoT; Road Accidents; Smart vehicle systems and Accident detection systems; Traffic
The difficulties with road accident rates today rank among the top concerns for health and social policy in nations across the continents. In this essay, we've spoken about the fatalities and injuries brought on by traffic accidents in several Indian states. We have also shed light on the various factors that contribute to traffic accidents. Many researchers have reported various methods for identifying automobile crashes or accidents that are discussed in this work. Additionally, we covered collision avoidance systems and their various kinds. An examination of the analysis techniques used to comprehend the numerous causes causing accidents is also included in the study. Traditional models are frequently used to identify problems such driver weariness, drowsiness, driving while intoxicated, and distractions. 2023 IEEE.
2023 3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/eSmarTA59349.2023.10293467 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178142009&doi=10.1109%2feSmarTA59349.2023.10293467&partnerID=40&md5=1ffedf00e66cfc460f3942ec032537e8
Patil H.P., Symbiosis Skills and Professional University, Dept. of Mechatronics, Pune, India; Upreti K., CHRIST(Deemed to Be University), Dept. of Computer Science, Ghaziabad, India; Poonia R.C., CHRIST(Deemed to Be University), Dept. of Computer Science, Ghaziabad, India
Concept Drift Detection for Social Media: A Survey
real-time data; streaming data; temporal data; Twitter
The research over information retrieval from social media data has progressed for streaming data since the last decade. Recently, academic researchers have witnessed users' changing topics, trends, and intent on social media. This change of information with time takes into account the temporal attribute for real-time data, and thus, advances in this domain are exponentially growing. Although concept drift is still not explored due to a shortage of available datasets, concept drift for social media is minimally explored. This manuscript makes attempts to identify the types of concept drift for social media data, discuss the historical perspective of concept drift on social media, and enlist the possible research directions. 2021 IEEE.
Proceedings - 2021 3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021, pp. 12-16.
https://doi.org/10.1109/ICAC3N53548.2021.9725548 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126983671&doi=10.1109%2fICAC3N53548.2021.9725548&partnerID=40&md5=a6790049bf2d2a26a22e0caba697de7c
Patil M.A., Amity University Rajasthan, Amity School of Engineering Technology, Jaipur, India; Kumar S., Amity University Rajasthan, Amity School of Engineering Technology, Jaipur, India; Kumar S., CHRIST (Deemed to Be University), Karnataka, Bangalore, 560029, India; Garg M., Amity University Rajasthan, Amity School of Engineering Technology, Jaipur, India
Enhanced radial basis function neural network for tomato plant disease leaf image segmentation
Enhanced radial basis function; Feature extraction; Modified sunflower optimization; Segmentation; Tomato leaf disease
Primary crop losses in agriculture are due to leaf diseases, which farmers cannot identify early. If the diseases are not detected early and correctly, then the farmer will have to undergo huge losses. Therefore, in the field of agriculture, the detection of leaf diseases in tomato crops plays a vital role. Recent advances in computer vision and deep learning techniques have made disease prediction easy in agriculture. Tomato crop front side leaf images are considered for research due to their high exposure to diseases. The image segmentation process assumes a significant role in identifying disease affected areas on tomato leaf images. Therefore, this paper develops an efficient tomato crop leaf disease segmentation model using an enhanced radial basis function neural network (ERBFNN). The proposed ERBFNN is enhanced using the modified sunflower optimization (MSFO) algorithm. Initially, the noise present in the images is removed by a Gaussian filter followed by CLAHE (contrast-limited adaptive histogram equalization) based on contrast enhancement and un-sharp masking. Then, color features are extracted from each leaf image and given to the segmentation stage to segment the disease portion of the input image. The performance of the proposed ERBFNN approach is estimated using different metrics such as accuracy, Jaccard coefficient (JC), Dice's coefficient (DC), precision, recall, F-Measure, sensitivity, specificity, and mean intersection over union (MIoU) and are compared with existing state-of-the-art methods of radial basis function (RBF), fuzzy c-means (FCM), and region growing (RG). The experimental results show that the proposed ERBFNN segmentation model outperformed with an accuracy of 98.92% compared to existing state-of-the-art methods like RBFNN, FCM, and RG, as well as previous research work. 2022 Elsevier B.V.
https://doi.org/10.1016/j.ecoinf.2022.101752 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135888868&doi=10.1016%2fj.ecoinf.2022.101752&partnerID=40&md5=fae568c73ea3f425f0a62d0a0e3c1e1b
Patil M.A., Research Scholar, Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India, Assistant Professor, Department of Information Technology, Vasavi College of Engineering, Ibrahimbagh, Telangana, Hyderabad, 500031, India; M M., Associate Professor, Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India
Plant Leaf Disease Classification Using Optimal Tuned Hybrid LSTM-CNN Model
CNN; Deep Learning; Hybrid classifier; LSTM; SISS-OB
Tomatoes are widely cultivated and consumed worldwide and are susceptible to various leaf diseases during their growth. Therefore, early detection and prediction of leaf diseases in tomato crops are crucial. Farmers can take proactive measures to prevent the spread and minimize the impact on crop yield and quality by identifying leaf diseases in their early stages. Several Machine Learning (ML) and Deep Learning (DL) frameworks have been developed recently to identify leaf diseases. This research presents an efficient deep-learning approach based on a hybrid classifier by optimizing the CNN and LSTM models, which helps to enhance classification accuracy. Initially, Median Filtering (MF) is used for leaf image pre-processing. Then, an improved watershed approach is used for segmenting the leaf images. Subsequently, enhanced Local Gabor Pattern (LGP) and statistical and color features are extracted. An optimized CNN and LSTM are used for classification, and the weights are tuned using the SISS-OB (Self Improved Shark Smell With Opposition Behavior) algorithm. Finally, we have analyzed the performance using various measures. Since we have done segmentation, feature extraction, and optimization improvisations, our proposed methodology results are higher than other available methods and existing works. The results obtained at Learning Percentage (LP) is 90% which is far superior to those obtained at other LPs. The FNR (False Negative Rate) is much lower (0.05) at the 90th LP. The proposed model achieved better classification performance in terms of Accuracy of 97.13%, Sensitivity of 95.09%, Specificity of 95.24%, Precision of 94.31%, F measure of 96.71% and MCC 87.34%. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
Patil M.A., Department of Computer Science and Engineering, Christ (Deemed to be University) School of Engineering and Technology, Kengeri, Karnataka, Bengaluru, 560074, India, Department of Computer Science and Engineering, G. Narayanamma Institute of Technology and Science, Shaikpet, Telangana, Hyderabad, 500104, India; Manohar M., Department of Computer Science and Engineering, Christ (Deemed to be University) School of Engineering and Technology, Kengeri, Karnataka, Bengaluru, 560074, India
Potato Leaf Disease Identification using Hybrid Deep Learning Model
CNN; leaf disease; LSTM; OCNN-LSTM; Potato
The potato is one of the most significant crops in the world. However, it is prone to several leaf diseases that can result in significant productivity losses, leading to economic challenges. Early and precise disease identification is essential for sensitive crops like potatoes. Deep learning approaches have demonstrated excellent potential in image-based disease classification tasks in recent years. This paper presents a hybrid strategy for classifying potato leaf image diseases by integrating Optimised Convolutional Neural Network (OCNN) and Long Short-Term Memory (LSTM) networks. The Adaptive Shark Smell Optimisation (ASSO) technique is used to optimize the weights of CNN models. The CNN component is initially used to extract pertinent characteristics from Potato leaves, capturing significant visual patterns related to various diseases. These extracted features are then fed into the LSTM model, which utilizes its sequential learning capability to model the temporal dependencies among the extracted features. The model performance is analyzed based on Accuracy, Precision, Recall, and F1-score criteria. Experimental results showed that the hybrid OCNN-LSTM model outperforms the individual CNN, LSTM, and MobileNet models. The proposed model results are compared with existing state-of-the-art work, and it was found that the OCNN-LSTM model performed better and received 99.02% accuracy. 2023 IEEE.
2023 International Conference on Network, Multimedia and Information Technology, NMITCON 2023
Patil M.A., School O F Engineering and Technology, Dept, of Computer Science and Engineering Christ (Deemed to Be University), Bengaluru, India; Manohar M., School O F Engineering and Technology, Dept, of Computer Science and Engineering Christ (Deemed to Be University), Bengaluru, India
A Comprehensive Review onCrop Disease Prediction Based onMachine Learning andDeep Learning Techniques
Classification; Crop disease; DL; Leaf image; ML; Segmentation
Leaf diseases cause direct crop losses in agriculture, and farmers cannot detect the disease early. If the diseases are not detected early and correctly, the farmer must undergo huge losses. It may lead to the wrong pesticide or over pesticide, directly affect crop productivity and economy, and indirectly affect human health. Sensitive crops have various leaf diseases, and early prediction of these diseases remains challenging. This paper reviews several machine learning (ML) and deep learning (DL) methods used for different crop disease segmentation and classification. In the last few years, computer vision and DL techniques have made tremendous progress in object detection and image classification. The study summaries the available research on different diseases on various crops based on machine learning (ML) and deep learning (DL) techniques. It also discusses the data sets used for research and the accuracy and performance of existing methods. It does mean that the methods and available data sets presented in this paper are not projected to replace published solutions for crop disease identification, perhaps to enhance them by finding the possible gaps. Seventy-five articles are analysed and reviewed to find essential issues that involve additional study for future research in this domain to promote continuous progress for data sets, methods, and techniques. It mainly focuses on image segmentation and classification techniques used to solve agricultural problems. Finally, this paper provides future research scope and challenges, limitations, and research gaps. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Lecture Notes in Networks and Systems, Vol-608, pp. 481-503.
Patil M.A., Department of Computer Science and Engineering, School of Engineering and Technology, Christ (Deemed to be University), Karnataka, Bangalore, 560074, India, Department of Information Technology, Vasavi College of Engineering, Telangana, Hyderabad, 500032, India; Manohar M., Department of Computer Science and Engineering, School of Engineering and Technology, Christ (Deemed to be University), Karnataka, Bangalore, 560074, India
Hybrid Deep Learning-Based Potato andTomato Leaf Disease Classification
Crop disease; Hybrid model; MobileNet; OCNN-OLSTM; ResNet50
Predicting potato and tomato leaf disease is vital to global food security and economic stability. Potatoes and tomatoes are among the most important staple crops, providing essential nutrition to millions worldwide. However, many tomato and potato leaf diseases can seriously reduce food productivity and yields. We are proposing a hybrid deep learning model that combines optimized CNN (OCNN) and optimized LSTM (OLSTM). The weight values of LSTM and CNN models are optimized using the modified raindrop optimization (MRDO) algorithm and the modified shark smell optimization (MSSO) algorithm, respectively. The OCNN model is used to extract potato leaf image features and then fed into the OLSTM model, which handles data sequences and captures temporal dependencies from the extracted features. Precision, recall, F1-score, and accuracy metrics are used to analyze the output of the proposed OCNN-OLSTM model. The experimental performance is compared without optimizing the CNN-LSTM model, individual CNN and LSTM models, and existing MobileNet and ResNet50 models. The presented model results are compared with existing available work. We have received an accuracy of 99.25% potato and 99.31% for tomato. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Patil M.A.; Manohar M.; Laxuman C.; Parane K.; Dodamani B.M.; Sunkad G.
Lecture Notes in Networks and Systems, Vol-891, pp. 157-174.
Patil M.A., Department of Computer Science and Engineering, Christ (Deemed to be University) School of Engineering and Technology, Kengeri, Karnataka, Bengaluru, 560074, India, Department of Computer Science and Engineering, G. Narayanamma Institute of Technology and Science, Shaikpet, Telangana, Hyderabad, 500104, India; Manohar M., Department of Computer Science and Engineering, Christ (Deemed to be University) School of Engineering and Technology, Kengeri, Karnataka, Bengaluru, 560074, India; Laxuman C., Zonal Agricultural Research Station, UAS-R, Karnataka, Kalaburagi, 585101, India; Parane K., NITK Surathkal, Karnataka, Surathkal, India; Dodamani B.M., Zonal Agricultural Research Station, UAS-R, Karnataka, Kalaburagi, 585101, India; Sunkad G., University of Agricultural Sciences, Karnataka, Raichur, 584104, India
Sensitive crop leaf disease prediction based on computer vision techniques with handcrafted features
Agricultural production is considered the primary source of the economy of many countries. Tomato and Potatoes are the most sensitive and consumable vegetables worldwide. However, during the growth of these crops, they suffer from many leaf diseases, which lead to loss of productivity and economy of the farmers. Many farmers detect and find plant diseases that are more time-consuming, expensive, and require expert decisions following the naked eye method. Therefore, early and accurate diagnosis of Tomato and Potato crops leaf diseases plays a vital role in sustainable agriculture. So, this research paper proposes an efficient leaf disease classification model based on computer vision techniques. The proposed Adaptive Deep Neural Network (ADNN) leaf disease classification method is a hybrid model which combines an optimized long short-term memory (OLSTM) and convolution neural network (CNN). The weight values supplied in the LSTM classifier are optimally selected using the Adaptive Raindrop Optimization algorithm. The handcrafted features are extracted from the segmented image and fused with the hybrid deep neural network to improve the classifier performance. The ADNN method consists of five steps: preprocessing, feature extraction, segmentation, handcrafted feature extraction, and classification. At first, the images are given to the preprocessing stage to remove the noise from leaf images. Then, the image-affected portion is segmented using an enhanced radial basis function neural network. After the segmentation process, the segmented image is given as an input to the adaptive deep neural network (ADNN) that classifies various types of diseases in the Potato and Tomato leaves. The efficiency of the ADNN model based on the OLSTM-CNN approach is determined concerning multiple metrics, namely Accuracy, Precision, Recall, F-measure, Specificity, and Sensitivity. The ADNN model achieved the best Accuracy of 98.02% for Tomatoes and 98% for Potatoes. The ADNN is compared with existing state-of-the-art CNN, LSTM, ResNet50, and MobileNet techniques. The performance analysis proved that the ADNN model improved efficiency in terms of all metrics and methods. 2023, The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden.
International Journal of System Assurance Engineering and Management, Vol-14, No. 6, pp. 2235-2266.
Patil M.A., Department of Computer Science and Engineering, Christ(Deemed to be University) School of Engineering and Technology, Kengeri, Karnataka, Bengaluru, 560074, India, Department of Computer Science and Engineering, G. Narayanamma Institute of Technology and Science, Shaikpet, Telangana, Hyderabad, 500104, India; Manur M., Department of Computer Science and Engineering, Christ(Deemed to be University) School of Engineering and Technology, Kengeri, Karnataka, Bengaluru, 560074, India
Smart Attendance Management System using IoT
Biometrics authentication; IoT; MySQLDB; PiCam; Raspberry Pi
Taking student attendance is mandatory in an educational organization, and maintaining those attendance plays a vital role. The conventional way of taking student attendance in any institution is time-consuming and challenging, because in the conventional procedure taking attendance/Roll call is performed manually by calling student names as per their roll numbers and marking 'absent(A)' or 'present(P)' on the attendance/logbook accordingly in every class per day. To improve teaching efficiency/teaching time in classrooms by reducing the time required for Roll call's, we have proposed a biometric student attendance system based on IoT. The proposed system records students' attendance using the facial-based biometric system and stores the attendance details on the server through the internet. In this system, the Raspberry pi camera captures the student face images and compares them with the stored images in the database. If the captured image is comparable with the stored image, then the student's attendance is recorded on the remote server as a present(P) in class; otherwise, attendance is recorded as absent (A). The developed system has been tested for sample classes, and the results proved that the system is simple, cost-effective, and portable for managing students' attendance. 2022 IEEE.
Patil M.A.; Parane K.; Sivaprasad D.D.; Poojara S.; Lamani M.R.
2022 2nd International Conference on Computer Science, Engineering and Applications, ICCSEA 2022
https://doi.org/10.1109/ICCSEA54677.2022.9936433 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142369430&doi=10.1109%2fICCSEA54677.2022.9936433&partnerID=40&md5=05e8bead475330ef500fc8728f1e3ef4
Patil M.A., Vasavi College of Engineering, Department of It, Hyderabad, Telangana, India; Parane K., Nit Surathkal, Dept. of Cse, Karnataka, India; Sivaprasad D.D., Kampala International University, School of Computing and It, Kampala, Uganda; Poojara S., Institute of Cs, University of Tartu, Tartu, Estonia; Lamani M.R., Christ (Deemed to Be University), Kengeri Campus, Dept. of Cse, Bangalore, Karnataka, India
A colorimetric chemosensor for distinct color change with (E)-2-(1-(3-aminophenyl)ethylideneamino)benzenethiol to detect Cu2+ in real water samples
Chemosensor; Colorimetry; DFT studies; Schiff base; Water samples
The study reports the synthesis of chemosensor (E)-2-(1-(3-aminophenyl)ethylideneamino)benzenethiol (C1), a highly sensitive, colorimetric metal probe that shows distinct selectivity for the detection of Cu2+ ion in various real water samples. Upon complexation with Cu2+ in CH3OH/H2O (60:40 v/v) (aqueous methanol), the C1 demonstrate significant enhancement in the absorption at 250nm and 300nm with a color change from light yellow to brown which was visualized using naked-eye. Therefore, these properties make C1 as an effective candidate for on-site Cu2+ ions detection. The emission spectrum of C1 illustrated TURN-ON recognition of Cu2+ with a limit of detection (LOD) of 46nM. Furthermore, Density Functional Theory (DFT) calculations were performed to better understand the interactions between C1 and Cu2+. The obtained results suggested that the electron clouds present around the NH2 innitrogen and sulfur in SH play a pivotal role in the formation of a stable complex. The computational results were in good agreement with the experimental UVvisible spectrometry results. Graphical abstract: [Figure not available: see fulltext.] 2023, The Author(s), under exclusive licence to The Japan Society for Analytical Chemistry.
Patil N.; Dhake R.B.; Phalak R.; Fegade U.; Inamuddin; Naushad M.; Bathula C.; Kanchi S.; Govender K.
Analytical Sciences, Vol-39, No. 8, pp. 1413-1423.
ISSN: 9106340; PubMed ID: 37280486; CODEN: ANSCE
https://doi.org/10.1007/s44211-023-00355-z https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160933423&doi=10.1007%2fs44211-023-00355-z&partnerID=40&md5=b1ff4230be21abf2ed537c6e61f3854a
Patil N., Department of Chemistry, D. D. N. Bhole College, Bhusawal, MH, Jalgaon, 425201, India, Department of Chemistry, Bhusawal Arts, Science and P. O. Nahata Commerce College, Bhusawal, MH, Jalgaon, 425201, India; Dhake R.B., Department of Chemistry, D. D. N. Bhole College, Bhusawal, MH, Jalgaon, 425201, India; Phalak R., Department of Chemistry, D. D. N. Bhole College, Bhusawal, MH, Jalgaon, 425201, India; Fegade U., Department of Chemistry, Bhusawal Arts, Science and P. O. Nahata Commerce College, Bhusawal, MH, Jalgaon, 425201, India; Inamuddin, Advanced Functional Materials Laboratory, Department of Applied Chemistry, Zakir Husain College of Engineering and Technology, Faculty of Engineering and Technology, Aligarh Muslim University, Aligarh, 202002, India; Naushad M., Department of Chemistry, College of Science, King Saud University, P.O. Box 2455, Riyadh, Saudi Arabia; Bathula C., Division of Electronics and Electrical Engineering, Dongguk University-Seoul, Seoul, 04620, South Korea; Kanchi S., Department of Chemistry, CHRIST (Deemed to be University), Bengaluru, 560 029, India; Govender K., Department of Chemical Sciences, Doornfontein Campus, University of Johannesburg, P. O. Box 17011, Johannesburg, 2028, South Africa, National Institute for Theoretical and Computational Sciences (NITheCS), Stellenbosch, South Africa
Liberalisation and cashew industry: evidence from India (1965 to 2018)
cashew industry; India; liberalisation; regression
We examine the impact of liberalisation on production, import, export and area under cultivation of cashew industry in India during 1965 to 2018 period using regression method. We divide data into two sub-periods. The liberalisation and pre-liberalisation period is from 1965 to 1991 and the post-liberalisation period covers the period from 1992 to 2018. We find that cashew production is not influenced post trade liberalisation. This study also finds trade liberalisation has a significant and positive impact on export. Further, we reveal an insignificant impact of liberalisation on import. This study show that the area under cultivation is not changed after the trade liberalisation. 2024 Inderscience Publishers. All rights reserved.
International Journal of Business and Globalisation, Vol-36, No. 4, pp. 373-380.
https://doi.org/10.1504/IJBG.2024.138015 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191088219&doi=10.1504%2fIJBG.2024.138015&partnerID=40&md5=20d69b715e2a57dae6c69eb2a089fcd8
Patil P., Ministry of Commerce and Industry (APEDA), Government of India, India; Faniband M., Department of Commerce, CHRIST (Deemed to be University, Bengaluru, 560029, India
Dandelion Algorithm for Optimal Location and Sizing of Battery Energy Storage Systemsin Electrical Distribution Networks
Dandelion algorithm; Energy storage system; Loss minimization; Optimization; Radial distribution network
This paper describes a new way to improve the performance of an EDN by integrating distributed battery energy storage systems (BESs) in the best way possible. This method is based on the Dandelion Algorithm (DA). The search space for BES locations is first predetermined using loss sensitivity factors (LSFs), and then DA is used to determine the optimal locations and sizes. The reduction of real power distribution loss is regarded as the primary objective function, and the impact of BESs is extended to examine the network voltage profile, voltage stability, and GHG emissions. IEEE 33-busEDN is used to calculate the computational efficiency of LSF-DA. Results show that DA is more efficient than Archimedes optimization (AOA), future search algorithm(FSA), pathfinder algorithm(PFA), and butterfly optimization algorithm(BOA) algorithms. Furthermore, the results show that the proposed DA enhances all technological and environmental factors and RDN performance. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Studies in Computational Intelligence, Vol-1117, pp. 315-323.
Patil R., SKN Sinhgad College of Engineering, Maharashtra, Pamdharpur, India; Janamala V., CHRIST (Deemed to be University), Karnataka, Bangalore, India
Advancements in Deep Learning Techniques for Potato Leaf Disease Identification Using SAM-CNNet Classification
convolutional neural network; data normalization; GoogLeNet; potato leaf diseases; Spatial Attention Mechanism
Potato leaf diseases like Late Blight and Early Blight significantly challenge potato cultivation, impacting crop yield and quality worldwide. Potatoes are a staple for over a billion people and crucial for food security, especially in developing countries. The economic impact is substantial, with Late Blight alone causing annual damages over $6 billion globally. Effective detection and management are essential to mitigate these effects on agricultural productivity and economic stability. This paper presents a novel approach to potato leaf disease detection using advanced deep learning and optimization techniques. Key components include data normalization to eliminate noise, feature extraction using GoogLeNet, and hyperparameter tuning through the Elk Herd Optimizer (EHO). Additionally, a Spatial Attention Mechanism and Convolutional Neural Network (SAM-CNNet) are employed for robust classification. The method is validated using the Plant Village dataset, yielding an accuracy of 98.58%, with precision of 97.68%, recall of 98.42%, and F1-Score of 98.21%, demonstrating exceptional performance and reliability. This study highlights the proposed approach's efficacy in accurately identifying and classifying potato leaf diseases, offering a promising solution for precision agriculture and crop management. Copyright: 2024 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license.
Patil S.; Satya A.D.V.; Bajjuri U.R.; Damarapati P.K.; Manur M.; Thirumalraj A.; Vatambeti R.
Ingenierie des Systemes d'Information, Vol-29, No. 5, pp. 2021-2030.
International Information and Engineering Technology Association
All Open Access; Hybrid Gold Open Access
https://doi.org/10.18280/isi.290533 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207450988&doi=10.18280%2fisi.290533&partnerID=40&md5=1a9b37d4fe16dfe4c3d8ee4fe647d79a
Patil S., Department of Information Technology, MLR Institute of Technology, Hyderabad, 500043, India; Satya A.D.V., Department of Chemistry, New Horizon College of Engineering, Bengaluru, 560103, India; Bajjuri U.R., Department of Computer Science and Engineering, Lakireddy Bali Reddy College of Engineering, Mylavaram, 521230, India; Damarapati P.K., Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur, 522302, India; Manur M., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, 560074, India; Thirumalraj A., Department of Computer Science and Engineering, K. Ramakrishna College of Technology, Trichy, 621112, India; Vatambeti R., School of Computer Science and Engineering, VIT-AP University, Vijayawada, 522237, India
Text Mining-A Comparative Review of Twitter Sentiments Analysis
airline sentiments; decision trees; gaussian naive bayes; gini index; machine learning; multinomial naive bayes; multinomial naive bayes with bagging; Opinion mining; random forest
Background: Text mining derives information and patterns from textual data. Online social media platforms, which have recently acquired great interest, generate vast text data about human behaviors based on their interactions. This data is generally ambiguous and unstructured. The data includes typing errors and errors in grammar that cause lexical, syntactic, and semantic uncertainties. This results in incorrect pattern detection and analysis. Researchers are employing various text mining techniques that can aid in Topic Modeling, the detection of Trending Topics, the identification of Hate Speeches, and the growth of communities in online social media net-works. Objective: This review paper compares the performance of ten machine learning classification techniques on a Twitter data set for analyzing users' sentiments on posts related to airline usage. Methods: Review and comparative analysis of Gaussian Naive Bayes, Random Forest, Multinomial Naive Bayes, Multinomial Naive Bayes with Bagging, Adaptive Boosting (AdaBoost), Optimized AdaBoost, Support Vector Machine (SVM), Optimized SVM, Logistic Regression, and Long-Short Term Memory (LSTM) for sentiment analysis. Results: The results of the experimental study showed that the Optimized SVM performed better than the other classifiers, with a training accuracy of 99.73% and testing accuracy of 89.74% compared to other models. Conclusion: Optimized SVM uses the RBF kernel function and nonlinear hyperplanes to split the dataset into classes, correctly classifying the dataset into distinct polarity. This, together with Feature Engineering utilizing Forward Trigrams and Weighted TF-IDF, has improved Optimized SVM classifier performance regarding train and test accuracy. Therefore, the train and test accuracy of Optimized SVM are 99.73% and 89.74% respectively. When compared to Random Forest, a mar-ginal of 0.09% and 1.73% performance enhancement is observed in terms of train and test accuracy and 1.29% (train accuracy) and 3.63% (test accuracy) of improved performance when compared with LSTM. Likewise, Optimized SVM, gave more than 10% of enhanced performance in terms of train accuracy when compared with Gaussian Nae Bayes, Multinomial Nae Bayes, Multinomial Nae Bayes with Bagging, Logistic Regression and a similar enhancement is observed with Ada-Boost and Optimized AdaBoost which are ensemble models during the experimental process. Optimized SVM also has outperformed all the classification models in terms of AUC-ROC train and test scores.. 2024 Bentham Science Publishers.
Patil S.; Subil D.; Nasar N.; Kokatnoor S.A.; Krishnan B.; Kumar S.
Recent Advances in Computer Science and Communications, Vol-17, No. 1, pp. 21-37.
Patil S., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, 74, Bangalore, India; Subil D., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, 74, Bangalore, India; Nasar N., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, 74, Bangalore, India; Kokatnoor S.A., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, 74, Bangalore, India; Krishnan B., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, 74, Bangalore, India; Kumar S., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, 74, Bangalore, India
Lattice thermal conduction in suspended molybdenum disulfide monolayers with defects
Grain boundary scattering; Lattice thermal conductivity; MoS 2 monolayer ; N-process; Vacancy scattering
In this study, we investigated the effect of lattice defects comprising vacancies and boundaries on the lattice thermal conductivity (LTC), ? p , of suspended molybdenum disulfide monolayers (MLs) over a wide temperature range (1 < T < 500 K). Using the phonon Boltzmann formalism, the acoustic phonons were considered to be scattered by the sample and grain boundaries, isotopic impurities, vacancies, and other phonons via Umklapp and normal (N-) processes. ? p was evaluated using a modified Callaway model by considering the in-plane longitudinal acoustic and transverse acoustic phonons, and out-of-plane flexural acoustic phonon modes. We demonstrated the need to include the often neglected non-resistive N-processes when evaluating the LTC. Numerical calculations of the temperature dependence of the LTC for crystalline and polycrystalline MoS 2 MLs showed the dominance of sample-dependent scattering mechanisms at low temperatures (T < 100 K) and of phonon-phonon scattering at higher temperatures, where the N-processes played an important role. The effects of vacancies and boundaries were to alter the behavior and suppress the magnitude of the LTC. The suppression due to vacancies was greater in crystalline MLs with specular surfaces and in polycrystalline MLs with larger grain sizes. The calculations compared well with recent thermal conductivity data obtained for polycrystalline samples. The need for further investigations is suggested. 2018 Elsevier Ltd
Patil S.B.; Sankeshwar N.S.; Mulimani B.G.
Journal of Physics and Chemistry of Solids, Vol-129, pp. 31-40.
Patil S.B., Department of Physics, Karnatak University, Dharwad, 580003, Karnataka, India; Sankeshwar N.S., Department of Physics, Karnatak University, Dharwad, 580003, Karnataka, India, Department of Physics, Christ University, Bangalore, 560029, Karnataka, India; Mulimani B.G., Department of Physics, Karnatak University, Dharwad, 580003, Karnataka, India
Effect of vacancies on thermopower of molybdenum disulfide monolayers
monolayer MoS2 ; thermopower; vacancies
A detailed theoretical investigation of the effect of scattering of electrons and phonons by lattice vacancies in molybdenum disulfide (MoS2) monolayers (MLs) on diffusion, S d, and phonon-drag, S g, components of thermoelectric power (TEP), S, is presented over a wide-temperature range (1 < T < 300 K) using the Boltzmann transport formalism. The diffusion component is assumed to be influenced, not only by vacancies via short-range and Coulomb disorder scattering, but also by charged impurities (CIs) and acoustic and optical phonons. In the case of S g, the phonons are considered to be scattered, besides the vacancies, by sample boundaries, substitutional isotopic impurities, as well as other phonons via both N- and U-processes. Numerical calculations of S d and S g, as functions of temperature and vacancy defect density are presented for MoS2 MLs with n s = 1017 m-2 supported on SiO2/Si substrates. The role of carrier scatterings by mono-sulfur and mono-molybdenum vacancies in influencing the overall electron and phonon relaxation rates and in determining S d and S g are investigated. The behavior of S d and S g is found to be noticeably influenced by vacancy scattering. The influence on S d is seen to be more for mono-sulfur vacancies for densities lesser than 1%. The influence, is to enhance S d slightly for MLs with realizable CI concentrations. On the other hand, S g is found to depend sensitively on the vacancy disorder for T < 50 K; a S-vacancy density of 0.1% is found to suppress the characteristic peak of S g by almost 60%. The extent of reduction in the characteristic peak of S g, observable in low temperature measurements of S, can provide information about defect density. The calculations demonstrate that defect engineering of MoS2 ML systems can be used to tune their thermoelectric performance. A need for detailed experimental studies is suggested. 2018 IOP Publishing Ltd.
Journal of Physics D: Applied Physics, Vol-51, No. 30
https://doi.org/10.1088/1361-6463/aaccc8 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049966430&doi=10.1088%2f1361-6463%2faaccc8&partnerID=40&md5=ced981d1ec5629fa2a5ca547b0d4f373
Patil S.B., Department of Physics, Karnatak University, Dharwad, Karnataka, 580003, India; Sankeshwar N.S., Department of Physics, Karnatak University, Dharwad, Karnataka, 580003, India, Department of Physics, Christ University, Bangalore, Karnataka, 560029, India; Mulimani B.G., Department of Physics, Karnatak University, Dharwad, Karnataka, 580003, India
Role of charged impurities in thermoelectric transport in molybdenum disulfide monolayers
charged impurities; electrical conductivity; electronic thermal conductivity; MoS2; Thermoelectric power
A theoretical study of the electronic properties, namely, electrical conductivity (EC), electronic thermal conductivity (ETC) and thermoelectric power (TEP) in 2D MoS2 monolayers (MLs), over a wide range of temperatures (10 < T < 300 K), is presented employing Boltzmann transport formalism. Considering the electrons to be scattered by screened charged impurities and the acoustic, optical and remote phonons, the transport equation is solved using Ritz iterative method. Numerical calculations of EC, ETC and TEP presented for supported and free-standing MLs with high electron concentrations, as a function of temperature, bring out the relative importance of the various scattering mechanisms operative. The role of CIs, with regard to both concentration and separation from the substrate-ML interface, in determining the properties of supported MLs is demonstrated for the first time. Validity of Wiedemann-Franz law and Mott formula are examined for supported and free standing MLs. Calculations are in consonance with recent experimental data on mobility and TEP of exfoliated SiO2-supported MoS2 ML samples. In the case of TEP it is found that though the diffusion contribution is dominant the inclusion of the drag component, incorporating contributions from all relevant phonon scattering mechanisms, is needed to obtain good agreement with the data. 2017 IOP Publishing Ltd.
Journal of Physics Condensed Matter, Vol-29, No. 48
ISSN: 9538984; PubMed ID: 28984276; CODEN: JCOME
https://doi.org/10.1088/1361-648X/aa91a0 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85037681757&doi=10.1088%2f1361-648X%2faa91a0&partnerID=40&md5=0d053d4dd4232430fde5c42415e2545f
Patil S.B., Department of Physics, Karnatak University, Dharwad, Karnataka, 580003, India; Sankeshwar N.S., Department of Physics, Karnatak University, Dharwad, Karnataka, 580003, India, Department of Physics, Christ University, Bangalore, Karnataka, 560029, India; Mulimani B.G., B.L.D.E. University, Vijayapura, Karnataka, 586103, India