Dr Dnayaneshwar K Kirange

@sscoetjalgaon.ac.in

Associate Professor, Computer Engineering
SSBT'S COLLEGE OF ENGINEERING AND TECHNOLOGY, JALGAON

Dr Dnayaneshwar K Kirange


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https://researchid.co/dkirange

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Computer Science Applications, Analysis, Multidisciplinary
15

Scopus Publications

646

Scholar Citations

14

Scholar h-index

14

Scholar i10-index

Scopus Publications

  • Shortest Path Forwarding in Software-Defined Networks Using RYU Controller
    Kishan P. Patel, Jıtendra P. Chaudhari, Hiren K. Mewada, Hardik S. Jayswal, Rajeshkumar V. Patel, Dnyaneshwar K. Kirange
    Ssrg International Journal of Electrical and Electronics Engineering, 2024
    The shortest path forwarding is not provided by OpenFlow. The benefit of using OpenFlow is that programmers can control the network devices by writing different applications. This research paper deals with the design of the shortest path algorithm using the RYU controller and OpenFlow. The datacenter topology with different network sizes is used for evaluating various shortest-path algorithms. In this work, the RYU controller’s basic switch application is used. The network application is divided into three parts, namely topology discovery, network view construction and forwarding. Mininet is used as an emulator with an RYU controller for SDN. The performance depicts that Dijkstra’s algorithm gives better throughput as compared to other shortest-path algorithms under consideration during this study.
  • Towards Automated Lip Reading: Developing Marathi Lip Reading Datasets and Neural Network Frameworks
    Apurva Kulkarni, Dnyaneshwar Khemachandra Kirange
    2024 4th International Conference on Intelligent Technologies Conit 2024, 2024
    This paper introduces an innovative method for automating lip-reading, with a specific focus on the Marathi language. Lip-reading plays a crucial role in aiding those with hearing impairments, but automating it presents significant challenges, especially for languages like Marathi lacking sufficient datasets. To tackle this, we propose a novel approach to automatic lip-reading, accompanied by the development of a specialized Marathi dataset. Leveraging advancements in computer vision and deep learning, our model deciphers linguistic content from lip movements, trained on this dataset. We employ various neural network architectures, including feed-forward, recurrent, and convolutional networks, to extract vital visual features crucial for accurate language interpretation Our primary goal is to provide a robust solution for automated lip-reading tailored specifically to regional languages like Marathi, aiming to enhance accessibility for individuals with hearing impairments, particularly in linguistically diverse contexts. This paper primarily discusses detailed exploration of dataset creation, neural network architectures for lip-reading system, we demonstrate the feasibility and potential impact of our approach. Our study underscores the significance of giving priority to regional languages in technological advancements to promote inclusivity for all individuals.
  • A survey on deep learning methods for brain tumor and liver lesion detection
    Suraj Patil, D. K. Kirange
    Aip Conference Proceedings, 2023
  • A Novel Hybrid U-Net with Custom Triplet Flatten Loss Function for Liver Lesion Detection
    Suraj Patil, Dnyaneshwar K. Kirange
    International Journal of Computer Theory and Engineering, 2023
    —Liver cancer ranks sixth among all cancers diagnosed globally. Due to the heterogeneous shape and size of the liver, the manual segmentation of the liver and lesions is a challenging task and time-consuming process. Most of the previous studies in this regard use traditional techniques of image processing to segment the liver and then use handcrafted features to detect lesions and tumors in the liver. The entire process is semi-automatic and results in a loss of information that affects the performance of prediction. Also, deep learning methods employed for liver lesion detection suffer from the misclassification of lesions due to an imbalance of pixel intensities and high processing computational costs. As a result, a new variant U-Net model is designed with a combination of ResNet-18 and ResNet-34 that automatically utilizes 3D contextual information of tumor tissue and detects lesions in the liver. In addition to these, a custom flattened triplet cross entropy function is designed that overcomes the problem of misclassification of lesions due to class imbalance. The novel methodology was evaluated using the benchmark LiTS17 dataset, and the best results were achieved with an accuracy, sensitivity, and specificity of 99.95%, 99.70%, and 99.85%, respectively. We were able to get a considerable reduction in error rate as well as excellent accuracy. The biomedical sector will be transformed as a result of this research.
  • Ensemble of Deep Learning Models for Brain Tumor Detection
    Suraj Patil, Dnyaneshwar Kirange
    Procedia Computer Science, 2022
    In the last two decades, improvement in artificial intelligence and medical imaging technology have made healthcare sector to achieve some remarkable achievements in diseases analysis and prediction. Due to advancement in medical imaging technology the brain images are taken in different modalities, that gives 3D view of different sections of brain for tumor diagnosis. The ability to extract relevant characteristics from magnetic resonance imaging (MRI) scans is a crucial step for brain tumor classifiers. As a result, several studies have proposed various strategies to extract relevant features from different modalities of MRI to predict the growth of abnormal tumor. Most of techniques used conventional techniques of image processing for feature extraction and machine learning for classification. More recently, the use deep learning algorithms in medical imaging has resulted in significant improvements in the classification and diagnosis of brain tumor. Since tumors are located at different regions of brain, the localizing the tumor and classifying it to particular category is challenging task. In this paper, we have solved this problem by designing deep ensemble model. In the proposed approach, first shallow convolutional neural network (SCNN) and VGG16 network were designed with T1C modality MRI image and subsequently loss and accuracy were examined. To improve the performance of model in terms accuracy and loss information, the extracted features from both the deep learning model were fused to improve the classification accuracy of three types of tumors. The obtained results from ensemble deep convolutional neural network model (EDCNN), proved that the fusion of deep learning model improves the accuracy of multiclass classification problem and also tries to address the problem of overfitting of model for imbalance dataset. The proposed model tries to give classification accuracy up to 97.77%. Furthermore, the proposed framework, achieves competitive results when compared with other state of art studies.
  • An Approach to Detect Overlapping Diseases in Tomato Leaf Using CNN
    Rizwanoddin Syed, H. D. Gadade, D. K. Kirange
    Proceedings 2022 4th International Conference on Advances in Computing Communication Control and Networking Icac3n 2022, 2022
    India is the world’s largest agricultural country and the world’s it is on second position in the list of tomato producers. There is a need for lots of advancement in technology. Tomato yields differ based on how they are produced. The most important factor that affects the crop production quality and quality is the Leaf disease in these tomato plants. As a result, it’s vital to correctly diagnose and characterise these diseases. Tomato production is influenced by a variety of diseases. Early detection of the tomato plant diseases would help us to minimize the impact of the disease on tomato plants and maximize the production of crop. Various novel methods of diagnosing certain diseases have been widely used. The existing research has shown that the Convolution Neural Network-based approach gives us impressive results compared to traditional approaches. This work will address the same problem of disease detection. In the proposed methodology, CNN will be used along with image segmentation for detecting multiple or overlapping diseases in the same leaf. The proposed model will first use a image segmentation module for generating multiple instances of the same image, then we will use the some of trained CNN model to find the affected disease in the given leafs. By using the above results, we will suggest the disease control methods to the end-user.
  • Machine Learning Based Identification of Tomato Leaf Diseases at Various Stages of Development
    H. D. Gadade, D. K. Kirange
    Proceedings 5th International Conference on Computing Methodologies and Communication Iccmc 2021, 2021
    Mosaic, early blight, late blight, Septoria virus, leaf mold, Brown spot, and spider mite are the nine common types of tomato leaf diseases. The early and accurate analysis of tomato leaf disease can increase the productivity and quality of the tomato product. The existing research in image processing does not guarantee an accurate diagnosis of the disease. Also, existing methods are complex. In this paper, an accurate and robust method for tomato leaf disease identification as well as classification into various stages of development using machine learning is proposed. The work is carried out in two stages. Firstly the tomato leaf images will be classified into appropriate disease types. Then in the second phase, the tomato leaf disease is diagnosed at various stages of development. Identifying the stage of development of tomato leaf would help to decide the type and amount of treatment required for the plant. The diseased leaf images which are taken from the PlantVillage dataset have been classified into high, medium, low, and normal severity grading. The images are preprocessed using median filtering. For feature extraction, the system using shape, color, and texture features is evaluated. The performance evaluation is also done on various classification techniques including SVM, KNN, Naive Bayes, Decision Trees, and LDA. The research indicated that the proposed model provides a robust solution for tomato leaf disease severity grading.
  • Tomato leaf disease diagnosis and severity measurement
    Haridas D. Gadade, D. K. Kirange
    Proceedings of the World Conference on Smart Trends in Systems Security and Sustainability Ws4 2020, 2020
    Indian economy is mostly dependent on agriculture. One of the highly used food crops in India is Tomato. Hence detection and analysis of leaf disease on tomato plants so as to increase the yield is highly essential. It becomes very hard to manually detect and analyze the tomato leaf diseases. Hence, in this paper we have proposed a segmentation-based approach for automatic segmentation of infected regions. The segmented area is further analyzed for disease classification and severity measurement. Leaf disease detection technique proposed here involves various stages including preprocessing, segmentation, feature extraction, training and classification followed by the severity measurement from the disease segmented region. We have analyzed the performance of different features extraction techniques including color, texture and shape features along with various classification techniques. The performance of the proposed system really inspires the farmers to use the automated system for detection and severity measurement of tomato plant disease.
  • Educational Data Mining Survey for Predicting Student’s Academic Performance
    Sharayu N. Bonde, D. K. Kirange
    Lecture Notes on Data Engineering and Communications Technologies, 2020
  • Diabetic retinopathy detection and grading using machine learning
    Kirange D.K. and
    International Journal of Advanced Trends in Computer Science and Engineering, 2019
    Diabetic Retinopathy (DR) is a constantly deteriorating disease, being one of the leading causes of vision impairment and blindness. Subtle distinction among different grades and existence of many significant small features make the task of recognition very challenging. In addition, the present approach of retinopathy detection is a very laborious and time-intensive task, which heavily relies on the skill of a physician. Automated detection of diabetic retinopathy is essential to tackle these problems. Early-stage detection of diabetic retinopathy is also very important for diagnosis, which can prevent blindness with proper treatment. In this paper, we developed a novel system which performs the early-stage detection by identifying all microaneurysms (MAs), the first signs of DR, along with correctly assigning labels to retinal fundus images which are graded into five categories. We have tested our system on the largest publicly available IDRiD diabetic retinopathy dataset, and achieved 77.85% accuracy with Gabor features and Naïve Bayes Classification.
  • Artificial Intelligence: A Survey on Lip-Reading Techniques
    Apurva H. Kulkarni, Dnyaneshwar Kirange
    2019 10th International Conference on Computing Communication and Networking Technologies Icccnt 2019, 2019
  • Survey on Evaluation of Student's Performance in Educational Data Mining
    Sharayu N. Bonde, D. K. Kirange
    Proceedings of the International Conference on Inventive Communication and Computational Technologies Icicct 2018, 2018
  • Analyzing the effect of database dimensionality on performance of adaptive apriori algorithm
    Shubhangi D. Patil, Ratnadeep R. Deshmukh, Dnyaneshwar K. Kirange, Swapnil Waghmare
    Communications in Computer and Information Science, 2018
  • Iris recognition using radon transform and GLCM
    Kanchan S Bhagat, Pramod B. Patil, Ratnadeep R. Deshmukh, D.K. Kirange, Swapnil Waghmare
    2017 International Conference on Advances in Computing Communications and Informatics Icacci 2017, 2017
  • Adaptive Apriori Algorithm for frequent itemset mining
    Shubhangi D. Patil, Ratnadeep R. Deshmukh, D.K. Kirange
    Proceedings of the 5th International Conference on System Modeling and Advancement in Research Trends Smart 2016, 2017

RECENT SCHOLAR PUBLICATIONS

  • Recursive feature elimination and optimized hybrid ensemble approach for early heart disease prediction
    JP Chaudhari, KP Patel, HK Mewada, HS Jayswal, YP Kosta, KS Bhagat, ...
    Advances in Technology Innovation 10 (1), 58-71 , 2025
    2025
    Citations: 5
  • Towards automated lip reading: Developing marathi lip reading datasets and neural network frameworks
    A Kulkarni, DK Kirange
    2024 4th International Conference on Intelligent Technologies (CONIT), 1-6 , 2024
    2024
    Citations: 2
  • Shortest Path Forwarding in Software-Defined Networks Using RYU Controller
    DKK Kishan P. Patel1 , Jıtendra P. Chaudhari 2 , Hiren K. Mewada3, Hardik S ...
    SSRG International Journal of Electrical and Electronics Engineering 11 (5 … , 2024
    2024
    Citations: 6
  • A survey on deep learning methods for brain tumor and liver lesion detection
    S Patil, DK Kirange
    ADVANCES IN INTELLIGENT APPLICATIONS AND INNOVATIVE APPROACH 2760 (1), 020021 , 2023
    2023
    Citations: 1
  • An Optimized Deep Learning Model with Feature Fusion for Brain Tumor Detection.
    S Patil, D Kirange
    International Journal of Next-Generation Computing 14 (1) , 2023
    2023
    Citations: 1
  • A Novel Hybrid U-Net with Custom Triplet Flatten Loss Function for Liver Lesion Detection
    DKK Suraj Patil
    IJCTE- International Journal of Computer Theory and Engineering 15 (2), 82-89 , 2023
    2023
    Citations: 1
  • Ensemble of deep learning models for brain tumor detection
    S Patil, D Kirange
    Procedia computer science 218, 2468-2479 , 2023
    2023
    Citations: 143
  • An approach to detect overlapping diseases in tomato leaf using cnn
    R Syed, HD Gadade, DK Kirange
    2022 4th International Conference on Advances in Computing, Communication … , 2022
    2022
    Citations: 2
  • Deep learning for tomato leaf disease detection for images captured in varying capturing conditions.
    HD Gadade, DK Kirange
    2022
    Citations: 2
  • Improved Salp Swarm Optimization-based Fuzzy Centroid Region Growing for Liver Tumor Segmentation and Deep Learning Oriented Classification.
    S Patil, D Kirange, R Hablani
    International Journal of Next-Generation Computing 13 (5) , 2022
    2022
    Citations: 1
  • Deep Learning for Tomato Leaf Disease Detection for Images Captured in Varying Capturing Conditions
    DDKK Haridas D. Gadade
    JJM 15 (1), 2191 - 2200 , 2022
    2022
  • Forecasting COVID 19 Cases on Radiological Images Using Deep Learning and VGG Model
    DDKK Dr Nandini Chaudhari,Dr Avani Vasant,Dr Nitesh Sureja,Dr Darshana H Patel
    Jundishapur Journal of Microbiology 15 (1), 117-732 , 2022
    2022
  • Machine learning based identification of tomato leaf diseases at various stages of development
    HD Gadade, DK Kirange
    2021 5th International Conference on Computing Methodologies and … , 2021
    2021
    Citations: 31
  • A Survey on Deep Learning Methods for Brain Tumor and Liver Lesion Detection
    DKK Suraj Patil
    International Conference on global entrepreneurship Trends & empowerment … , 2021
    2021
  • Tomato leaf disease diagnosis and severity measurement
    HD Gadade, DK Kirange
    2020 fourth world conference on smart trends in systems, security and … , 2020
    2020
    Citations: 55
  • END POINT ANALYSIS FOR DETECTING PITCH BOUNDARIES OF SPOKEN INDIAN DEVNAGARI LANGUAGE NUMERICAL USING AUTOCORRELATION TECHNIQUE
    DJPC Dr Pramod B. Patil, Dr Shubhangi D Patil, Dr D K Kirange, Dr K S Bhagat
    High Technology Letters,ISSN NO : 1006-6748 26 (5), 424-427 , 2020
    2020
  • Ealuation of Bandwidth Utilization in SDN
    DSDP Dr. J P Chaudhari ,Dr. D K Kirange ,Dr. K S Bhagat
    Journal of Xidian University ISSN No. 1001-2400 14 (5), 1758-1767 , 2020
    2020
  • Software Defined Networks using Mininet
    SDP Pramod B Patil., Kanchan S. Bhagat., D K Kirange
    International Journal of Recent Technology and Engineering (IJRTE),ISSN … , 2020
    2020
    Citations: 5
  • Machine Learning Approach towards Tomato Leaf Disease Classification
    D.K.Kirange, H. D. Gadade
    International Journal of Advanced Trends in Computer Science and Engineering … , 2020
    2020
    Citations: 29
  • PREDICTIVE MODELLING OF BRAIN TUMOR DETECTION USING DEEP LEARNING
    VN Suraj Patil , Dr D. K. Kirange
    JOURNAL OF CRITICAL REVIEWS,ISSN- 2394-5125 7 (04), 1805-1813 , 2020
    2020
    Citations: 24

MOST CITED SCHOLAR PUBLICATIONS

  • Ensemble of deep learning models for brain tumor detection
    S Patil, D Kirange
    Procedia computer science 218, 2468-2479 , 2023
    2023
    Citations: 143
  • ASPECT BASED SENTIMENT ANALYSIS SEMEVAL-2014 TASK 4
    RRD D. K. Kirange
    Asian Journal of Computer Science And Information Technology 4 (8), 72-75 , 2014
    2014
    Citations: 83
  • Emotion Classification of News Headlines Using SVM
    RRD D. K. Kirange
    Asian Journal Of Computer Science And Information Technology 2 (5), 104 –106 , 2012
    2012
    Citations: 63
  • Sentiment Analysis of News Headlines for Stock Price Prediction
    DRRD Mr. D. K. Kirange
    COMPUSOFT, An international journal of advanced computer technology 5 (3 … , 2016
    2016
    Citations: 61
  • Tomato leaf disease diagnosis and severity measurement
    HD Gadade, DK Kirange
    2020 fourth world conference on smart trends in systems, security and … , 2020
    2020
    Citations: 55
  • Machine learning based identification of tomato leaf diseases at various stages of development
    HD Gadade, DK Kirange
    2021 5th International Conference on Computing Methodologies and … , 2021
    2021
    Citations: 31
  • Machine Learning Approach towards Tomato Leaf Disease Classification
    D.K.Kirange, H. D. Gadade
    International Journal of Advanced Trends in Computer Science and Engineering … , 2020
    2020
    Citations: 29
  • PREDICTIVE MODELLING OF BRAIN TUMOR DETECTION USING DEEP LEARNING
    VN Suraj Patil , Dr D. K. Kirange
    JOURNAL OF CRITICAL REVIEWS,ISSN- 2394-5125 7 (04), 1805-1813 , 2020
    2020
    Citations: 24
  • Diabetic Retinopathy Detection and Grading Using Machine Learning
    NC D. K. Kirange, J.P. Chaudhari , K. P. Rane , K. S. Bhagat
    International Journal of Advanced Trends in Computer Science and Engineering … , 2019
    2019
    Citations: 23
  • Emotion classification of restaurant and laptop review dataset: Semeval 2014 task 4
    DK Kirange, RR Deshmukh
    International Journal of Computer Applications 113 (6), 17-20 , 2015
    2015
    Citations: 20
  • Classifying News Headlines for Providing User Centered E-Newspaper Using SVM
    DKKRR Deshmukh
    nternational Journal of Emerging Trends & Technology in ComputerScience … , 2013
    2013
    Citations: 19
  • Adaptive Apriori Algorithm for frequent itemset mining
    SD Patil, RR Deshmukh, K D K
    System Modeling & Advancement in Research Trends (SMART), IEEE Conference, 7 … , 2016
    2016
    Citations: 17
  • Artificial intelligence: a survey on lip-reading techniques
    AH Kulkarni, D Kirange
    2019 10th International Conference on Computing, Communication and … , 2019
    2019
    Citations: 15
  • Survey on Evaluation of Student's Performance in Educational Data Mining
    SN Bonde, DK Kirange
    2018 Second International Conference on Inventive Communication and … , 2018
    2018
    Citations: 14
  • Educational data mining survey for predicting student’s academic performance
    SN Bonde, DK Kirange
    International Conference on Computer Networks, Big data and IoT, 293-302 , 2018
    2018
    Citations: 9
  • Iris recognition using radon transform and GLCM
    KS Bhagat, PB Patil, RR Deshmukh, DK Kirange, S Waghmare
    International Conference on Advances in Computing, Communications and … , 2017
    2017
    Citations: 7
  • Shortest Path Forwarding in Software-Defined Networks Using RYU Controller
    DKK Kishan P. Patel1 , Jıtendra P. Chaudhari 2 , Hiren K. Mewada3, Hardik S ...
    SSRG International Journal of Electrical and Electronics Engineering 11 (5 … , 2024
    2024
    Citations: 6
  • Recursive feature elimination and optimized hybrid ensemble approach for early heart disease prediction
    JP Chaudhari, KP Patel, HK Mewada, HS Jayswal, YP Kosta, KS Bhagat, ...
    Advances in Technology Innovation 10 (1), 58-71 , 2025
    2025
    Citations: 5
  • Software Defined Networks using Mininet
    SDP Pramod B Patil., Kanchan S. Bhagat., D K Kirange
    International Journal of Recent Technology and Engineering (IJRTE),ISSN … , 2020
    2020
    Citations: 5
  • ASPECT AND EMOTION CLASSIFICATION OF RESTAURANT AND LAPTOP REVIEWS USING SVM
    RRD D.K.Kirange
    International Journal of Current Research 8 (03), 28352-28356 , 2016
    2016
    Citations: 5