Nishant Nilkanth Pachpor

@iims.ac.in

Asst. Professor
Yashaswi education society International Institute of Management Science, Chinchwad Pune

Nishant Nilkanth Pachpor


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https://researchid.co/pachpornishant

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Artificial Intelligence, Computer Science
14

Scopus Publications

61

Scholar Citations

4

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • Artificial intelligence powered crime scene analysis service
    Vanita Kshirsagar, Nishant Pachpor, Shubhangi Suryawanshi, Tanvi Chavan, Navya J. Nair, Purvesh Agrawal, Tejas Shahane
    Methodsx, 2025
    Crime remains a major concern in modern culture. As a result, emphasizing prevention and ensuring prompt justice delivery are critical. In criminal investigations and law enforcement, forensic science plays a critical role. However, typical approaches in this field frequently rely on physical procedures, which are inefficient and likely to increase human mistakes, hampering the timely administration of justice. To tackle these issues, artificial intelligence skills were integrated into investigation processes. The suggested novel web application aims to help forensic investigators streamline crime scene investigations by automating important portions of the procedure. The system has three main functions:•fingerprint reconstruction,•weapon detection, and•human activity recognition. Its easy-to-use interface allows officials to upload images or videos from the crime scene in order to assist in reconstructions and detections. The fingerprint reconstruction model built using autoencoders outputs better partially covered fingerprint images with a validation loss of 0.0477 and a step loss of 0.0487, which helps detect the persons responsible for the event. Furthermore, the model for object detection, YOLO NAS, plays an important role in recognizing weapons that might be present at the scene, with an mAP of 77.8 %, while human activity detection techniques such as VGG16 have a total accuracy of 98.21 %.
  • HEDL: Deep learning multiple approaches for early detection of depression using sarcastic text
    Vanita Kshirsagar, Nishant Pachpor, Ashwini Brahme, Ravindra Aapre, Shubhangi Suryawanshi, Digvijay Bhosale
    Methodsx, 2025
    Sarcasm is particularly notorious towards mental health, and thus it is quite essential for early identification of depressive indicators. This paper introduces the Hybrid Ensemble Deep Learning model as novel for the task of the detection of sarcasm task, targeting the weaknesses which were found in traditional approaches of SVC, DT, RF, and LR by using unique combinations of CNN, LSTM, and GRU to capture the sarcasm patterns that appear fine feature representation and enhanced robustness and accuracy. Our model uniquely integrates the architectures of CNN, LSTM, and GRU into one framework for capturing more complex patterns in feature representation, accuracy, and robustness. We tested it on a news headline dataset; HEDL gained 84 % accuracy along with marked reduction in false positives compared to baseline models, which improved the accuracy as well as the recall. Results of the experiment do support that the HEDL model is indeed much more accurate and reliable sarcastic detection methodology; it can have applications such as monitoring mental health or analysing sentiment.•Proposed the Hybrid Ensemble Deep Learning Algorithm (HEDL) for text data.•The proposed model outperforms traditional models in cognitive skill impairment detection.•Demonstrated scalability for diverse healthcare datasets.
  • Rainfall Forecasting and Analysis Using Machine Learning Models
    Nishant Pachpor, Ashwini Brahme, Anand Labade, Prashant Dike, Dnyaneshwar Mantri, Sachin Misal
    2025 IEEE 3rd Global Conference on Wireless Computing and Networking Gcwcn 2025, 2025
    Rainfall forecasting plays a crucial role in agriculture, water resource management, and disaster mitigation. This study presents a comparative analysis of various machine learning and deep learning models, including ANN, LSTM-CNN, and hybrid optimization-based methods such as TRI-LSTM with Hybrid Moth-Flame Colliding Bodies Optimization (HMFCBO), for accurate rainfall prediction. Publicly available datasets from Kaggle and government meteorological sources were used to train and evaluate the models. Performance metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Classification Error (CE), and Prediction Interval (PI) were employed to assess accuracy and reliability. The results indicate that hybrid and deep learning approaches outperform conventional models, demonstrating improved forecasting precision and generalization capability. The study highlights the potential of AI-driven techniques for reliable and scalable rainfall prediction across diverse geographical regions.
  • Predictive Analytics using Knowledge Discovery System in Healthcare for Viral Diseases Outbreaks
    Ashwini Brahme, Sagar Kulkarni, Shivaji Mundhe, Manasi Kulkarni, Dnyaneshwar Mantri, Nishant Pachpor
    2025 IEEE 3rd Global Conference on Wireless Computing and Networking Gcwcn 2025, 2025
    Healthcare Management is challenging domain were enormous data is collected through the various day to day tasks. Working on vast, heterogonous, real time data is biggest challenge in any sector where getting the required information and processing is the need of current era. The ample amount of data available all over the globe is in structured, semi structured and unstructured format. Mining structured, unstructured data to identify meaningful patterns and support decision-making is challenging because of diversity in data and information. The implications of data mining in healthcare and predictive analytics plays significant role. The paper aimed towards the analysis of outbreaks prediction through manually and through the proposed knowledge discovery application. This paper is intended towards the testing of knowledge discovery system designed by the researcher from the doctor’s perspective. The prediction of highly frequent diseases is studied, predictive analysis of diseases and its geographical location is carried out in the research for the better decision making and precautionary measures need to be focused by healthcare and other stakeholders. The present research is aimed to test the designed predictive knowledge discovery system usage, need and its significance in healthcare.
  • Hybrid machine learning method for classification and recommendation of vector-borne disease
    Salim Gulab Shaikh, Billakurthi Suresh Kumar, Geetika Narang, Nishant Nilkanth Pachpor
    Journal of Autonomous Intelligence, 2024
    Vector-borne diseases (VBD) are a class of infectious illnesses that are transmitted to humans and animals through the bites of arthropod vectors, such as mosquitoes, ticks, and fleas. These diseases are caused by a variety of pathogens, including bacteria, viruses, and parasites, and are a significant global public health concern. Vector-borne diseases are prevalent in many parts of the world, particularly in tropical and subtropical regions, where the vectors thrive. This research has contributed by constructing a hybrid machine learning based prediction model, which helps to discover patients who are infected by vector-borne disease at an earlier stage and also helps with the categorization and diagnosis of severe vector-borne disease. The model that has been proposed is made up of units: data conversion, data preprocessing, normalization, extraction of feature, splitting of dataset, and classification and prediction unit. The fact that the suggested prediction model is capable of identifying vector-borne disease in its early phases as well as categorizing the kind of disease using the medical report of a sufferer is one of the innovative aspects of the model. The 7 distinct conventional machine learning and single hybrid machine learning (HML) are applied for classification and Recurrent Neural Network (RNN) based reinforcement learning are utilized for recommendation. In order to evaluate the effectiveness of the system that’s been proposed, a number of tests were carried out. A dataset consisting of 1539 different cases of a disease transmitted by vectors has been collected. The 11 common vector-borne diseases namely malaria, dengue, Japanese encephalitis, kala-azar and chikungunya were taken for experimental evaluation. The performance accuracy of the proposed prediction model has been measured at 98.76%, which assists the healthcare team in making decisions on a timely basis and ultimately helps to save the patient’s lives. The final phase system provides the recommendation for those classifiers resulting in four different classes such as normal, mild, moderate and severe respectively. The recommendation is also demonstrating future direction for cure of vector borne disease.
  • Intelligent rainfall forecasting model: heuristic assisted adaptive deep temporal convolutional network with optimal feature selection
    Nishant Nilkanth Pachpor, B. Suresh Kumar, Prakash S. Prasad, Salim G. Shaikh
    International Journal of Intelligent Information and Database Systems, 2024
    A deep learning technology is adopted to predict seasonal rainfall efficiently. Various rainfall data are collected from the internet. A deep feature extraction is done by autoencoder. Further, the deep extracted features are provided to the optimal feature selection phase, where the weights are optimised by utilising the developed modified attack power-based sail fish-hybrid leader optimisation (MAP-SFHLO). Then, the selected optimal features are provided as input to the prediction stage, and the prediction is done using the enhanced atrous-based adaptive deep temporal convolutional network (EA-ADTCN) along with the aid of the developed MAP-SFHLO algorithm to offer an effective prediction rate as the final outcome. Throughout the analysis, the performance of the developed model shows 5.2% and 6.0% regarding MAE and RMSE metrics. Thus, the suggested system performs more accurately in terms of accuracy rate in predicting rainfall than conventional techniques.
  • A Hybrid Feature Selection Gradient Recurrent Neural Network (HFSGRNN) Model for Rainfall Prediction in India Regions
    International Journal of Intelligent Engineering and Systems, 2024
    In current studies, India is a farming country, and the accomplishment or disappointment of the crop mainly depends on the country's rainfall design.Generally, India's farming production is primarily based on the nature of the precipitation of the rainy season rainfall.The rainy season is the primary source of water in India.Regular rainfall forecasting is the primary source for crop development.Several analyses have defined the direct effect of rainwater on harvests.The main motive of this research work is proper and early rainfall prediction, which is helpful to people who live in northeast regions inclined to natural disasters like floods, etc.It helps agriculture with decision-making in their crop and water management (WM) using extensive dataset analysis that generates maximum terms of production for farmers and profits.This proposed work introduced an improved rainfall forecasting framework, a hybrid feature selection gradient-based RNN (HFSGRNN) model with an RNN algorithm.The research uses the HFSGRNN model steps, such as initial data preprocessing steps, which are used for forecasting rainfall, handling missing value outliers, and typecasting the rainfall dataset collected from the government site.After that, an HFSGRNN method is implemented to select the valuable using stochastic gradient descent (SGD) and optimal solutions calculated by particle swarm optimization (PSO) from the preprocessed data.The hybrid optimized feature sets are fed to the rainfall forecasting of the RNN classifier.Lastly, the valuable feature sets are forecasted using decision-making, and the simulation outcome shows that the research approach performed better in rainfall forecasting.The simulation results define that the HFSGRNN model delivered the minimum value of Root means square error (RMSE= 0.10) and maximum value of accuracy rate (acc = 98.1%) compared with existing methods, such as logistic regression (LR), Long Short Term -Convolutional Neural Network (LSTM-CNN), etc.The outcomes of the research analysis will help the farmers accept efficient modeling methods for forecasting long-term seasonal rainfall.
  • Adaptive membership enhanced fuzzy classifier with modified LSTM for automated rainfall prediction model
    Nishant Nilkanth Pachpor, B. Suresh Kumar, Prakash S. Prasad
    Intelligent Decision Technologies, 2023
    Nowadays, various research works is explored to predict the rainfall in the different areas. The emerging research is assisted to make effective decision capacities that are involved in the field of agriculture broadly related to the irrigation process and cultivation. Here, the atmospheric and climatic factors such as wind speed, temperature, and humidity get varies from one place to another place. Thus, it makes the system more complex, and it attains higher error rate during computation for providing accurate rainfall prediction results. In this paper, the major intention is to design an advanced Artificial Intelligent (AI) model for rainfall prediction for different areas. The rainfall data from diverse areas are collected initially, and data cleaning is performed. Further, data normalization is done for ensuring the proper organization and related data in each record. Once these pre-processing phases are completed, rainfall recognition is the main step, in which Adaptive Membership Enhanced Fuzzy Classifier (AME-FC) is adopted for classifying the data into low, medium, and high rainfall. Then for each degree of low, medium, and high rainfall, the prediction process is performed individually by training the developed Tri-Long Short-Term Memory (TRI-LSTM). Additionally, the output achieved from the trained TRI-LSTM rainfall prediction in cm for each low, medium, and high rainfall. The meta-heuristic technique with Hybrid Moth-Flame Colliding Bodies Optimization (HMFCBO) enhances the recognition and prediction phases. The experimental outcome shows that the different rainfall prediction databases prove the developed model overwhelms the conventional models, and thus it would be helpful to predict more accurate rainfall.
  • Diagnosis of Vector Borne Disease using Various Machine Learning Techniques
    International Journal of Intelligent Systems and Applications in Engineering, 2023
  • Several Classification and Recommendations Methods Used in Dengue Fever Prediction System
    Salim G. Shaikh, B. Suresh Kumar, Geetika Narang, N.N. Pachpor
    2023 International Conference on Integration of Computational Intelligent System Icicis 2023, 2023
    Mosquitos influence dengue fever, and the dengue virus is a universal community health issue worldwide. An analysis and prediction are required to resolve the effects of the dengue virus in communities. The main motive of this article is to recognize the classification or recommendation methods based on machine learning (ML) and deep learning (DL) for predicting and detecting dengue fever. The classification methods such as SVM, KNN, DT, and naïve bayes are used to perform experimental results. In this article, a comparison of these methods is executed, and SVM achieves a better accuracy rate. This method is highest accurate and suitable for predicting the dengue virus. The naïve bays is an effective method for better performance with less time-consuming. This method takes 0.01 seconds and reduces the probability of errors. The techniques like DT, KNN, and naïve Bayes provide 55.5%, 96%, and 72% accuracy, respectively. The SVM, DT, and naïve bayes consumed the time of 0.16sec, 0.05sec, and 0.01sec, respectively.
  • Different Nature-Inspired Optimization Models Using Heavy Rainfall Prediction: A Review
    Nishant N. Pachpor, B. Suresh Kumar, Prakash S. Parsad, Salim G. Shaikh
    Lecture Notes in Networks and Systems, 2022
  • Improving the Performance of System in Cloud by Using Selective Deduplication
    Nishant N. Pachpor, Prakash S. Prasad
    Proceedings of the 2nd International Conference on Electronics Communication and Aerospace Technology Iceca 2018, 2018
  • Wireless sensor network multirate signal processing for channel capacity and delay improvement
    Prashant R. Dike, Vandana M. Rohakle, T. S. Vishwanath, Nishant N. Pachpor
    Proceedings of the International Conference on Inventive Computation Technologies Icict 2016, 2016
  • Spectral efficiency enhancement through Wavelet Transform (WT) for 5G
    Anand A. Labade, Ganesh V. Lohar, Prashant R. Dike, Nishant N. Pachpor
    Proceedings 2014 IEEE Global Conference on Wireless Computing and Networking Gcwcn 2014, 2015

RECENT SCHOLAR PUBLICATIONS

  • Artificial intelligence powered crime scene analysis service
    V Kshirsagar, N Pachpor, S Suryawanshi, T Chavan, NJ Nair, P Agrawal, ...
    MethodsX 15, 103430 , 2025
    2025
    Citations: 4
  • Predictive Analytics using Knowledge Discovery System in Healthcare for Viral Diseases Outbreaks
    A Brahme, S Kulkarni, S Mundhe, M Kulkarni, D Mantri, N Pachpor
    2025 IEEE 3rd Global Conference on Wireless Computing and Networking (GCWCN … , 2025
    2025
  • Rainfall Forecasting and Analysis Using Machine Learning Models
    N Pachpor, A Brahme, A Labade, P Dike, D Mantri, S Misal
    2025 IEEE 3rd Global Conference on Wireless Computing and Networking (GCWCN … , 2025
    2025
  • HEDL: Deep learning multiple approaches for early detection of depression using sarcastic text
    V Kshirsagar, N Pachpor, A Brahme, R Aapre, S Suryawanshi, D Bhosale
    MethodsX 14, 103370 , 2025
    2025
    Citations: 3
  • Studying and exploring various machine learning methods employed in rainfall forecasting prediction
    NN Pachpor, BS Kumar, PS Prasad, SG Shaikh
    Sustainable Smart Technology Businesses in Global Economies, 513-527 , 2025
    2025
    Citations: 1
  • Cybersecurity for Entrepreneurs: Opportunities, Challenges and Threats
    SM Ashwini Brahme, Sagar Kulkarni, Nishant Pachpor, Ashwini Chavan, Smita Chavan
    https://jisem-journal.com/index.php/journal/article/view/2929/1208 10 (18 … , 2025
    2025
  • Association Rule Mining and Information Retrieval Using Stemming and Text Mining Techniques
    AAJNP Ashwini Brahme1, SalimShaikh2,Sunita Lokare3, Sagar Kulkarni4 ...
    Journal of Information Systems Engineering and Management 10 (18), 622-628 , 2025
    2025
    Citations: 2
  • Early Prediction of Depression by using Text and Deep Learning
    NP Vanita Ganesh Kshirsagar
    https://jisem-journal.com/index.php/journal/article/view/2929/1208 10 (15S … , 2025
    2025
  • Optimal Keyword Selection by Hybrid Optimization with Itemset Mining for Text Summarization in Biomedical Sector
    N Pachpor, S Shaikh, S Misal, A Brahme
    IJCRT Research Journal| UGC Approved and UGC Care Journal| Scopus Indexed … , 2024
    2024
    Citations: 2
  • Enhanced Fake News Detection with the Aid of Improved Spider Monkey Optimization-Based Optimal Feature Selection and Deep Neural Network
    N Pachpor, S Shaikh, M Ansari
    IJCRT Research Journal| UGC Approved and UGC Care Journal| Scopus Indexed … , 2024
    2024
  • Intelligent rainfall forecasting model: heuristic assisted adaptive deep temporal convolutional network with optimal feature selection
    SG Shaikh, PP B. Suresh Kumar
    International Journal of Intelligent Information and Database Systems 16 (4 … , 2024
    2024
    Citations: 1
  • A Hybrid Feature Selection Gradient Recurrent Neural Network (HFSGRNN) Model for Rainfall Prediction in India Regions.
    NN Pachpor, BS Kumar, PS Prasad, SG Shaikh
    International Journal of Intelligent Engineering & Systems 17 (2) , 2024
    2024
    Citations: 6
  • Several Classification and Recommendations Methods Used in Dengue Fever Prediction System
    SG Shaikh, BS Kumar, G Narang, NN Pachpor
    2023 International Conference on Integration of Computational Intelligent … , 2024
    2024
    Citations: 1
  • Cybersecurity for Entrepreneurs: Opportunities, Challenges and Threats
    A Brahme, S Kulkarni, N Pachpor, A Chavan, S Chavan, S Mundhe
    2024
    Citations: 1
  • Original Research Article Hybrid machine learning method for classification and recommendation of vector-borne disease
    SG Shaikh, BS Kumar, G Narang, NN Pachpor
    Journal of Autonomous Intelligence 7 (2) , 2024
    2024
    Citations: 10
  • Adaptive membership enhanced fuzzy classifier with modified LSTM for automated rainfall prediction model
    NN Pachpor, BS Kumar, PS Prasad
    Intelligent Decision Technologies 17 (4), 1031-1060 , 2023
    2023
    Citations: 4
  • Diagnosis of vector borne disease using various machine learning techniques
    SG Shaikh, BS Kumar, G Narang, NN Pachpor
    International Journal of Intelligent Systems and Applications in Engineering … , 2023
    2023
    Citations: 8
  • Different Nature-Inspired Optimization Models Using Heavy Rainfall Prediction: A Review
    NN Pachpor, B Suresh Kumar, PS Parsad, SG Shaikh
    Intelligent Sustainable Systems: Proceedings of ICISS 2022, 761-775 , 2022
    2022
    Citations: 1
  • Securing the data deduplication to improve the performance of systems in the cloud infrastructure
    N N. Pachpor, P S. Prasad
    Performance Management of Integrated Systems and its Applications in … , 2019
    2019
    Citations: 3
  • Improving the performance of system in cloud by using selective deduplication
    NN Pachpor, PS Prasad
    2018 Second International Conference on Electronics, Communication and … , 2018
    2018
    Citations: 10

MOST CITED SCHOLAR PUBLICATIONS

  • Original Research Article Hybrid machine learning method for classification and recommendation of vector-borne disease
    SG Shaikh, BS Kumar, G Narang, NN Pachpor
    Journal of Autonomous Intelligence 7 (2) , 2024
    2024
    Citations: 10
  • Improving the performance of system in cloud by using selective deduplication
    NN Pachpor, PS Prasad
    2018 Second International Conference on Electronics, Communication and … , 2018
    2018
    Citations: 10
  • Diagnosis of vector borne disease using various machine learning techniques
    SG Shaikh, BS Kumar, G Narang, NN Pachpor
    International Journal of Intelligent Systems and Applications in Engineering … , 2023
    2023
    Citations: 8
  • A Hybrid Feature Selection Gradient Recurrent Neural Network (HFSGRNN) Model for Rainfall Prediction in India Regions.
    NN Pachpor, BS Kumar, PS Prasad, SG Shaikh
    International Journal of Intelligent Engineering & Systems 17 (2) , 2024
    2024
    Citations: 6
  • Artificial intelligence powered crime scene analysis service
    V Kshirsagar, N Pachpor, S Suryawanshi, T Chavan, NJ Nair, P Agrawal, ...
    MethodsX 15, 103430 , 2025
    2025
    Citations: 4
  • Adaptive membership enhanced fuzzy classifier with modified LSTM for automated rainfall prediction model
    NN Pachpor, BS Kumar, PS Prasad
    Intelligent Decision Technologies 17 (4), 1031-1060 , 2023
    2023
    Citations: 4
  • Spectral efficiency enhancement through Wavelet Transform (WT) for 5G
    AA Labade, GV Lohar, PR Dike, NN Pachpor
    2014 IEEE Global Conference on Wireless Computing & Networking (GCWCN), 268-272 , 2014
    2014
    Citations: 4
  • HEDL: Deep learning multiple approaches for early detection of depression using sarcastic text
    V Kshirsagar, N Pachpor, A Brahme, R Aapre, S Suryawanshi, D Bhosale
    MethodsX 14, 103370 , 2025
    2025
    Citations: 3
  • Securing the data deduplication to improve the performance of systems in the cloud infrastructure
    N N. Pachpor, P S. Prasad
    Performance Management of Integrated Systems and its Applications in … , 2019
    2019
    Citations: 3
  • Association Rule Mining and Information Retrieval Using Stemming and Text Mining Techniques
    AAJNP Ashwini Brahme1, SalimShaikh2,Sunita Lokare3, Sagar Kulkarni4 ...
    Journal of Information Systems Engineering and Management 10 (18), 622-628 , 2025
    2025
    Citations: 2
  • Optimal Keyword Selection by Hybrid Optimization with Itemset Mining for Text Summarization in Biomedical Sector
    N Pachpor, S Shaikh, S Misal, A Brahme
    IJCRT Research Journal| UGC Approved and UGC Care Journal| Scopus Indexed … , 2024
    2024
    Citations: 2
  • Studying and exploring various machine learning methods employed in rainfall forecasting prediction
    NN Pachpor, BS Kumar, PS Prasad, SG Shaikh
    Sustainable Smart Technology Businesses in Global Economies, 513-527 , 2025
    2025
    Citations: 1
  • Intelligent rainfall forecasting model: heuristic assisted adaptive deep temporal convolutional network with optimal feature selection
    SG Shaikh, PP B. Suresh Kumar
    International Journal of Intelligent Information and Database Systems 16 (4 … , 2024
    2024
    Citations: 1
  • Several Classification and Recommendations Methods Used in Dengue Fever Prediction System
    SG Shaikh, BS Kumar, G Narang, NN Pachpor
    2023 International Conference on Integration of Computational Intelligent … , 2024
    2024
    Citations: 1
  • Cybersecurity for Entrepreneurs: Opportunities, Challenges and Threats
    A Brahme, S Kulkarni, N Pachpor, A Chavan, S Chavan, S Mundhe
    2024
    Citations: 1
  • Different Nature-Inspired Optimization Models Using Heavy Rainfall Prediction: A Review
    NN Pachpor, B Suresh Kumar, PS Parsad, SG Shaikh
    Intelligent Sustainable Systems: Proceedings of ICISS 2022, 761-775 , 2022
    2022
    Citations: 1
  • Predictive Analytics using Knowledge Discovery System in Healthcare for Viral Diseases Outbreaks
    A Brahme, S Kulkarni, S Mundhe, M Kulkarni, D Mantri, N Pachpor
    2025 IEEE 3rd Global Conference on Wireless Computing and Networking (GCWCN … , 2025
    2025
  • Rainfall Forecasting and Analysis Using Machine Learning Models
    N Pachpor, A Brahme, A Labade, P Dike, D Mantri, S Misal
    2025 IEEE 3rd Global Conference on Wireless Computing and Networking (GCWCN … , 2025
    2025
  • Cybersecurity for Entrepreneurs: Opportunities, Challenges and Threats
    SM Ashwini Brahme, Sagar Kulkarni, Nishant Pachpor, Ashwini Chavan, Smita Chavan
    https://jisem-journal.com/index.php/journal/article/view/2929/1208 10 (18 … , 2025
    2025
  • Early Prediction of Depression by using Text and Deep Learning
    NP Vanita Ganesh Kshirsagar
    https://jisem-journal.com/index.php/journal/article/view/2929/1208 10 (15S … , 2025
    2025