MURALI KRISHNA SENAPATY
@giet.edu
Asso Professor, CSE
GIET UNIVERSITY
RESEARCH INTERESTS
Machine Learning , IoT and Deep learning
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Murali Krishna Senapaty, Abhishek Ray, and Neelamadhab Padhy
MDPI AG
Today, crop suggestions and necessary guidance have become a regular need for a farmer. Farmers generally depend on their local agriculture officers regarding this, and it may be difficult to obtain the right guidance at the right time. Nowadays, crop datasets are available on different websites in the agriculture sector, and they play a crucial role in suggesting suitable crops. So, a decision support system that analyzes the crop dataset using machine learning techniques can assist farmers in making better choices regarding crop selections. The main objective of this research is to provide quick guidance to farmers with more accurate and effective crop recommendations by utilizing machine learning methods, global positioning system coordinates, and crop cloud data. Here, the recommendation can be more personalized, which enables the farmers to predict crops in their specific geographical context, taking into account factors like climate, soil composition, water availability, and local conditions. In this regard, an existing historical crop dataset that contains the state, district, year, area-wise production rate, crop name, and season was collected for 246,091 sample records from the Dataworld website, which holds data on 37 different crops from different areas of India. Also, for better analysis, a dataset was collected from the agriculture offices of the Rayagada, Koraput, and Gajapati districts in Odisha state, India. Both of these datasets were combined and stored using a Firebase cloud service. Thirteen different machine learning algorithms have been applied to the dataset to identify dependencies within the data. To facilitate this process, an Android application was developed using Android Studio (Electric Eel | 2023.1.1) Emulator (Version 32.1.14), Software Development Kit (SDK, Android SDK 33), and Tools. A model has been proposed that implements the SMOTE (Synthetic Minority Oversampling Technique) to balance the dataset, and then it allows for the implementation of 13 different classifiers, such as logistic regression, decision tree (DT), K-Nearest Neighbor (KNN), SVC (Support Vector Classifier), random forest (RF), Gradient Boost (GB), Bagged Tree, extreme gradient boosting (XGB classifier), Ada Boost Classifier, Cat Boost, HGB (Histogram-based Gradient Boosting), SGDC (Stochastic Gradient Descent), and MNB (Multinomial Naive Bayes) on the cloud dataset. It is observed that the performance of the SGDC method is 1.00 in accuracy, precision, recall, F1-score, and ROC AUC (Receiver Operating Characteristics–Area Under the Curve) and is 0.91 in sensitivity and 0.54 in specificity after applying the SMOTE. Overall, SGDC has a better performance compared to all other classifiers implemented in the predictions.
Murali Krishna Senapaty, Abhishek Ray, and Neelamadhab Padhy
AIP Publishing
A Sreelakshmi, Neelamadhab Padhy, and Murali Krishna Senapaty
IEEE
Sales can be affected by many factors due to heavy competition in the business world. The analysis to identify the customer’s interest in advance is an important factor in it. Association rule mining in machine learning allows for analyses of the huge dataset to identify associated item sets. A focus is given to the daily sales of grocery datasets. In this paper, we have collected a dataset from a local grocery market and taken initial steps for analysis. It has been identified that the dataset for a year will be analyzed completely to identify the customer’s interests. The customer’s interest also varies based on the season and the customer’s regular purchase habits. Based on the purchase interests observed, regular customers can be encouraged by sending messages about associated product offers. A brief study on the Apriori and FP Growth algorithms has been conducted based on a literature review to determine their performance. Based on the analysis, a model has been proposed in which our dataset can be used for identifying associated datasets based on different factors such as customer and season. The best algorithm shall be selected based on accuracy, execution time, and the number of associated pairs. Further, a hybridization of algorithms and other tools is suggested for enhancing performance.
Murali Krishna Senapaty, Abhishek Ray, and Neelamadhab Padhy
MDPI AG
Healthy and sufficient crop and food production are very much essential for everyone as the population is increasing globally. The production of crops affects the economy of a country to a great extent. In agriculture, observing the soil, weather, and water availability and, based on these factors, selecting an appropriate crop, finding the availability of seeds, analysing crop demand in the market, and having knowledge of crop cultivation are important. At present, many advancements have been made in recent times, starting from crop selection to crop cutting. Mainly, the roles of the Internet of Things, cloud computing, and machine learning tools help a farmer to analyse and make better decisions in each stage of cultivation. Once suitable crop seeds are chosen, the farmer shall proceed with seeding, monitoring crop growth, disease detection, finding the ripening stage of the crop, and then crop cutting. The main objective is to provide a continuous support system to a farmer so that he can obtain regular inputs about his field and crop. Additionally, he should be able to make proper decisions at each stage of farming. Artificial intelligence, machine learning, the cloud, sensors, and other automated devices shall be included in the decision support system so that it will provide the right information within a short time span. By using the support system, a farmer will be able to take decisive measures without fully depending on the local agriculture offices. We have proposed an IoT-enabled soil nutrient classification and crop recommendation (IoTSNA-CR) model to recommend crops. The model helps to minimise the use of fertilisers in soil so as to maximise productivity. The proposed model consists of phases, such as data collection using IoT sensors from cultivation lands, storing this real-time data into cloud memory services, accessing this cloud data using an Android application, and then pre-processing and periodic analysis of it using different learning techniques. A sensory system was prepared with optimised cost that contains different sensors, such as a soil temperature sensor, a soil moisture sensor, a water level indicator, a pH sensor, a GPS sensor, and a colour sensor, along with an Arduino UNO board. This sensory system allowed us to collect moisture, temperature, water level, soil NPK colour values, date, time, longitude, and latitude. The studies have revealed that the Agrinex NPK soil testing tablets should be applied to a soil sample, and then the soil colour can be sensed using an LDR colour sensor to predict the phosphorus (P), nitrogen (N), and potassium (K) values. These collected data together were stored in Firebase cloud storage media. Then, an Android application was developed to fetch and analyse the data from the Firebase cloud service from time to time by a farmer. In this study, a novel approach was identified via the hybridisation of algorithms. We have developed an algorithm using a multi-class support vector machine with a directed acyclic graph and optimised it using the fruit fly optimisation method (MSVM-DAG-FFO). The highest accuracy rate of this algorithm is 0.973, compared to 0.932 for SVM, 0.922 for SVM kernel, and 0.914 for decision tree. It has been observed that the overall performance of the proposed algorithm in terms of accuracy, recall, precision, and F-Score is high compared to other methods. The IoTSNA-CR device allows the farmer to maintain his field soil information easily in the cloud service using his own mobile with minimum knowledge. Additionally, it reduces the expenditure to balance the soil minerals and increases productivity.
Ribhu Abhusan Panda, Murali Krishna Senapaty, Premansu Sekhar Rath, and Debasis Mishra
IEEE
This paper illustrates the mathematical derivation for the biconvex shaped patch which can be used for the 5G application. The gain enhancement has been done by using the superstrate with metal blocks. A 40mm × 40 mm substrate has been taken in which FR4 Epoxy dielectric is used for the substrate with a height 1.6 mm. Air gap between the superstrate and the substrate is also 1.6 mm. The conformal rounded patch has been altered in such a way that it will be shaped as a biconvex lens. The parameters like S-Parameter, SWR, antenna gain etc have been found out and a comparison has been done taking the proposed antenna with and without superstrate. The effect of metal blocks that are used on the superstrate is considered for gain enrichment of the projected model. The return loss (<−10 dB) has been found out to be −31.18 dB at 27.8 GHz with a bandwidth of 5.1 GHz.
Sibo Prasad Patro, Padmaja Patel, Murali Krishna Senapaty, Neelamadhab Padhy, and Rahul Deo Sah
IEEE
Today, due to the growth of IoT(Internet of Things) the concept of smart cities has gained considerable popularity. To maximize the productivity and reliability of urban infrastructure consistent efforts are being made in the field of IoT. Many problems such as traffic congestion and road safety are being solved by the use of IoT. Today peoples face a common problem in the parking area to find a free parking slot in cities. In this study, we are designing a Smart Parking System, which will enable the user to find Parking slots in a given parking area. It also avoids unnecessary traveling through filled parking lots. In this paper, the author presents a smart parking system with the help of IoT over Wi-Fi. This intelligent parking system consists of an IoT module that helps to track the availability of each single vacant parking space. The author used an Arduino Uno, which can be embedded over the Wi-Fi module to establish a connection to the internet. This technology helps to transfer the data live. In this smart parking system, with the help of digital IR sensors, the system gets the status regarding the parking slot status, whether it is occupied or vacant. This sensor sends the collected data to the microcontroller. Latter the data are processed, and the status of parking slots is updated in the central database. The IR sensors need to be deployed in the appropriate locations so that the system can cover all the parking slots. Each parking slot is identified with a unique id to identify them on the network