CLASSIFICATION MODEL FOR DISCOVERING THE TYPE OF CROP TO PLANT USING ENSEMBLE TECHNIQUES

Farming plays a role in ensuring survival, especially with the growing need for increased agricultural output. It is vital for farmers to efficiently choose the crops to cultivate. By using crop recommendation systems farmers can make decisions on what crops to plant leading to yields and improved resource management. The success of crop production depends on maintaining the balance of soil nutrients and favorable weather conditions. In this research project, we created a crop recommendation system utilizing learning methods to predict the appropriate crops based on essential soil nutrients and weather patterns. We worked with a dataset sourced from Kaggle, which included 2,200 entries featuring elements like Nitrogen, Phosphorus, Potassium, Temperature, Humidity, pH levels, and Rainfall data in CSV format. We compared two techniques: bagging and boosting. For bagging, we utilized base estimators such as Decision Tree, Random Forest, and Support Vector Classifier (SVC). Our analysis revealed that the Random Forest classifier attained an accuracy rate of 99% after optimizing parameters, emerging as the effective model for our dataset. Additionally, the boosting approach using the Gradient Boosting classifier also performed well, with an accuracy rate of 98%. These findings underscore how ensemble methods can significantly improve accuracy in crop recommendation systems. The bagging model based on Random Forest showed effectiveness in this scenario, providing insights for making decisions in agriculture

COMBINATORIAL ALGORITHMS FOR GRAPH DISCOVERY AND EXPERIMENTAL DESIGN

In this thesis, we study the design and analysis of algorithms for discovering the structure and properties of an unknown graph, with applications in two different domains: causal inference and sublinear graph algorithms. In both these domains, graph discovery is possible using restricted forms of experiments, and our objective is to design low-cost experiments. First, we describe efficient experimental approaches to the causal discovery problem, which in its simplest form, asks us to identify the causal relations (edges of the unknown graph) between variables (vertices of the unknown graph) of a given system. For causal discovery, we study algorithms for the problem of learning the causal relationships between a set of observed variables in the presence of hidden or unobserved variables while minimizing a suitable cost of interventions on the observed variables. An intervention on a set of variables helps learn the presence of causal relations adjacent to them. Under various cost models for interventions, we design combinatorial algorithms for causal discovery by identifying new connections between discrete optimization, graph property testing, and efficient intervention design. Next, we investigate query-efficient experimental approaches for estimating various graph properties, such as the number of edges and graph connectivity. The access to the graph, or equivalently performing an experiment, is via a Bipartite Independent Set (BIS) oracle. The BIS oracle is related to the interventional access model used in our work for causal graph discovery, with other applications in group testing and fine-grained complexity. In this setting, we develop non-adaptive algorithms that lead to efficient implementations in highly parallelized and low-memory streaming settings

Secure and Lightweight Authentication Protocols for Devices in Internet of Things

The Internet of Things (IoT) has become an intriguing trend worldwide as it allows any smart device with an IP address to participate in a highly immersive and connected environment that integrates physical, digital and social aspects of the user’s lives. The perpetual growth of IoT devices is resulting in less attention on the security side allowing attackers to find easy ways to exploit the devices. Hence, security is one of the important and challenging research areas in IoT. Furthermore, the resource-constrained nature of these devices results in poor performance when the traditional security protocols are used. In this thesis, we propose secure and lightweight authentication protocols for devices in IoT. A centralized network model is considered where the devices in the perception layer are mutually authenticated with the gateway of the system. A mutual authentication mechanism which uses symmetric key negotiation using Elliptic Curve Diffie-Hellman(ECDH) in the registration part of the protocol to protect the credentials of the devices and at the same time it minimizes the computation cost on the devices. At the end of the authentication, key agreement based on the symmetric key cryptography is established between the sensor devices and the gateway. Further, Elliptic Curve Integrated Encryption Scheme (ECIES) method is used to avoid the possibility of man-in-the-middle attack(MITM) in the registration phase of the previous protocol. An informal security verification of the protocols is presented which proves that they are resilient against perception layer attacks. The performance evaluation based on the metrics such as execution time, communication cost, computation cost of the protocol has been performed after the protocol is simulated in the Cooja simulator under Contiki OS environment. Further, the comparison results with the existing protocols show that the proposed system is lightweight as it provides low computation cost and better execution time

Learning Scheduling Algorithms for Data Processing Clusters

Efficiently scheduling data processing jobs on distributed compute clusters requires complex algorithms. Current systems, however, use simple generalized heuristics and ignore workload characteristics, since developing and tuning a scheduling policy for each workload is infeasible. In this paper, we show that modern machine learning techniques can generate highly-efficient policies automatically. Decima uses reinforcement learning (RL) and neural networks to learn workload-specific scheduling algorithms without any human instruction beyond a high-level objective such as minimizing average job completion time. Off-the-shelf RL techniques, however, cannot handle the complexity and scale of the scheduling problem. To build Decima, we had to develop new representations for jobs' dependency graphs, design scalable RL models, and invent RL training methods for dealing with continuous stochastic job arrivals. Our prototype integration with Spark on a 25-node cluster shows that Decima improves the average job completion time over hand-tuned scheduling heuristics by at least 21%, achieving up to 2x improvement during periods of high cluster load

Epithelium detection and cervical intraepithelial neoplasia classification in digitized histology images

“Cervical cancer is one of the most deadly cancers faced by women. It is the second leading cause of cancer death in women aged 20 to 39 years. In order to detect cancer at early stages, pathologists analyze the epithelium region from the cervical histology images. These histology images have a pre-cervical cancer condition called cervical intraepithelial neoplasia (CIN) determined by pathologists. This study deals with automating the process of epithelium detection and epithelium CIN classification in digitized histology images. For epithelium detection, the objective is to detect epithelium regions in microscopy images from non-epithelium regions and background. convolutional neural networks, both shallow and deep networks are used for epithelium detection. The highest epithelium detection accuracy of 98.84% is obtained using transfer learning on VGG-19 architecture, pre-trained on the ImageNet dataset. For CIN classification, the epithelium region is divided into 5 segments along the medial axis and patches from each segment were used for training the deep learning model. Vertical segment level classification probabilities from deep learning model are obtained and further classified using SVM, LDA, MLP, logistic and RF classifiers. The highest image level accuracy obtained is 77.27% for MLP classifier using voting”--Abstract, page iii

A one-year prospective study of morbidity and mortality in first year following a hip fracture among the elderly patients

Background: Hip fractures are one of the most commonest and devastating injuries among the geriatric population. Increasing age, cognitive impairment and higher ASA scores are significantly associated with mortality among the geriatric age group. The objective of this study to evaluate the incidence and causes of morbidity and mortality associated with fracture of the hip in first year after the injury among the elderly population.Methods: A eighteen months prospective study at Narayana Medical College was conducted among patients aged >60 years treated for fracture of hip by arthroplasty and internal fixation. The cases were followed up for one year and mortality and morbidity were evaluated. Associated medical co morbidities were noted for all the cases in the study. Statistical analysis was carried out with IBM SPSS 24.0 and Stata 14 software.Results: In the study, 76 cases with M:F ratio of 7:12 and mean age of 63.21±1.4 years were included. Cardio vascular disease and anaemia were common co morbidities. Total mortality percentage in the present study after one year of follow up was 31.58%. Mean age of the cases with death in arthroplasty group was 71 years and 69 years in internal fixation group. Associated medical conditions had a direct relationship with mortality on the patients following surgery for fracture of the hip.Conclusions: Hemiarthroplasty for fracture of the neck and Internal fixation using dynamic hip screw for trochanteric fracture are still good options in the elderly. Proper post-operative management and follow up management for medical co morbidities provide better functional outcome and good results

Study of functional outcome of surgical management of proximal humerus fracture by various modalities: a two-year study at a tertiary care hospital

Background: Proximal humerus fractures account for nearly 6-10% and are on a rise. The management of this is controversial and is challenging task. There is a significant heterogeneity among the studies in describing the best surgical procedure in proximal humerus fracture. The objective of the study is to assess and compare the functional outcome with different modalities in fixation of proximal humerus shaft fractures.Methods: A two-year prospective study was conducted after getting ethical approval at Narayana Medical College on cases admitted with proximal humerus fractures as per the inclusion criteria based on Neer’s classification. Radiological evaluation was done, and surgery was performed. Postoperative follow-up was done at 1st, 4th, 8th and 14th week and outcome were evaluated for each case based on Neer’s shoulder score.Results: 30 cases were included with a mean age of 48.2 years. Road traffic injury was common cause of fracture. Of the total 30 cases, 23 cases had excellent results, 4 cases were satisfactory, 2 cases were unsatisfactory, and one case had a failure. The mean scores observed on Neer’s score was pain (33.5 units), Function (23.5 units), range of motion (16.55 units) and anatomy (6.9 units).Conclusions: Clinical evaluation, obtaining proper radiological views, age of the patient and activity holds the key for realistic approach and surgical management of complex humerus fractures. Proper patient selection and thorough knowledge of the anatomy and biomechanical principles are the pre-requisites for a successful surgery and good functional outcome

Orbits design for Leo space based solar power satellite system

Space Based Solar Power satellites use solar arrays to generate clean, green, and renewable electricity in space and transmit it to earth via microwave, radiowave or laser beams to corresponding receivers (ground stations). These traditionally are large structures orbiting around earth at the geo-synchronous altitude. This thesis introduces a new architecture for a Space Based Solar Power satellite constellation. The proposed concept reduces the high cost involved in the construction of the space satellite and in the multiple launches to the geo-synchronous altitude. The proposed concept is a constellation of Low Earth Orbit satellites that are smaller in size than the conventional system. For this application a Repeated Sun-Synchronous Track Circular Orbit is considered (RSSTO). In these orbits, the spacecraft re-visits the same locations on earth periodically every given desired number of days with the line of nodes of the spacecraft’s orbit fixed relative to the Sun. A wide range of solutions are studied, and, in this thesis, a two-orbit constellation design is chosen and simulated. The number of satellites is chosen based on the electric power demands in a given set of global cities. The orbits of the satellites are designed such that their ground tracks visit a maximum number of ground stations during the revisit period. In the simulation, the locations of the ground stations are chosen close to big cities, in USA and worldwide, so that the space power constellation beams down power directly to locations of high electric power demands. The j2 perturbations are included in the mathematical model used in orbit design. The Coverage time of each spacecraft over a ground site and the gap time between two consecutive spacecrafts visiting a ground site are simulated in order to evaluate the coverage continuity of the proposed solar power constellation. It has been observed from simulations that there always periods in which s spacecraft does not communicate with any ground station. For this reason, it is suggested that each satellite in the constellation be equipped with power storage components so that it can store power for later transmission. This thesis presents a method for designing the solar power constellation orbits such that the number of ground stations visited during the given revisit period is maximized. This leads to maximizing the power transmission to ground stations