Sameerchand Pudaruth

Bio: Sameerchand Pudaruth is an academic researcher from University of Mauritius. The author has contributed to research in topics: Wireless network & Computer science. The author has an hindex of 7, co-authored 47 publications receiving 288 citations.

Plant Leaf Recognition Using Shape Features and Colour Histogram with K-nearest Neighbour Classifiers

Abstract: Automated systems for plant recognition can be used to classify plants into appropriate taxonomies. Such information can be useful for botanists, industrialists, food engineers and physicians. In this work, a recognition system capable of identifying plants by using the images of their leaves has been developed. A mobile application was also developed to allow a user to take pictures of leaves and upload them to a server. The server runs pre-processing and feature extraction techniques on the image before a pattern matcher compares the information from this image with the ones in the database in order to get potential matches. The different features that are extracted are the length and width of the leaf, the area of the leaf, the perimeter of the leaf, the hull area, the hull perimeter, a distance map along the vertical and horizontal axes, a colour histogram and a centroid-based radial distance map. A k-Nearest Neighbour classifier was implemented and tested on 640 leaves belonging to 32 different species of plants. An accuracy of 83.5% was obtained. The system was further enhanced by using information obtained from a colour histogram which increased the recognition accuracy to 87.3%. Furthermore, our system is simple to use, fast and highly scalable.

Automatic Recognition of Medicinal Plants using Machine Learning Techniques

Abstract: The proper identification of plant species has major benefits for a wide range of stakeholders ranging from forestry services, botanists, taxonomists, physicians, pharmaceutical laboratories, organisations fighting for endangered species, government and the public at large. Consequently, this has fueled an interest in developing automated systems for the recognition of different plant species. A fully automated method for the recognition of medicinal plants using computer vision and machine learning techniques has been presented. Leaves from 24 different medicinal plant species were collected and photographed using a smartphone in a laboratory setting. A large number of features were extracted from each leaf such as its length, width, perimeter, area, number of vertices, colour, perimeter and area of hull. Several derived features were then computed from these attributes. The best results were obtained from a random forest classifier using a 10-fold cross-validation technique. With an accuracy of 90.1%, the random forest classifier performed better than other machine learning approaches such as the k-nearest neighbour, naive Bayes, support vector machines and neural networks. These results are very encouraging and future work will be geared towards using a larger dataset and high-performance computing facilities to investigate the performance of deep learning neural networks to identify medicinal plants used in primary health care. To the best of our knowledge, this work is the first of its kind to have created a unique image dataset for medicinal plants that are available on the island of Mauritius. It is anticipated that a web-based or mobile computer system for the automatic recognition of medicinal plants will help the local population to improve their knowledge on medicinal plants, help taxonomists to develop more efficient species identification techniques and will also contribute significantly in the protection of endangered species.

Authorship Attribution Using Stylometry and Machine Learning Techniques

Abstract: Plagiarism is considered to be a highly unethical activity in the academic world. Text-alignment is currently the preferred technique for estimating the degree of similarity with existing written works. Due to its dependency on other documents it becomes increasingly tedious and time-consuming to scale up to the growing number of online and offline documents. Thus, this paper aims at studying the use of stylometric features present in a document in order to verify its authorship. Two machine learning algorithms, namely k-NN and SMO, were used to predict the authenticity of the writings. A computer program consisting of 446 features was implemented. Ten PhD theses, split into different segments of 1000, 5000 and 10000 words, were used, totaling 520 documents as our corpus. Our results show that authorship attribution using stylometry method has generated an accuracy of above 90 %, except for 7-NN with 1000 words. We also showed how authorship attribution can be used to identify potential cases of plagiarism in formal writings.

Sentiment Analysis from Facebook Comments using Automatic Coding in NVivo 11

Abstract: The number and size of social networks have grown significantly as years have passed. With its 1.5 billion active users, Facebook is by far the most popular social networks on the planet. From kindergarten kids to grandparents to teenagers, Facebook attracts users of all ages, religions, personalities and social status. Facebook users are sharing their personal information, their lifestyle, their precious moments and their feelings online. In this paper, we download a set of comments from the page ‘Opposing Views’ from Facebook. These were then categorised into either a positive comment or a negative comment using the auto code feature in NVivo 11. Comments where no positive or negative sentiments are found are considered to be neutral. Out of 626 comments, 29.6% were found to contain positive sentiments while 62.0% were found to contain negative sentiments. The outcome of this work can be used by businesses to assess public reviews about their products. This will help them understand what is working and what is not. Thus, they can improve their products and respond to customer demands sufficiently quickly.

A Review of Mobile Ad hoc NETwork (MANET) Protocols and their Applications

Abstract: In this paper, an investigation on protocol issues for mobile ad hoc networks (MANET) is conducted. The capacity and relevance of nomadic computing are evident with the quick propagation of wireless devices like laptops, wireless sensors, and smartphones. A MANET is a non-temporary infrastructure, consisting of a group of mobile hosts which have no central management and dynamically create their network or connections. This type of network introduces complexities such as regular changes to the topology. The constraints of bandwidth, low energy, and storage capacities of such mobile nodes impose severe limitations on their abilities. Some nodes also cannot communicate directly as there are generally small transmission ranges within nodes in MANETs. Therefore, routing paths can be of multiple hops, and it is the responsibility of neighbouring nodes to operate as routers. A comparative analysis using qualitative techniques is performed by categorizing different routing protocols as proactive, reactive, hybrid and nature-inspired. Protocols such as the Destination-Sequenced Distance Vector (DSDV), Wireless Routing Protocol (WRP), Ad Hoc On-demand Distance Vector Routing (AODV), Zone Routing Protocol (ZRP) and AntHocNet are described to know their suitability for real-life applications. This study reveals that hybrid protocols are better as they consume less power and uses bandwidth more efficiently.

Armstrong\'s handbook of human resource management practice / Michael Armstrong

Abstract: Armstrong's Handbook of Human Resource Management Practice is the bestselling, definitive text for all HRM students and professionals. Providing a complete resource for understanding and implementing HR in relation to the needs of the business as a whole, it contains in-depth coverage of all the key areas essential to the HR function such as employment law, employee relations, learning and development, performance and reward. Accessible and to the point as ever, this fully updated 14th edition includes emerging theory and practice, embracing the most current thinking on engagement, talent management and leadership development. With updated case studies and references to academic journals, professional magazines and recent research and surveys, it also includes coverage of new approaches to topics such as job evaluation and pay structures. Armstrong's Handbook of Human Resource Management Practice is aligned with the Chartered Institute of Personnel and Development (CIPD) professional map and standards, with the sections meeting CIPD learning outcomes now even clearer than before. Comprehensive online support material for instructors, students and HR managers are included. Resources for students and professionals include multiple-choice-questions, flash cards, case studies, further reading and a glossary of HRM terms. The lecturers' manual contains session notes, discussion questions, a literature review and a complete set of PowerPoint slides.

Two-stage plant species recognition by local mean clustering and Weighted sparse representation classification

Abstract: Aiming at the difficult problem of plant leaf recognition on the large-scale database, a two-stage local similarity based classification learning (LSCL) method is proposed by combining local mean-based clustering (LMC) method and local sparse representation based classification (SRC) (LWSRC). In the first stage, LMC is applied to coarsely classifying the test sample. k nearest neighbors of the test sample, as a neighbor subset, is selected from each training class, then the local geometric center of each class is calculated. S candidate neighbor subsets of the test sample are determined with the first S smallest distances between the test sample and each local geometric center. In the second stage, LWSRC is proposed to approximately represent the test sample through a linear weighted sum of all $$k\times S$$ samples of the S candidate neighbor subsets. Experimental results on the leaf image database demonstrate that the proposed method not only has a high accuracy and low time cost, but also can be clearly interpreted.

Surveying Stylometry Techniques and Applications

Abstract: The analysis of authorial style, termed stylometry, assumes that style is quantifiably measurable for evaluation of distinctive qualities. Stylometry research has yielded several methods and tools over the past 200 years to handle a variety of challenging cases. This survey reviews several articles within five prominent subtasks: authorship attribution, authorship verification, authorship profiling, stylochronometry, and adversarial stylometry. Discussions on datasets, features, experimental techniques, and recent approaches are provided. Further, a current research challenge lies in the inability of authorship analysis techniques to scale to a large number of authors with few text samples. Here, we perform an extensive performance analysis on a corpus of 1,000 authors to investigate authorship attribution, verification, and clustering using 14 algorithms from the literature. Finally, several remaining research challenges are discussed, along with descriptions of various open-source and commercial software that may be useful for stylometry subtasks.

Classification Techniques in Machine Learning: Applications and Issues

Abstract: Classification is a data mining (machine learning) technique used to predict group membership for data instances. There are several classification techniques that can be used for classification purpose. In this paper, we present the basic classification techniques. Later we discuss some major types of classification method including Bayesian networks, decision tree induction, k-nearest neighbor classifier and Support Vector Machines (SVM) with their strengths, weaknesses, potential applications and issues with their available solution. The goal of this study is to provide a comprehensive review of different classification techniques in machine learning. This work will be helpful for both academia and new comers in the field of machine learning to further strengthen the basis of classification methods.

Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach

Abstract: The timely identification and early prevention of crop diseases are essential for improving production. In this paper, deep convolutional-neural-network (CNN) models are implemented to identify and diagnose diseases in plants from their leaves, since CNNs have achieved impressive results in the field of machine vision. Standard CNN models require a large number of parameters and higher computation cost. In this paper, we replaced standard convolution with depth=separable convolution, which reduces the parameter number and computation cost. The implemented models were trained with an open dataset consisting of 14 different plant species, and 38 different categorical disease classes and healthy plant leaves. To evaluate the performance of the models, different parameters such as batch size, dropout, and different numbers of epochs were incorporated. The implemented models achieved a disease-classification accuracy rates of 98.42%, 99.11%, 97.02%, and 99.56% using InceptionV3, InceptionResNetV2, MobileNetV2, and EfficientNetB0, respectively, which were greater than that of traditional handcrafted-feature-based approaches. In comparison with other deep-learning models, the implemented model achieved better performance in terms of accuracy and it required less training time. Moreover, the MobileNetV2 architecture is compatible with mobile devices using the optimized parameter. The accuracy results in the identification of diseases showed that the deep CNN model is promising and can greatly impact the efficient identification of the diseases, and may have potential in the detection of diseases in real-time agricultural systems.