COVID-19 pandemic has rapidly increased health crises globally and is affecting our day-to-day lifestyle. A motive for survival recommendations is to wear a safe facemask, stay protected against the transmission of coronavirus. By wearing a facemask, the most effective preventive care must be taken against COVID-19. Monitoring manually if the individuals are wearing facemask correctly and to notify the victim in public and crowded areas is a difficult task. This paper approaches a simplified way to achieve facemask detection and notifying the individual if not wearing facemask. Using Kaggle datasets, the proposed system/model is trained and examined. The system runs in real-time and detects if an individual face has facemask if not then notify the individual personally through text message. The mask is extracted from real-time faces in public and is fed as an input into convolutional neural network (CNN).

/pdf/face-mask-detection-by-using-optimistic-convolutional-neural-3o0jqy5kd7.pdf

Face Mask Detection by using Optimistic Convolutional Neural Network

A tutorial on the design and development of automatic speaker-recognition systems is presented. Automatic speaker recognition is the use of a machine to recognize a person from a spoken phrase. These systems can operate in two modes: to identify a particular person or to verify a person's claimed identity. Speech processing and the basic components of automatic speaker-recognition systems are shown and design tradeoffs are discussed. Then, a new automatic speaker-recognition system is given. This recognizer performs with 98.9% correct identification. Last, the performances of various systems are compared.

Speaker recognition : A tutorial : Automated biometrics

Research methods in machine learning play a pivotal role since the accuracy and reliability of the results are influenced by the research methods used. The main aims of this paper were to explore current research methods in machine learning, emerging themes, and the implications of those themes in machine learning research.  To achieve this the researchers analyzed a total of 100 articles published since 2019 in IEEE journals. This study revealed that Machine learning uses quantitative research methods with experimental research design being the de facto research approach. The study also revealed that researchers nowadays use more than one algorithm to address a problem. Optimal feature selection has also emerged to be a key thing that researchers are using to optimize the performance of Machine learning algorithms. Confusion matrix and its derivatives are still the main ways used to evaluate the performance of algorithms, although researchers are now also considering the processing time taken by an algorithm to execute. Python programming languages together with its libraries are the most used tools in creating, training, and testing models. The most used algorithms in addressing both classification and prediction problems are; Naive Bayes, Support Vector Machine, Random Forest, Artificial Neural Networks, and Decision Tree. The recurring themes identified in this study are likely to open new frontiers in Machine learning research.

/pdf/research-methods-in-machine-learning-a-content-analysis-5bkokmnc86.pdf

Research Methods in Machine Learning: A Content Analysis

Accurate estimation of Reference Evapotranspiration (ET0) is important for efficient management and conservation of irrigation water. Existing methods of ET0 rate determination are complex for application at the farmer level. Apart from standard methods of ET0 determination, many data-driven soft computing approaches were also proposed to determine the ET0 with limited data set. We proposed a temperature and humidity-based ML approach for ET0 rate determination on directly sensed environmental conditions of the crop field. Crop field environmental conditions for (ET0) rate determination are sensed by the proposed Internet of Things (IoT) architecture. Crop field environmental conditions from the year 2015 to 2021 in Pakistan are used for the training and testing of the proposed model. Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), k-Nearest Neighbours (KNN), and Artificial Neural Network (ANN) based models are compared for performance. Crop fields directly sensed temperature and humidity pass to the model to train and predict the ET0-rate of crop fields. The 10-fold cross-validation technique is applied for the evaluation of the proposed approach. The accuracy of the proposed solution for the ET0 rate is compared against the Food and Agriculture Organization (FAO) recommended Penman-Monteith method for ET0 rate determination. As concerned of the ML-based models the KNN model is more accurate as compared to SVM,GNB and ANN models with 92% accuracy. The KNN model of ET0 is more efficient in reducing the Root Mean Squared Errors (RMSE) by 16% and Mean Absolute Errors (MAE) by 3% against the state of the art approach.

Machine Learning Based Prediction of Reference Evapotranspiration (ET0) Using IoT

Abstract Because there is more demand for freshwater around the world and the world’s population is growing at the same time, there is a severe lack of freshwater resources in the central part of the planet. The world’s current population of 7.2 billion people is expected to grow to over 9 billion by the year 2050. The vast majority of freshwater is used for things like cooking, cleaning, and farming. Most industrialised countries are in desperate need of smart irrigation systems, which are now a must-have because of how quickly technology is improving. In article presents IoT based Sensor integrated intelligent irrigation system for agriculture industry. IoT based humidity and soil sensors are used to collect soil related data. This data is stored in a centralized cloud. Features are selected by CFS algorithm. This will help in discarding irrelevant data. Clustering of data is performed by K means algorithm. This will help in keeping similar data together. Then classification model is build using the SVM, Random Forest and Naïve Bayes algorithm. Model is trained, validated and tested using the acquired data. Historical soil and humidity related data is also used in training the model. K-means SVM hybrid classifier is achieving better results for classification, prediction of water demand and saving fresh water by intelligent irrigation. K-means SVM hybrid classifier has achieved accuracy rate of 98.5 percent. Specificity, recall and precision of K-means SVM hybrid classifier is also higher than random forest and naïve bayes classifier. 

Internet of things sensors and support vector machine integrated intelligent irrigation system for agriculture industry

The shortage of water around the world force us to minimize the usage of water. More than 75% of fresh water resources were using for irrigation purpose so efficient utilization of water in irrigation system with advanced method is required. This paper presents an advanced technology based smart system to predict the irrigation requirements of a field by sensing of ground parameter like moisture of the soil, temperature-humidity and water level using Machine learning algorithm (ML). Existing algorithms like K-mean and SVM are facing overfitting problem to overcome this problem we are using K-Nearest neighbor method along with this proposed system has a capacity to realize a fully automated irrigation scheme and discussed in detail about the information processing results with three weeks pre-defined data based on the proposed algorithm. The system is capable and the prediction results are more accurate.

Efficient Machine Learning Algorithm for Smart Irrigation

A survey on classification and clustering algorithms for uncompressed and compressed text

Object tracking is a procedure of movement estimation in computer vision application we present a combined multiple object tracking technique for a video. A video is a frame by frame sequence of images. Optical flow is a flexible representation of visual motion that is particularly suitable for computers for analyzing digital images. In this work the Horn & Schunck method is used to find the optical flow vectors which in turn pave a way for the detection and tracking of the single moving object in a video. Kalman filter removes the noise that effects a background subtracted image and predicts the position of an object accurately. A combination of Optical flow and Kalman filter method is designed in order to attain an accurate object tracking system. The accuracy of occluded object in dynamic background is promising compared to simple background subtraction. The experiments are conducted on different videos to prove the efficiency and the results are discussed. KeywordKalman Filters (KF), Optical flow (OF), Background Subtraction, Occlusion Handling.

Improved multiple object detection and tracking using KF-OF method

Mobile Ad hoc Networks (MANETs) shows a vital role for temporary and quick communication. The two routing protocols such as tree-based and mesh-based protocol provides the guarantee and successful delivery. Because of the existence of only one link between two nodes Tree-based gives low bandwidth consumption, high forwarding efficiency. A tree-based multicast routing protocol is best suitable for lightweight ad hoc networks. The QoS (Quality of Service) and performance is degraded when the load of network increases. By coalescing the benefits of the tree structure and mesh structure the effect of network load can be improved. To improve the efficiency and packet delivery ratio congestion control mechanism is introduced by transmitting the compressed data packet. This results in enhanced performance in the network even in dense capacity ad hoc networks. It is experiential that the planned scheme can attain high packet delivery ratio than the existing system.

Bandwidth optimized multicast routing algorithm based on hybrid mesh and tree structure with collision control in MANET using lempel-ziv-oberhumer method

Speaker recognition is an important branch of authenticating a speaker’s identity automatically based on human biological feature. We present a novel method of representing a speech by interval valued symbolic features. A method of speaker identification based on the proposed representation is also presented. It is an ideal choice for biometric which can change the future of speaker authentication mechanism as it is computationally effective and efficient. We also adopted LBZ-Vector Quantization technique for the purpose of speaker modelling using MFCC features. MFCC takes human perception sensitivity into consideration with respect to frequencies and therefore are best for speaker recognition. The technique of VQ consists of extracting a small number of representative feature vectors as an efficient means of characterizing the speaker specific features. The newly proposed model significantly reduces the dimension of feature vectors and also the time taken to classify a given speech utterances. In this work we provide a brief overview of the area of speaker recognition, describing applications and some underlying techniques. We will discuss some of the strengths and weaknesses of current speaker recognition technologies. We outline some potential future trends in research, development and applications.

https://www.ijsr.net/archive/v3i7/MDIwMTQxMjg1.pdf

Symbolic Representation of Speech for Text Independent Speaker Recognition

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