Face recognition: Past, present and future (a review)

A rise in the population has immensely increased the pressure on the agriculture sector. With the advent of technology, this decade is witnessing a shift from conventional approaches to the most advanced ones. The Internet of Things (IoT) has transformed both the quality and quantity of the agriculture sector. Hybridization of species along with the real-time monitoring of the farms paved a way for resource optimization. Scientists, research institutions, academicians, and most nations across the globe are moving towards the practice and execution of collaborative projects to explore the horizon of this field for serving mankind. The tech industry is racing to provide more optimal solutions. Inclusion of IoT, along with cloud computing, big data analytics, and wireless sensor networks can provide sufficient scope to predict, process, and analyze the situations and improve the activities in the real-time scenario. The concept of heterogeneity and interoperability of the devices by providing flexible, scalable, and durable methods, models are also opening new domains in this field. Therefore, this paper contributes towards the recent IoT technologies in the agriculture sector, along with the development of hardware and software systems. The public and private sector projects and startup’s started all over the globe to provide smart and sustainable solutions in precision agriculture are also discussed. The current scenario, applications, research potential, limitations, and future aspects are briefly discussed. Based on the concepts of IoT a precision farming framework is also proposed in this article.

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Recent Developments of the Internet of Things in Agriculture: A Survey

Transfer learning and ensemble learning are the new trends for solving the problem that training data and test data have different distributions. In this paper, we design an ensemble transfer learning framework to improve the classification accuracy when the training data are insufficient. First, a weighted-resampling method for transfer learning is proposed, which is named TrResampling. In each iteration, the data with heavy weights in the source domain are resampled, and the TrAdaBoost algorithm is used to adjust the weights of the source data and target data. Second, three classic machine learning algorithms, namely, naive Bayes, decision tree, and SVM, are used as the base learners of TrResampling, where the base learner with the best performance is chosen for transfer learning. To illustrate the performance of TrResampling, the TrAdaBoost and decision tree are used for evaluation and comparison on 15 UCI data sets, TrAdaBoost, ARTL, and SVM are used for evaluation and comparison on five text data sets. According to the experimental results, our proposed TrResampling is superior to the state-of-the-art learning methods on UCI data sets and text data sets. In addition, TrResampling, bagging-based transfer learning algorithm, and MultiBoosting-based transfer learning algorithm (TrMultiBoosting) are assembled in the framework, and we compare the three ensemble transfer learning algorithms with TrAdaBoost to illustrate the framework’s effective transfer ability.

Ensemble Transfer Learning Algorithm

Grey wolf optimizer (GWO) is known as one of the recent popular metaheuristic algorithms inspired from the social collaboration and team hunting activities of grey wolves in nature This algorithm benefits from stochastic operators, but it is still prone to stagnation in local optima and premature convergence when solving problems with a large number of variables (eg, clustering problems) To alleviate this shortcoming, the GWO algorithm is hybridized with the well-known tabu search (TS) To investigate the performance of the proposed hybrid GWO and TS (GWOTS), it is compared with well-regarded metaheuristics on various clustering datasets The comprehensive experiments and analysis verify that the proposed GWOTS shows an improved performance compared to GWO and can be utilized for clustering applications

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Clustering analysis using a novel locality-informed grey wolf-inspired clustering approach

Early screening of skeptical masses or breast carcinomas in mammograms is supposed to decline the mortality rate among women. This amount can be decreased more on development of the computer-aided diagnosis with reduction of false suppositions in medical informatics. Our aim is to provide a robust tumor detection system for accurate classification of breast masses using normal, abnormal, benign, or malignant classes. The breast carcinomas are classified on the basis of observed classes. This is highly dependent on feature extraction process. In propose work, a novel algorithm for classification based on the combination of top Hat transformation and gray level co-occurrence matrix with back propagation neural network. The aim of this study is to present a robust classification model for automated diagnosis of the breast tumor with reduction of false assumptions in medical informatics. The proposed method is verified on two datasets MIAS and DDSM. It is observed that rate of false positives decreased by the proposed method to improve the performance of classification, efficiently.

A novel classification scheme to decline the mortality rate among women due to breast tumor.

Automatic Face Recognition (FR) presents a challenging task in the field of pattern recognition and despite the huge research in the past several decades; it still remains an open research problem. This is primarily due to the variability in the facial images, such as non-uniform illuminations, low resolution, occlusion, and/or variation in poses. Due to its non-intrusive nature, the FR is an attractive biometric modality and has gained a lot of attention in the biometric research community. Driven by the enormous number of potential application domains, many algorithms have been proposed for the FR. This paper presents an overview of the state-of-the-art FR algorithms, focusing their performances on publicly available databases. We highlight the conditions of the image databases with regard to the recognition rate of each approach. This is useful as a quick research overview and for practitioners as well to choose an algorithm for their specified FR application. To provide a comprehensive survey, the paper divides the FR algorithms into three categories: (1) intensity-based, (2) video-based, and (3) 3D based FR algorithms. In each category, the most commonly used algorithms and their performance is reported on standard face databases and a brief critical discussion is carried out.

A review on state-of-the-art face recognition approaches

In recent era, the weighted matrix rank minimization is used to reduce image noise, promisingly. However, low-rank weighted conditions may cause oversmoothing or oversharpening of the denoised image. This demands a clever engineering algorithm. Particularly, to remove heavy noise in image is always a challenging task, specially, when there is need to preserve the fine edge structures. To attain a reliable estimate of heavy noise image, a norm weighted fusion estimators method is proposed in wavelet domain. This holds the significant geometric structure of the given noisy image during the denoising process. Proposed method is applied on standard benchmark images, and simulation results outperform the most popular rivals of noise reduction approaches, such as BM3D, EPLL, LSSC, NCSR, SAIST, and WNNM in terms of the quality measurement metric PSNR (dB) and structural analysis SSIM indices.

Image denoising with norm weighted fusion estimators

The agriculture sector holds paramount importance in Pakistan due to the intrinsic agrarian nature of the economy Pakistan has its GDP based on agriculture, however it relies on manual monitoring of crops, which is a labour intensive and ineffective method In contrast to this, several cutting edge technology-based solutions are being employed in the developed countries to enhance the crop yield with the optimal use of resources To this end, we have proposed an integrated approach for monitoring crop health using IoT, machine learning and drone technology The integration of these sensing modalities generate heterogeneous data which not only varies in nature (ie observed parameter) but also has different temporal fidelity The spatial resolution of these methods is also different, hence, the optimal integration of these sensing modalities and their implementation in practice are addressed in the proposed system In our proposed solution, the IoT sensors provide the real-time status of environmental parameters impacting the crop, and the drone platform provide the multispectral data used for generating Vegetation Indices (VIs) such as Normalized Difference vegetation Index (NDVI) for analyzing the crop health The NDVI provides information about the crop based on the chlorophyll content, which offers limited information regarding the crop health In order to obtain a rich and detailed knowledge about crop health, the variable length time series data of IoT sensors and multispectral images were converted to a fixed-sized representation to generate crop health maps A number of machine and deep learning algorithms were applied on the collected data wherein deep neural network with two hidden layers was found to be the most optimal model among all the selected models, providing an accuracy of (984%) Further, the health maps were validated through ground surveys and by agriculture experts due to the absence of reference data The proposed research is basically an indigenous, technology based agriculture solution capable of providing important insights into the crop health by extracting complementary features from multi-modal data set, and minimizing the crop ground survey effort, particularly useful when the agriculture land is large in size

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A Multi-Modal Approach for Crop Health Mapping Using Low Altitude Remote Sensing, Internet of Things (IoT) and Machine Learning

In this article, a blind data hiding reversible methodology to embed the secret data for hiding purpose into cover image is proposed. The key advantage of this research work is to resolve the privacy and secrecy issues raised during the data transmission over the internet. Firstly, data is decomposed into sub-bands using the integer wavelets. For decomposition, the Fresnelet transform is utilized which encrypts the secret data by choosing a unique key parameter to construct a dummy pattern. The dummy pattern is then embedded into an approximated sub-band of the cover image. Our proposed method reveals high-capacity and great imperceptibility of the secret embedded data. With the utilization of family of integer wavelets, the proposed novel approach becomes more efficient for hiding and retrieving process. It retrieved the secret hidden data from the embedded data blindly, without the requirement of original cover image.

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Reversible integer wavelet transform for blind image hiding method

Most of the existing singular value decomposition-based digital watermarking methods are not robust to geometric rotation, which change the pixels’ locations without maintaining the corresponding changes to the pixel’s intensity values of entire image and yield high computational cost. To answer this, we propose a digital image watermarking algorithm using the Hall property. In the proposed method, a digital watermark image is factorized into lower-triangular, upper-triangular, and permutation matrices. The permutation matrix is used as the valid key matrix for authentication of the rightful ownership of the watermark image. The product of the lower and upper triangular matrices is processed with a few iterations of the Arnold transformation to obtain the scrambled data. The scrambled data are embedded into particular sub-bands of a cover image using Wavelet transform. Our experiments show that the proposed algorithm is highly reliable and computationally efficient compared with state-of-the-art methods that are based on singular value decomposition.

Zahid Mahmood

Papers

A review on state-of-the-art face recognition approaches

Image denoising with norm weighted fusion estimators

A Multi-Modal Approach for Crop Health Mapping Using Low Altitude Remote Sensing, Internet of Things (IoT) and Machine Learning

Reversible integer wavelet transform for blind image hiding method

Digital watermarking using Hall property image decomposition method