•A contrast stretching technique is proposed to enhance the contrast of infected region.•Construction of a codebook using an improved texture, color, and geometric features.•Implement a feature ...selection technique based on PCA, skewness, and entropy.•Preparing the database of diseases images for citrus leaves.
In agriculture, plant diseases are primarily responsible for the reduction in production which causes economic losses. In plants, citrus is used as a major source of nutrients like vitamin C throughout the world. However, ‘Citrus’ diseases badly effect the production and quality of citrus fruits. From last decade, the computer vision and image processing techniques have been widely used for detection and classification of diseases in plants. In this article, we propose a hybrid method for detection and classification of diseases in citrus plants. The proposed method consists of two primary phases; (a) detection of lesion spot on the citrus fruits and leaves; (b) classification of citrus diseases. The citrus lesion spots are extracted by an optimized weighted segmentation method, which is performed on an enhanced input image. Then, color, texture, and geometric features are fused in a codebook. Furthermore, the best features are selected by implementing a hybrid feature selection method, which consists of PCA score, entropy, and skewness-based covariance vector. The selected features are fed to Multi-Class Support Vector Machine (M-SVM) for final citrus disease classification. The proposed technique is tested on Citrus Disease Image Gallery Dataset, Combined dataset (Plant Village and Citrus Images Database of Infested with Scale), and our own collected images database. We used these datasets for detection and classification of citrus diseases namely anthracnose, black spot, canker, scab, greening, and melanose. The proposed technique outperforms the existing methods and achieves 97% classification accuracy on citrus disease image gallery dataset, 89% on combined dataset and 90.4% on our local dataset.
The detection of clinically relevant disease-specific biomolecules, including nucleic acids, circulating tumor cells, proteins, antibodies, and extracellular vesicles, has been indispensable to ...understand their functions in disease diagnosis and prognosis. Therefore, a biosensor for the robust, ultrasensitive, and selective detection of these low-abundant biomolecules in body fluids (blood, urine, and saliva) is emerging in current clinical research. In recent years, nanomaterials, especially superparamagnetic nanomaterials, have played essential roles in biosensing due to their intrinsic magnetic, electrochemical, and optical properties. However, engineered multicomponent magnetic nanoparticle-based current biosensors that offer the advantages of excellent stability in a complex biomatrix; easy and alterable biorecognition of ligands, antibodies, and receptor molecules; and unified point-of-care integration have yet to be achieved. This review introduces the recent advances in superparamagnetic nanostructures for electrochemical and optical biosensing for disease-specific biomarkers. This review emphasizes the synthesis, biofunctionalization, and intrinsic properties of nanomaterials essential for robust, ultrasensitive biosensing. With a particular emphasis on nanostructure-based electrochemical and optical detection of disease-specific biomarkers such as nucleic acids (DNA and RNA), proteins, autoantibodies, and cells, this review also chronicles the needs and challenges of nanoarchitecture-based detection. These summaries provide further insights for researchers to inspire their future work on the development of nanostructures for integrating into biosensing and devices for a broad field of applications in analytical sensing and in clinic.
Synthesis, bio-functionalization, and multifunctional activities of superparamagnetic-nanostructures have been extensively reviewed with a particular emphasis on their uses in a range of disease-specific biomarker detection and associated challenges.
In video sequences, human action recognition is a challenging problem due to motion variation, in frame person difference, and setting of video recording in the field of computer vision. Since last ...few years, applications of human activity recognition have increased significantly. In the literature, many techniques are implemented for human action recognition, but still they face problem in contrast of foreground region, segmentation, feature extraction, and feature selection. This article contributes a novel human action recognition method by embedding the proposed frames fusion working on the principle of pixels similarity. An improved hybrid feature extraction increases the recognition rate and allows efficient classification in the complex environment. The design consists of four phases, (a) enhancement of video frames (b) threshold-based background subtraction and construction of saliency map (c) feature extraction and selection (d) neural network (NN) for human action classification. Results have been tested using five benchmark datasets including Weizmann, KTH, UIUC, Muhavi, and WVU and obtaining recognition rate 97.2, 99.8, 99.4, 99.9, and 99.9%, respectively. Contingency table and graphical curves support our claims. Comparison with existent techniques identifies the recognition rate and trueness of our proposed method.
The availability of sufficient, affordable and environmentally benign energy is one of the major challenges worldwide. This study was performed to evaluate the feasibility of poultry waste for energy ...generation in Pakistan. The adoption of renewable energy sources is of paramount importance towards the energy security. This paper reports the potential of energy generation from poultry waste. To date, twenty five thousand poultry farms are operating to fulfill the protein demand of the population, which is increasing day by day and a huge amount of waste is produced from poultry farming in Pakistan. The waste generated from poultry farming is estimated and technology for the conversion of poultry waste into biogas is discussed and finally, the electricity generation based on poultry waste is estimated. A 280MWh/day of electricity can be generated from the biogas produced from poultry waste and this adaptation would be a valuable addition of renewable energy in country existing energy system. Currently, there is a lack of infrastructure in Pakistan to adopt alternate and renewable energy sources. The researcher, businessman, venture capital and Government policies collectively could initiate the renewable energy disposition. Initially, a public-private partnership could be best practice to initiate this technology at the farm level. The utilization of poultry waste for energy generation is feasible and environmentally benign. The Government financial initiatives and technical support under the renewable energy policies is of paramount importance of adoption of this technology in Pakistan.
Human activity monitoring in the video sequences is an intriguing computer vision domain which incorporates colossal applications, e.g., surveillance systems, human-computer interaction, and traffic ...control systems. In this research, our primary focus is in proposing a hybrid strategy for efficient classification of human activities from a given video sequence. The proposed method integrates four major steps: (a) segment the moving objects by fusing novel uniform segmentation and expectation maximization, (b) extract a new set of fused features using local binary patterns with histogram oriented gradient and Harlick features, (c) feature selection by novel Euclidean distance and joint entropy-PCA-based method, and (d) feature classification using multi-class support vector machine. The three benchmark datasets (MIT, CAVIAR, and BMW-10) are used for training the classifier for human classification; and for testing, we utilized multi-camera pedestrian videos along with MSR Action dataset, INRIA, and CASIA dataset. Additionally, the results are also validated using dataset recorded by our research group. For action recognition, four publicly available datasets are selected such as Weizmann, KTH, UIUC, and Muhavi to achieve recognition rates of 95.80, 99.30, 99, and 99.40%, respectively, which confirm the authenticity of our proposed work. Promising results are achieved in terms of greater precision compared to existing techniques.
A wireless sensor network (WSN) has achieved significant importance in tracking different physical or environmental conditions using wireless sensor nodes. Such types of networks are used in various ...applications including smart cities, smart building, military target tracking and surveillance, natural disaster relief, and smart homes. However, the limited power capacity of sensor nodes is considered a major issue that hampers the performance of a WSN. A plethora of research has been conducted to reduce the energy consumption of sensor nodes in traditional WSN, however the limited functional capability of such networks is the main constraint in designing sophisticated and dynamic solutions. Given this, software defined networking (SDN) has revolutionized traditional networks by providing a programmable and flexible framework. Therefore, SDN concepts can be utilized in designing energy-efficient WSN solutions. In this paper, we exploit SDN capabilities to conserve energy consumption in a traditional WSN. To achieve this, an energy-aware multihop routing protocol (named EASDN) is proposed for software defined wireless sensor network (SDWSN). The proposed protocol is evaluated in a real environment. For this purpose, a test bed is developed using Raspberry Pi. The experimental results show that the proposed algorithm exhibits promising results in terms of network lifetime, average energy consumption, the packet delivery ratio, and average delay in comparison to an existing energy efficient routing protocol for SDWSN and a traditional source routing algorithm.
Human Action Recognition (HAR) is an active research topic in machine learning for the last few decades. Visual surveillance, robotics, and pedestrian detection are the main applications for action ...recognition. Computer vision researchers have introduced many HAR techniques, but they still face challenges such as redundant features and the cost of computing. In this article, we proposed a new method for the use of deep learning for HAR. In the proposed method, video frames are initially pre-processed using a global contrast approach and later used to train a deep learning model using domain transfer learning. The Resnet-50 Pre-Trained Model is used as a deep learning model in this work. Features are extracted from two layers: Global Average Pool (GAP) and Fully Connected (FC). The features of both layers are fused by the Canonical Correlation Analysis (CCA). Then features are selected using the Shanon Entropy-based threshold function. The selected features are finally passed to multiple classifiers for final classification. Experiments are conducted on five publicly available datasets as IXMAS, UCF Sports, YouTube, UT-Interaction, and KTH. The accuracy of these data sets was 89.6%, 99.7%, 100%, 96.7% and 96.6%, respectively. Comparison with existing techniques has shown that the proposed method provides improved accuracy for HAR. Also, the proposed method is computationally fast based on the time of execution.
Saffron, the "golden spice" derived from Crocus sativus L., is renowned for its richness in secondary metabolites such as crocin and safranal, contributing to its unique properties. Facing challenges ...like decreasing global production, optimizing cultivation techniques becomes imperative for enhanced yields. Although the impact of factors like planting density, planting depth, spacing, and corm size on saffron growth has been studied, the interaction between corm size and planting depth remains underexplored. This study systematically investigates the interactive effects of corm size and planting depth on saffron growth and yield, providing evidence-based guidelines for optimizing cultivation. A factorial experiment, employing a completely randomized design, was conducted to assess the influence of corm size (05-10g, 10.1-15g, 15.1-20g) and planting depth (10cm, 15cm, 20cm) on saffron yield. Uniform-sized corms were obtained, and a suitable soil mixture was prepared for cultivation. Morphological and agronomic parameters were measured, and statistical analyses were performed using ANOVA and Tukey's HSD test. The study revealed that planting depth significantly affected saffron emergence. The corms sown under 15cm depth showed 100% emergence regardless of corm size (either 05-10g, 10.1-15g, 15.1-20g) followed by 10cm depth corms. Corm dry weight exhibited a complex interaction, where larger corms benefited from deeper planting, while intermediate-sized corms thrived at shallower depths. Similar patterns were observed in shoot fresh weight and dry weight. Specifically, the largest corm size (t3, 15.1-20g) produced the greatest fresh-weight biomass at the deepest planting depth of 20cm (T3), while intermediate-sized corms (t2, 10.1-15g) were superior at the shallowest 10cm depth (T1). The total plant biomass demonstrated that larger corms excelled in deeper planting, while intermediate-sized corms were optimal at moderate depths. This research highlights the intricate interplay between corm size and planting depth in influencing saffron growth. Larger corms generally promote higher biomass, but the interaction with planting depth is crucial. Understanding these dynamics can aid farmers in tailoring cultivation practices for optimal saffron yields. The study emphasizes the need for a coordinated approach to corm selection and depth placement, providing valuable insights for sustainable saffron production and economic growth.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Hearing impairment (HI) is a heterogeneous condition that affects many individuals globally with different age groups. HI is a genetically and phenotypically heterogeneous disorder. Over the last ...several years, many genes/loci causing rare autosomal recessive and dominant forms of hearing impairments have been identified, involved in various aspects of ear development. In the current study, two affected individuals of a consanguineous family exhibiting autosomal recessive nonsyndromic hearing impairment (AR-NSHI) were clinically and genetically characterized. The single affected individual (IV-2) of the family was subjected to whole-exome sequencing (WES) accompanied by traditional Sanger sequencing. Clinical examinations using air conduction audiograms of both the affected individuals showed profound hearing loss across all frequencies. WES revealed a homozygous missense variant (c.44G>C) in the SIX5 gene located on chromosome 19q13.32. We report the first case of autosomal recessive NSHI due to a biallelic missense variant in the SIX5 gene. This report further supports the evidence that the SIX5 variant might cause profound HI and supports its vital role in auditory function. Identification of novel candidate genes might help in application of future gene therapy strategies that may be implemented for NSHI, such as gene replacement using cDNA, gene silencing using RNA interference, and gene editing using the CRISPR/Cas9 system.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Human gait recognition (HGR) has received a lot of attention in the last decade as an alternative biometric technique. The main challenges in gait recognition are the change in in-person view angle ...and covariant factors. The major covariant factors are walking while carrying a bag and walking while wearing a coat. Deep learning is a new machine learning technique that is gaining popularity. Many techniques for HGR based on deep learning are presented in the literature. The requirement of an efficient framework is always required for correct and quick gait recognition. We proposed a fully automated deep learning and improved ant colony optimization (IACO) framework for HGR using video sequences in this work. The proposed framework consists of four primary steps. In the first step, the database is normalized in a video frame. In the second step, two pre-trained models named ResNet101 and InceptionV3 are selected and modified according to the dataset's nature. After that, we trained both modified models using transfer learning and extracted the features. The IACO algorithm is used to improve the extracted features. IACO is used to select the best features, which are then passed to the Cubic SVM for final classification. The cubic SVM employs a multiclass method. The experiment was carried out on three angles (0, 18, and 180) of the CASIA B dataset, and the accuracy was 95.2, 93.9, and 98.2 percent, respectively. A comparison with existing techniques is also performed, and the proposed method outperforms in terms of accuracy and computational time.
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