An Efficient Algorithm for Recognition of Human Actions

Recognition of human actions is an emerging need. Various researchers have endeavored to provide a solution to this problem. Some of the current state-of-the-art solutions are either inaccurate or computationally intensive while others require human intervention. In this paper a sufficiently accurate while computationally inexpensive solution is provided for the same problem. Image moments which are translation, rotation, and scale invariant are computed for a frame. A dynamic neural network is used to identify the patterns within the stream of image moments and hence recognize actions. Experiments show that the proposed model performs better than other competitive models

Speak Pakistan: Challenges in Developing Pakistan Sign Language using Information Technology

Gesture based communication called Sign Language (SL) is the fundamental communication channel between hard of hearing individuals. Communication through signing is a visual motion dialect. Hard of hearing individuals use gesture based communication as their primary medium for correspondence. Different countries have their own sign language as the United States of America has American Sign Language (ASL), China has Chinese Sign Language (CSL), India has Indian Sign Language (ISL), and similarly Pakistan has Pakistan Sign Language (PSL). Most of the developed nations have addressed the issues of their hearing impaired people by launching projects involving Information Technology to reduce this gap between a deaf and a normal person. In central and south Asia, a considerable work has been conducted on ISL and CSL. However, Pakistan Sign Language is a linguistically under-investigated in the absence of any structured information about the language contents, grammar, and tools and services for communication. Hence, the major contributions of this research are to highlight the challenges to bridge this communication gap for Pakistani deaf community by using the existing literature, and to propose an Information Technology based architectural framework to identify major components to build applications which may help bridging the gap between the deaf and normal people of the country

A Roadmap to Elevate Pakistan Sign Language among Regional Sign Languages

Several nations have worked hard to make their societies truly inclusive by developing gadgets, tools, services, and applications. A lot of work has been done for bringing the deaf community to the mainstream. Tools and applications exist for several sign languages including American Sign Language (ASL), Chinese Sign Language (CSL), Indian Sign Language (ISL) and Arabic Sign Language (ArSL). These tools help translating natural language text into respective sign language and vice versa. Similarly, standard corpora exist for all afore-mentioned sign languages and for many other languages. Unfortunately, no such noticeable development exists in the case of Pakistan Sign Language (PSL). This research aims to define a roadmap for the development of Pakistan Sign Language so as to bring it at par with other sign languages of the world.&nbsp

Detection of COVID-19 in Chest X-ray Images: A Big Data Enabled Deep Learning Approach

Coronavirus disease (COVID-19) spreads from one person to another rapidly. A recently discovered coronavirus causes it. COVID-19 has proven to be challenging to detect and cure at an early stage all over the world. Patients showing symptoms of COVID-19 are resulting in hospitals becoming overcrowded, which is becoming a significant challenge. Deep learning’s contribution to big data medical research has been enormously beneficial, offering new avenues and possibilities for illness diagnosis techniques. To counteract the COVID-19 outbreak, researchers must create a classifier distinguishing between positive and negative corona-positive X-ray pictures. In this paper, the Apache Spark system has been utilized as an extensive data framework and applied a Deep Transfer Learning (DTL) method using Convolutional Neural Network (CNN) three architectures —InceptionV3, ResNet50, and VGG19—on COVID-19 chest X-ray images. The three models are evaluated in two classes, COVID-19 and normal X-ray images, with 100 percent accuracy. But in COVID/Normal/pneumonia, detection accuracy was 97 percent for the inceptionV3 model, 98.55 percent for the ResNet50 Model, and 98.55 percent for the VGG19 model, respectively

CONSTRAINT BASED NLP ENGINE

Visual representations are always better than narrations in accordance to children, for better understanding. This is quite advantageous in learning school lessons and it eventually helps in engaging the children and enhancing their imaginative skills. Using natural language processing techniques and along the computer graphics it is possible to bridge the gap between these two individual fields, it will not only eliminate the existing manual labor involved instead it can also give rise to efficient and effective system frameworks that can form a foundation for complex applications. In this paper we present an architecture to design for a NLP engine that can be used for 3D scene generation, the input would be in textual form that would be processed by each module of the natural language processing (NLP) engine. This text would be restricted in terms of the constraint based grammar (CBG), eliminating the maximum occurrence of any ambiguity and easing the noun fragmentation process. Eventually, the output of the NLP engine would be a sentence that fulfills the custom grammatical rules

Deep Reinforcement Learning for Anomaly Detection: A Systematic Review

Anomaly detection has been used to detect and analyze anomalous elements from data for years. Various techniques have been developed to detect anomalies. However, the most convenient one is Machine learning which is performing well but still has limitations for large-scale unlabeled datasets. Deep Reinforcement Learning (DRL) based techniques outperform the existing supervised or unsupervised and other alternative techniques for anomaly detection. This study presents a Systematic Literature Review (SLR), which analyzes DRL models that detect anomalies in their application. This SLR aims to analyze the DRL frameworks for anomaly detection applications, proposed DRL methods, and their performance comparisons against alternative methods. In this review, we have identified 32 research articles published from 2017–2022 that discuss DRL techniques for various anomaly detection applications. After analyzing the selected research articles, this paper presents 13 different applications of anomaly detection found in the selected research articles. We identified 50 different datasets applied in experiments on anomaly detection and demonstrated 17 distinct DRL models used in the selected papers to detect anomalies. Finally, we analyzed the performance of these DRL models and reviewed them. Additionally, we observed that detecting anomalies using DRL frameworks is a promising area of research and showed that DRL had shown better performance for anomaly detection where other models lack. Therefore, we provide researchers with recommendations and guidelines based on this review

A multivariate analysis of health risk assessment, phytoremediation potential, and biochemical attributes of Spinacia oleracea exposed to cadmium in the presence of organic amendments under hydroponic conditions

Cadmium (Cd) phytoremediation potential and its accumulation in edible and nonedible plant tissues is the function of various biochemical processes taking place inside plants. This study assessed the impact of organic ligands on Cd phyto uptake and different biophysiochemical processes of Spinacia oleracea L., and associated health hazards. Plants were exposed to Cd alone and chelated with citric acid (CA) and ethylenediaminetetraacetic acid (EDTA). Results revealed that the effect of Cd on lipid peroxidation, H2O2 production and pigment contents varied greatly with its applied level and the type of organic ligand. Moreover, the effect was more prominent in root tissues than leaf tissues and for high concentrations of Cd and organic ligands. Cadmium accumulation increased by 90 and 74% in roots and leaves, respectively, with increasing Cd levels (25–100 µM). Cadmium exposure at high levels caused lipid peroxidation in roots only. Application of both CA and EDTA slightly diminished Cd toxicity with respect to pigment contents, lipid peroxidation and hydrogen peroxide (H2O2) contents. Hazard quotient (HQ) of Cd was \u3c1.00 for all the treatments. Under nonlinear effect of treatments, multivariate analysis can be an effective tool to trace overall effects/trends

Mechanisms of metal-phosphates formation in the rhizosphere soils of pea and tomato: environmental and sanitary consequences

International audiencePurpose At the global scale, soil contamination with persistent metals such as lead (Pb), zinc (Zn), and copper (Cu) induces a serious threat of entering the human food chain. In the recent past, different natural and synthetic compounds have been used to immobilizemetals in soil environments. However, the mechanisms involved in amendment-induced immobilization of metals in soil remained unclear. The objective of the present work was therefore to determine the mechanisms involved in metal-phosphates formation in the rhizospheric soils of pea and tomato currently cultivated in kitchen gardens. Materials and methods Pea and tomato were cultivated on a soil polluted by past industrial activities with Pb and Zn under two kinds of phosphate (P) amendments: (1) solid hydroxyapatite and (2) KH2PO4. The nature and quantities of metal-P formed in the rhizospheric soils were studied by using the selective chemical extractions and employing the combination of X-ray fluorescence micro-spectroscopy, scanning electron microscopy, and electron microprobe methods. Moreover, the influence of soil pH and organic acids excreted by plant roots on metal-P complexes formation was studied. Results and discussion Our results demonstrated that P amendments have no effect on metal-P complex formation in the absence of plants. But, in the presence of plants, P amendments cause Pb and Zn immobilization by forming metal-P complexes. Higher amounts of metal-P were formed in the pea rhizosphere compared to the tomato rhizosphere and in the case of soluble P compared to the solid amendment. The increase in soil-metal contact time enhanced metal-P formation. Conclusions The different forms of metal-P formed for the different plants under two kinds of P amendments indicate that several mechanisms are involved in metal immobilization. Metal-P complex formation in the contaminated soil depends on the type of P amendment added, duration of soil-plant contact, type of plant species, and excretion of organic acids by the plant roots in the rhizosphere