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Linking mesohabitat selection and ecological traits of a fish assemblage in a small tropical stream (Tinggi River, Pahang Basin) of the Malay Peninsula

Kano Y, Miyazaki Y, Tomiyama Y, Mitsuyuki C, Nishida S, Rashid ZA

Zoolog Sci, 2013 Mar;30(3):178-84.
PMID: 23480377 DOI: 10.2108/zsj.30.178

Mesohabitat selection in fluvial fishes was studied in a small tropical stream of the Malay Peninsula. A total of 681 individuals representing 24 species were sampled at 45 stations within heterogeneous stream (ca. 1 km in length), in which water depth, water velocity, substrate size, and riparian canopy cover were measured as environmental variables. A canonical correspondence analysis (CCA) yielded a diagram that shows a specific mesohabitat selection of the fish assemblage, in which the species were plotted widely on the CCA1-CCA2 biplot. Generalized linear model also revealed a significant pattern of the mesohabitat selection of several species. Water velocity and substrate size mainly separated on CCA1, indicating variation of pool (deep, slow-flow section) and riffle (shallow, fast-flow section) structures is a primary factor of mesohabitat selection in the fluvial fish assemblage. The mean body weight of species significantly correlated with CCA1; larger species tended to inhabit pools, while small ones occupied riffles. The riparian canopy cover separated on CCA2. The trophic level of species significantly correlated with CCA2; herbivorous species (low trophic level) selected open sites without riparian cover, whereas omnivorous/carnivorous (middle-high trophic level) species preferred highly covered sites. In conclusion, our results suggest that mesohabitat selection is closely related to the species feeding habit, which is consistent with the results of previous studies.

Matched MeSH terms: Human Activities
ADA-SR: Activity detection and analysis using security robots for reliable workplace safety

Zhang G, Jing W, Tao H, Rahman MA, Salih SQ, Al-Saffar A, et al.

Work, 2021;68(3):935-943.
PMID: 33612535 DOI: 10.3233/WOR-203427

BACKGROUND: Human-Robot Interaction (HRI) has become a prominent solution to improve the robustness of real-time service provisioning through assisted functions for day-to-day activities. The application of the robotic system in security services helps to improve the precision of event detection and environmental monitoring with ease.
OBJECTIVES: This paper discusses activity detection and analysis (ADA) using security robots in workplaces. The application scenario of this method relies on processing image and sensor data for event and activity detection. The events that are detected are classified for its abnormality based on the analysis performed using the sensor and image data operated using a convolution neural network. This method aims to improve the accuracy of detection by mitigating the deviations that are classified in different levels of the convolution process.
RESULTS: The differences are identified based on independent data correlation and information processing. The performance of the proposed method is verified for the three human activities, such as standing, walking, and running, as detected using the images and sensor dataset.
CONCLUSION: The results are compared with the existing method for metrics accuracy, classification time, and recall.

Matched MeSH terms: Human Activities
Fulltext THE EFFECTIVENESS OF ADSORBENT DERIVED FROM MORINGA OLEIFERA AS A WATER TREATMENT AGENT

Farasyahida A. Samad, Wan Salida Wan Mansor, idayatul Aini Zakaria

UMT Journal of Undergraduate Research, 2019;1(4):51-60.
MyJurnal

Clean, safe and readily available water is very crucial in everyday life, especially for health, hygiene, and the productivity of the community. Unfortunately, increase in contaminants in water supplies from human activities and industrialization is very worrying. Conventional wastewater treatment includes the usage of alum that will affect health with prolonged consumption. This research was carried out to focus on the development of wastewater treatment system using adsorbent from Moringa oleifera seeds. Adsorbent was successfully synthesized from the seeds of Moringa oleifera. Characterization of the sample was made using X-Ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscope (SEM), while the effectiveness of water treatment was analyzed using Turbidity Meter. Then, all samples were tested against kaolin wastewater. XRD results showed that all the adsorbent samples were amorphous in nature. FTIR results indicated that there were hydroxyl group and carboxylic group in the sample representing numerous oxygen-riddled functional groups on the surface. From SEM results, it was clearly shown that the pore structure and size of Moringa oleifera affected the capability of adsorption where the smaller the size, the more effective the sample. Turbidity test showed that the sample that worked best for wastewater treatment was adsorbent from Moringa oleifera seeds in size of 125µm that was heated for 4 hours with 93.76% turbidity removal. Therefore, this study proved that the adsorbent from Moringa oleifera seeds is very suitable for high turbidity wastewater treatment. Further studies investigating the combination of conventional activated carbon with adsorbent from Moringa oleifera seeds should be conducted before these samples are made available for further use so that we can compare which sample works best for wastewater treatment.

Matched MeSH terms: Human Activities
Fulltext General Perceptions and Awareness Level among Local Residents in Penang Island toward Bats Conservation Efforts

Shafie NJ, Sah SAM, Mutalib AHA, Fadzly N

Trop Life Sci Res, 2017 Jul;28(2):31-44.
PMID: 28890759 MyJurnal DOI: 10.21315/tlsr2017.28.2.3

The population of bats has declined from year to year caused by human activities such as logging and hunting activities. Since the human factor is linked to the issues of population decline in many animal species, a community-based conservation strategy that involved local communities is needed. We conducted face-to-face surveys among residents in Penang Island to assess knowledge and awareness level toward bats conservation efforts. We collected demographic values such as age, gender, level of education, length of residency as well as their monthly income, since different group in these variable might have different perception. We found that age groups, level of education and monthly income have shown significant differences among the respondents. However, no other significant differences were indicated for by gender and length of residency. Respondent's knowledge of bats showed that the majority of the respondents were less likely to value the importance of bats in the ecosystem. We recommended stronger legal system, earlier exposure towards environmental education, well-planned urbanisation implementation and long-term monitoring programs to strengthen efforts in conserving bats in Malaysia.

Matched MeSH terms: Human Activities
Fulltext Phylogenetic Relatedness of Several Agarwood-Producing Taxa (Thymelaeaceae) from Indonesia

Lee SY, Turjaman M, Mohamed R

Trop Life Sci Res, 2018 Jul;29(2):13-28.
PMID: 30112138 MyJurnal DOI: 10.21315/tlsr2018.29.2.2

Indonesia is home to several tree taxa that are harvested for agarwood. This highly valuable oleoresin ironically was the cause for some species to become vulnerable due to gluttonous human activity. However, information on the genetic diversity of these endangered trees is limited. In this study, 28 specimens representing eight species from two genera, Aquilaria and Gyrinops, were collected from ex-situ and in-situ populations in Indonesia. Phylogenetic analysis conducted on DNA sequences of the nuclear ribosomal internal transcribed spacer (ITS) and the trnL-trnF intergenic spacer regions, revealed that Aquilaria and Gyrinops are paraphyletic when Aquilaria cumingiana is excluded. The phylogenetic analysis for ITS and trnL-trnF showed capability to categorise agarwood-producing species based on their regions: East Indonesia and West Indonesia, using Wallace's Line as the divider. In addition, we discuss challenges in species identification and taxonomy of agarwood-producing genera, and their conservation efforts in Indonesia.

Matched MeSH terms: Human Activities
Ultrastructural changes of cell morphology and viral morphogenesis of two ecotypes of dengue virus infection in human monocytic U-937 cell

Fish-Low CY, Abu Bakar S, Othman F, Chee HY

Trop Biomed, 2018 Dec 01;35(4):1154-1159.
PMID: 33601863

Dengue virus (DENV) is maintained and circulated in both sylvatic/enzootic and endemic/human cycles and spill over infection of sylvatic DENV into human populations has been reported. Extensive deforestation and increase human activities in forest may increase the risk of human exposure to sylvatic dengue infection and this may become a threat to human. Present study investigated the changes in cell morphology and viral morphogenesis upon infection with sylvatic and endemic ecotypes in human monocytic U-937 cells using transmission electron microscopy. Autophagy, a process that is either pro-viral or anti-viral, was observed in U-937 cells of both infections, however only the replication of endemic DENV was evidenced. An insight into the infection responses of sylvatic progenitors of DENV in susceptible host cells may provide better understanding on dengue emergence in human populations.

Matched MeSH terms: Human Activities
Apes in the Anthropocene: flexibility and survival

Hockings KJ, McLennan MR, Carvalho S, Ancrenaz M, Bobe R, Byrne RW, et al.

Trends Ecol Evol, 2015 Apr;30(4):215-22.
PMID: 25766059 DOI: 10.1016/j.tree.2015.02.002

We are in a new epoch, the Anthropocene, and research into our closest living relatives, the great apes, must keep pace with the rate that our species is driving change. While a goal of many studies is to understand how great apes behave in natural contexts, the impact of human activities must increasingly be taken into account. This is both a challenge and an opportunity, which can importantly inform research in three diverse fields: cognition, human evolution, and conservation. No long-term great ape research site is wholly unaffected by human influence, but research at those that are especially affected by human activity is particularly important for ensuring that our great ape kin survive the Anthropocene.

Matched MeSH terms: Human Activities*
Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms

He Q, Shahabi H, Shirzadi A, Li S, Chen W, Wang N, et al.

Sci Total Environ, 2019 May 01;663:1-15.
PMID: 30708212 DOI: 10.1016/j.scitotenv.2019.01.329

Landslides are major hazards for human activities often causing great damage to human lives and infrastructure. Therefore, the main aim of the present study is to evaluate and compare three machine learning algorithms (MLAs) including Naïve Bayes (NB), radial basis function (RBF) Classifier, and RBF Network for landslide susceptibility mapping (LSM) at Longhai area in China. A total of 14 landslide conditioning factors were obtained from various data sources, then the frequency ratio (FR) and support vector machine (SVM) methods were used for the correlation and selection the most important factors for modelling process, respectively. Subsequently, the resulting three models were validated and compared using some statistical metrics including area under the receiver operating characteristics (AUROC) curve, and Friedman and Wilcoxon signed-rank tests The results indicated that the RBF Classifier model had the highest goodness-of-fit and performance based on the training and validation datasets. The results concluded that the RBF Classifier model outperformed and outclassed (AUROC = 0.881), the NB (AUROC = 0.872) and the RBF Network (AUROC = 0.854) models. The obtained results pointed out that the RBF Classifier model is a promising method for spatial prediction of landslide over the world.

Matched MeSH terms: Human Activities
Fulltext Air quality changes during the COVID-19 lockdown over the Yangtze River Delta Region: An insight into the impact of human activity pattern changes on air pollution variation

Li L, Li Q, Huang L, Wang Q, Zhu A, Xu J, et al.

Sci Total Environ, 2020 Aug 25;732:139282.
PMID: 32413621 DOI: 10.1016/j.scitotenv.2020.139282

The outbreak of COVID-19 has spreaded rapidly across the world. To control the rapid dispersion of the virus, China has imposed national lockdown policies to practise social distancing. This has led to reduced human activities and hence primary air pollutant emissions, which caused improvement of air quality as a side-product. To investigate the air quality changes during the COVID-19 lockdown over the YRD Region, we apply the WRF-CAMx modelling system together with monitoring data to investigate the impact of human activity pattern changes on air quality. Results show that human activities were lowered significantly during the period: industrial operations, VKT, constructions in operation, etc. were significantly reduced, leading to lowered SO2, NOx, PM2.5 and VOCs emissions by approximately 16-26%, 29-47%, 27-46% and 37-57% during the Level I and Level II response periods respectively. These emission reduction has played a significant role in the improvement of air quality. Concentrations of PM2.5, NO2 and SO2 decreased by 31.8%, 45.1% and 20.4% during the Level I period; and 33.2%, 27.2% and 7.6% during the Level II period compared with 2019. However, ozone did not show any reduction and increased greatly. Our results also show that even during the lockdown, with primary emissions reduction of 15%-61%, the daily average PM2.5 concentrations range between 15 and 79 μg m-3, which shows that background and residual pollutions are still high. Source apportionment results indicate that the residual pollution of PM2.5 comes from industry (32.2-61.1%), mobile (3.9-8.1%), dust (2.6-7.7%), residential sources (2.1-28.5%) in YRD and 14.0-28.6% contribution from long-range transport coming from northern China. This indicates that in spite of the extreme reductions in primary emissions, it cannot fully tackle the current air pollution. Re-organisation of the energy and industrial strategy together with trans-regional joint-control for a full long-term air pollution plan need to be further taken into account.

Matched MeSH terms: Human Activities
Microplastics in the freshwater and terrestrial environments: Prevalence, fates, impacts and sustainable solutions

Wong JKH, Lee KK, Tang KHD, Yap PS

Sci Total Environ, 2020 Jun 01;719:137512.
PMID: 32229011 DOI: 10.1016/j.scitotenv.2020.137512

The ubiquitous occurrences of microplastics in the environment have raised much concern and resulted in voluminous studies related to microplastics. Studies on microplastics pollution of the marine environment have received significantly higher attention compared to those of the freshwater and terrestrial environments. With the impetus to better understand microplastics in the freshwater and terrestrial environments, this review elucidates the findings of >100 articles related to the prevalence, fates and impacts of microplastics therein and the sustainable solutions, mostly in the past 10 years. This review shows the interconnection between terrestrial and freshwater microplastics with wastewater and sewage treatment plants as the most significant contributors of environmental microplastics via sludge and effluent discharges. Microplastics in both ecosystems comprise the primary and secondary forms with the latter resulted from weathering of the former. Besides retaining in soil and infiltrating with rainwater underground, terrestrial microplastics also enter the freshwater environment. The environmental microplastics interact with the biotic and abiotic components resulting in entrainment, settlement, biofouling, degradation, fragmentation and entry into the food chain, with subsequent transfer across the food chain. The abundance of environmental microplastics is attributed to population density and urbanization though tidal cycle, storms, floods and human activities can affect their distribution. The leaching of additives from microplastics poses major health concern and sustainable solutions target at reduction of plastics use and disposal, substitution with bioplastics and wastewater treatment innovations. Further studies on classification, detection, characterization and toxicity of microplastics are necessary to permit more effective formulation of solutions.

Matched MeSH terms: Human Activities
Fulltext Factors influencing the prevalence and distribution of ticks and tick- borne pathogens among domestic animals in Malaysia

Mohammed, Konto, Tukur, Salamatu M., Watanabe, Mahira, Abd-rani, Puteri A.m., Lau, Seng F., Shettima, Yasheruram M., et al.

The Pertanika Journal of Scholarly Research Reviews, 2016;2(2):12-22.
MyJurnal

Changes in tick-vector densities and a resultant incidence of tick-borne diseases are
caused mainly by human activities affecting the environmental ecosystem, especially in tropical
countries. As one of the most important invertebrate arthropod vectors of disease transmission, ticks
are susceptible to changes in their environment due to their sole dependence of all their life stages on
prevailing environment. Upon completion of their lifecycle, ticks depend on the availability of hosts
and other several factors related to their surroundings to survive. This review discusses the major
factors that influence the prevalence and distribution of tick-borne diseases among domestic animals
in Malaysia. It is highly imperative to understand the factors that lead to increase in tick-vector
populations, infection intensity and hence the spatial distribution of ticks and tick-borne diseases in
order to prevent their emergence and resurgence as well as to serve as a basis for effectivecontrol.

Matched MeSH terms: Human Activities
Fulltext Hydrological responses to climate and land use change at watershed scale_ Malaysia

Mahboubeh Ebrahimian, Ahmad Ainuddin Nuruddin, Mohd Amin Mohd Soom, Alias Mohd Sood, Liew Juneng

The Pertanika Journal of Scholarly Research Reviews, 2017;3(2):10-32.
MyJurnal

The hydrological effects of climate variation and land use conversion can occur at various spatial scales, but the most important sources of these changes are at the regional or watershed scale. In addition, the managerial and technical measures are primarily implemented at local and watershed scales in order to mitigate adverse impacts of human activities on the renewable resources of the watershed. Therefore, quantitative estimation of the possible hydrological consequences of potential land use and climate changes on hydrological regime at watershed scale is of tremendous importance. This paper focuses on the impacts of climate change as well as land use change on the hydrological processes of river basin based on pertinent published literature which were precisely scrutinized. The various causes, forms, and consequences of such impacts were discussed to synthesize the key findings of literature in reputable sources and to identify gaps in the knowledge where further research is required. Results indicate that the watershed-scale studies were found as a gap in tropical regions. Also, these studies are important to facilitate the application of results to real environment. Watershed scale studies are essential to measure the extent of influences made to the hydrological conditions and understanding of causes and effects of climate variation and land use conversion on hydrological cycle and water resources.

Matched MeSH terms: Human Activities
Fulltext Classifying Sources Influencing Indoor Air Quality (IAQ) Using Artificial Neural Network (ANN)

Saad SM, Andrew AM, Shakaff AY, Saad AR, Kamarudin AM, Zakaria A

Sensors (Basel), 2015;15(5):11665-84.
PMID: 26007724 DOI: 10.3390/s150511665

Monitoring indoor air quality (IAQ) is deemed important nowadays. A sophisticated IAQ monitoring system which could classify the source influencing the IAQ is definitely going to be very helpful to the users. Therefore, in this paper, an IAQ monitoring system has been proposed with a newly added feature which enables the system to identify the sources influencing the level of IAQ. In order to achieve this, the data collected has been trained with artificial neural network or ANN--a proven method for pattern recognition. Basically, the proposed system consists of sensor module cloud (SMC), base station and service-oriented client. The SMC contain collections of sensor modules that measure the air quality data and transmit the captured data to base station through wireless network. The IAQ monitoring system is also equipped with IAQ Index and thermal comfort index which could tell the users about the room's conditions. The results showed that the system is able to measure the level of air quality and successfully classify the sources influencing IAQ in various environments like ambient air, chemical presence, fragrance presence, foods and beverages and human activity.

Matched MeSH terms: Human Activities
Fulltext BeSafe B2.0 Smart Multisensory Platform for Safety in Workplaces

Márquez-Sánchez S, Campero-Jurado I, Robles-Camarillo D, Rodríguez S, Corchado-Rodríguez JM

Sensors (Basel), 2021 May 12;21(10).
PMID: 34066186 DOI: 10.3390/s21103372

Wearable technologies are becoming a profitable means of monitoring a person's health state, such as heart rate and physical activity. The use of the smartwatch is becoming consolidated, not only as a novelty but also as a very useful tool for daily use. In addition, other devices, such as helmets or belts, are beneficial for monitoring workers and the early detection of any anomaly. They can provide valuable information, especially in work environments, where they help reduce the rate of accidents and occupational diseases, which makes them powerful Personal Protective Equipment (PPE). The constant monitoring of the worker's health can be done in real-time, through temperature, falls, noise, impacts, or heart rate meters, activating an audible and vibrating alarm when an anomaly is detected. The gathered information is transmitted to a server in charge of collecting and processing it. In the first place, this paper provides an exhaustive review of the state of the art on works related to electronics for human activity behavior. After that, a smart multisensory bracelet, combined with other devices, developed a control platform that can improve operators' security in the working environment. Artificial Intelligence and the Internet of Things (AIoT) bring together the information to improve safety on construction sites, power stations, power lines, etc. Real-time and historic data is used to monitor operators' health and a hybrid system between Gaussian Mixture Model and Human Activity Classification. That is, our contribution is also founded on the use of two machine learning models, one based on unsupervised learning and the other one supervised. Where the GMM gave us a performance of 80%, 85%, 70%, and 80% for the 4 classes classified in real time, the LSTM obtained a result under the confusion matrix of 0.769, 0.892, and 0.921 for the carrying-displacing, falls, and walking-standing activities, respectively. This information was sent in real time through the platform that has been used to analyze and process the data in an alarm system.

Matched MeSH terms: Human Activities
Fulltext Development of a User-Adaptable Human Fall Detection Based on Fall Risk Levels Using Depth Sensor

Nizam Y, Mohd MNH, Jamil MMA

Sensors (Basel), 2018 Jul 13;18(7).
PMID: 30011823 DOI: 10.3390/s18072260

Unintentional falls are a major public health concern for many communities, especially with aging populations. There are various approaches used to classify human activities for fall detection. Related studies have employed wearable, non-invasive sensors, video cameras and depth sensor-based approaches to develop such monitoring systems. The proposed approach in this study uses a depth sensor and employs a unique procedure which identifies the fall risk levels to adapt the algorithm for different people with their physical strength to withstand falls. The inclusion of the fall risk level identification, further enhanced and improved the accuracy of the fall detection. The experimental results showed promising performance in adapting the algorithm for people with different fall risk levels for fall detection.

Matched MeSH terms: Human Activities
Fulltext Classification of Human Daily Activities Using Ensemble Methods Based on Smartphone Inertial Sensors

Ku Abd Rahim KN, Elamvazuthi I, Izhar LI, Capi G

Sensors (Basel), 2018 Nov 26;18(12).
PMID: 30486242 DOI: 10.3390/s18124132

Increasing interest in analyzing human gait using various wearable sensors, which is known as Human Activity Recognition (HAR), can be found in recent research. Sensors such as accelerometers and gyroscopes are widely used in HAR. Recently, high interest has been shown in the use of wearable sensors in numerous applications such as rehabilitation, computer games, animation, filmmaking, and biomechanics. In this paper, classification of human daily activities using Ensemble Methods based on data acquired from smartphone inertial sensors involving about 30 subjects with six different activities is discussed. The six daily activities are walking, walking upstairs, walking downstairs, sitting, standing and lying. It involved three stages of activity recognition; namely, data signal processing (filtering and segmentation), feature extraction and classification. Five types of ensemble classifiers utilized are Bagging, Adaboost, Rotation forest, Ensembles of nested dichotomies (END) and Random subspace. These ensemble classifiers employed Support vector machine (SVM) and Random forest (RF) as the base learners of the ensemble classifiers. The data classification is evaluated with the holdout and 10-fold cross-validation evaluation methods. The performance of each human daily activity was measured in terms of precision, recall, F-measure, and receiver operating characteristic (ROC) curve. In addition, the performance is also measured based on the comparison of overall accuracy rate of classification between different ensemble classifiers and base learners. It was observed that overall, SVM produced better accuracy rate with 99.22% compared to RF with 97.91% based on a random subspace ensemble classifier.

Matched MeSH terms: Human Activities
Fulltext Incremental Learning of Human Activities in Smart Homes

Chua SL, Foo LK, Guesgen HW, Marsland S

Sensors (Basel), 2022 Nov 03;22(21).
PMID: 36366154 DOI: 10.3390/s22218458

Sensor-based human activity recognition has been extensively studied. Systems learn from a set of training samples to classify actions into a pre-defined set of ground truth activities. However, human behaviours vary over time, and so a recognition system should ideally be able to continuously learn and adapt, while retaining the knowledge of previously learned activities, and without failing to highlight novel, and therefore potentially risky, behaviours. In this paper, we propose a method based on compression that can incrementally learn new behaviours, while retaining prior knowledge. Evaluation was conducted on three publicly available smart home datasets.

Matched MeSH terms: Human Activities*
Fulltext Multi-View Human Action Recognition Using Skeleton Based-FineKNN with Extraneous Frame Scrapping Technique

Malik NUR, Sheikh UU, Abu-Bakar SAR, Channa A

Sensors (Basel), 2023 Mar 02;23(5).
PMID: 36904953 DOI: 10.3390/s23052745

Human action recognition (HAR) is one of the most active research topics in the field of computer vision. Even though this area is well-researched, HAR algorithms such as 3D Convolution Neural Networks (CNN), Two-stream Networks, and CNN-LSTM (Long Short-Term Memory) suffer from highly complex models. These algorithms involve a huge number of weights adjustments during the training phase, and as a consequence, require high-end configuration machines for real-time HAR applications. Therefore, this paper presents an extraneous frame scrapping technique that employs 2D skeleton features with a Fine-KNN classifier-based HAR system to overcome the dimensionality problems.To illustrate the efficacy of our proposed method, two contemporary datasets i.e., Multi-Camera Action Dataset (MCAD) and INRIA Xmas Motion Acquisition Sequences (IXMAS) dataset was used in experiment. We used the OpenPose technique to extract the 2D information, The proposed method was compared with CNN-LSTM, and other State of the art methods. Results obtained confirm the potential of our technique. The proposed OpenPose-FineKNN with Extraneous Frame Scrapping Technique achieved an accuracy of 89.75% on MCAD dataset and 90.97% on IXMAS dataset better than existing technique.

Matched MeSH terms: Human Activities
Fulltext Robust video content analysis schemes for human action recognition

Aly CA, Abas FS, Ann GH

Sci Prog, 2021;104(2):368504211005480.
PMID: 33913378 DOI: 10.1177/00368504211005480

INTRODUCTION: Action recognition is a challenging time series classification task that has received much attention in the recent past due to its importance in critical applications, such as surveillance, visual behavior study, topic discovery, security, and content retrieval.
OBJECTIVES: The main objective of the research is to develop a robust and high-performance human action recognition techniques. A combination of local and holistic feature extraction methods used through analyzing the most effective features to extract to reach the objective, followed by using simple and high-performance machine learning algorithms.
METHODS: This paper presents three robust action recognition techniques based on a series of image analysis methods to detect activities in different scenes. The general scheme architecture consists of shot boundary detection, shot frame rate re-sampling, and compact feature vector extraction. This process is achieved by emphasizing variations and extracting strong patterns in feature vectors before classification.
RESULTS: The proposed schemes are tested on datasets with cluttered backgrounds, low- or high-resolution videos, different viewpoints, and different camera motion conditions, namely, the Hollywood-2, KTH, UCF11 (YouTube actions), and Weizmann datasets. The proposed schemes resulted in highly accurate video analysis results compared to those of other works based on four widely used datasets. The First, Second, and Third Schemes provides recognition accuracies of 57.8%, 73.6%, and 52.0% on Hollywood2, 94.5%, 97.0%, and 59.3% on KTH, 94.5%, 95.6%, and 94.2% on UCF11, and 98.9%, 97.8% and 100% on Weizmann.
CONCLUSION: Each of the proposed schemes provides high recognition accuracy compared to other state-of-art methods. Especially, the Second Scheme as it gives excellent comparable results to other benchmarked approaches.

Matched MeSH terms: Human Activities*
Fulltext Orangutans venture out of the rainforest and into the Anthropocene

Spehar SN, Sheil D, Harrison T, Louys J, Ancrenaz M, Marshall AJ, et al.

Sci Adv, 2018 06;4(6):e1701422.
PMID: 29963619 DOI: 10.1126/sciadv.1701422

Conservation benefits from understanding how adaptability and threat interact to determine a taxon's vulnerability. Recognizing how interactions with humans have shaped taxa such as the critically endangered orangutan (Pongo spp.) offers insights into this relationship. Orangutans are viewed as icons of wild nature, and most efforts to prevent their extinction have focused on protecting minimally disturbed habitat, with limited success. We synthesize fossil, archeological, genetic, and behavioral evidence to demonstrate that at least 70,000 years of human influence have shaped orangutan distribution, abundance, and ecology and will likely continue to do so in the future. Our findings indicate that orangutans are vulnerable to hunting but appear flexible in response to some other human activities. This highlights the need for a multifaceted, landscape-level approach to orangutan conservation that leverages sound policy and cooperation among government, private sector, and community stakeholders to prevent hunting, mitigate human-orangutan conflict, and preserve and reconnect remaining natural forests. Broad cooperation can be encouraged through incentives and strategies that focus on the common interests and concerns of different stakeholders. Orangutans provide an illustrative example of how acknowledging the long and pervasive influence of humans can improve strategies to preserve biodiversity in the Anthropocene.

Matched MeSH terms: Human Activities

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