A Committee Machine Gas Identification System Based on Dynamically Reconfigurable FPGA

This paper proposes a gas identification system based on the committee machine (CM) classifier, which combines various gas identification algorithms, to obtain a unified decision with improved accuracy. The CM combines five different classifiers: K nearest neighbors (KNNs), multilayer perceptron (MLP), radial basis function (RBF), Gaussian mixture model (GMM), and probabilistic principal component analysis (PPCA). Experiments on real sensors' data proved the effectiveness of our system with an improved accuracy over individual classifiers. Due to the computationally intensive nature of CM, its implementation requires significant hardware resources. In order to overcome this problem, we propose a novel time multiplexing hardware implementation using a dynamically reconfigurable field programmable gate array (FPGA) platform. The processing is divided into three stages: sampling and preprocessing, pattern recognition, and decision stage. Dynamically reconfigurable FPGA technique is used to implement the system in a sequential manner, thus using limited hardware resources of the FPGA chip. The system is successfully tested for combustible gas identification application using our in-house tin-oxide gas sensors.     

Deep Transfer Learning-Enabled Activity Identification and Fall Detection for Disabled People

The human motion data collected using wearables like smartwatches can be used for activity recognition and emergency event detection. This is especially applicable in the case of elderly or disabled people who live self-reliantly in their homes. These sensors produce a huge volume of physical activity data that necessitates real-time recognition, especially during emergencies. Falling is one of the most important problems confronted by older people and people with movement disabilities. Numerous previous techniques were introduced and a few used webcam to monitor the activity of elderly or disabled people. But, the costs incurred upon installation and operation are high, whereas the technology is relevant only for indoor environments. Currently, commercial wearables use a wireless emergency transmitter that produces a number of false alarms and restricts a user’s movements. Against this background, the current study develops an Improved Whale Optimization with Deep Learning-Enabled Fall Detection for Disabled People (IWODL-FDDP) model. The presented IWODL-FDDP model aims to identify the fall events to assist disabled people. The presented IWODL-FDDP model applies an image filtering approach to pre-process the image. Besides, the EfficientNet-B0 model is utilized to generate valuable feature vector sets. Next, the Bidirectional Long Short Term Memory (BiLSTM) model is used for the recognition and classification of fall events. Finally, the IWO method is leveraged to fine-tune the hyperparameters related to the BiLSTM method, which shows the novelty of the work. The experimental analysis outcomes established the superior performance of the proposed IWODL-FDDP method with a maximum accuracy of 97.02%.     

A review of the desert gold jojoba (Simmondsia chinensis) whole plant,oil, and meal: Phytochemical composition,medicinal uses,and detoxification

Simmondsia chinensis (Link) C.K. Schneider (Jojoba) is a valuable shrub that can bear harsh conditions and is cultivated in many countries globally. Its prominence originates from the unique oil that constitutes more than 50% of the seeds. The great economic value of jojoba oil is highlighted in many fields, especially the cosmetic industry. The remaining meal, which is rich in proteins, constitutes a good source for cattle feeding. However, the presence of antinutritional principles in the meal limited its use and encouraged the researchers to find different ways for its detoxification. The detoxification ways of jojoba meal included physical, biological, and chemical treatments. The phytochemical composition of the oil was deeply studied, but for the remaining plant, only few studies have reported its chemical composition. Jojoba oil composed of wax esters (97%), fatty acids, fatty alcohols, sterols, and small percentage of vitamin E. Jojoba possesses a long traditional history. It has been used in folklore for treatment of cold, dysuria, and obesity. Many recent studies reported its medicinal and pharmacological properties like antioxidant, anti-inflammatory, antimicrobial, anticancer, anti-acne, anti-psoriasis, wound healing, and hepatoprotective activities. Many of these biological activities have been attributed to the presence of several phytochemicals such as simmondsin and phenolic compounds. In this review, the authors will highlight the previous phytochemical studies, medicinal applications of jojoba oil and different plant parts, and the various ways of meal detoxification.     

Toward a web-based multi-criteria decision support system for the layered evaluation of interactive adaptive systems

Universal Access in the Information Society - The choice of suitable evaluation methods for the layered evaluation of Interactive Adaptive Systems (IAS) needs the consideration of different...     

Erucic acid ameliorates the lipopolysaccharide-induced memory deficit in rats through inhibited inflammation cytokines expression/caspase 3/NF-κB pathways

Erucic acid is a single unsaturated fatty acid that falls under the omega-9 fatty acid family. It was suggested to treat Wistar rats with lipopolysaccharide (LPS)-induced memory impairment and minimize cognitive impairment. A total of 30 animals were randomized: group I was normally treated group, group II was administered with LPS, group III was treated with LPS along with erucic acid at the dose of 10 mg kg^–1 p.o.^–1, group IV was treated with LPS along with erucic acid at 20 mg kg^–1 p.o.^–1 and group V was the erucic acid per se group provided at the dose of 20 mg kg^–1 p.o.^–1 per se. Behavioral tests were evaluated by using the Morris water maze and Y-maze. Biochemical analysis including acetylcholine esterase (AChE), choline acetyltransferase (ChAT), glutathione (GSH), catalase activity (CAT), superoxide dismutase (SOD), and nitric oxide (NO) along with proinflammatory mediators tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), caspase 3, and neuroinflammatory biomarker (nuclear factor kappa B-NF-κB) were measured. Erucic acid produced substantial behavioral improvement in the Y-maze test, including spontaneous alterations and reduced latency time during acquisition, and a longer duration of time in the consolidation phase undergoing the MWM test. Furthermore, erucic acid improved the AChE, proinflammatory markers, and oxidative stress as well as restoring endogenous antioxidant levels, ChAT, caspase 3, and NF-κB levels. Erucic acid may be a therapeutic component for conditions related to memory disorders such as memory impairment, enhances memory functioning, and protects against neuronal damage.     

Intelligent Aquila Optimization Algorithm-Based Node Localization Scheme for Wireless Sensor Networks

In recent times, wireless sensor network (WSN) finds their suitability in several application areas, ranging from military to commercial ones. Since nodes in WSN are placed arbitrarily in the target field, node localization (NL) becomes essential where the positioning of the nodes can be determined by the aid of anchor nodes. The goal of any NL scheme is to improve the localization accuracy and reduce the localization error rate. With this motivation, this study focuses on the design of Intelligent Aquila Optimization Algorithm Based Node Localization Scheme (IAOAB-NLS) for WSN. The presented IAOAB-NLS model makes use of anchor nodes to determine proper positioning of the nodes. In addition, the IAOAB-NLS model is stimulated by the behaviour of Aquila. The IAOAB-NLS model has the ability to accomplish proper coordinate points of the nodes in the network. For guaranteeing the proficient NL process of the IAOAB-NLS model, widespread experimentation takes place to assure the betterment of the IAOAB-NLS model. The resultant values reported the effectual outcome of the IAOAB-NLS model irrespective of changing parameters in the network.