Halalnet: A Deep Neural Network That Classifies the Halalness of Slaughtered Chicken from Their Images

Halal requirement in food is important for millions of Muslims worldwide especially for meat and chicken products, insuring that slaughter houses adhere to this requirement is a challenging task to do manually. In this paper a method is proposed that uses a camera that takes images of slaughtered chicken on the conveyor in a slaughter house, the images are then analyzed by a deep neural network to classify if the image is of a halal slaughtered chicken or not. However, traditional deep learning models require large amounts of data to train on, which in this case these amounts of data were challenging to collect especially the images of non-halal slaughtered chicken, hence this paper shows how the use of one shot learning (Lake, Brenden, Salakhutdinov, Ruslan, Gross & Jas, 2011) and transfer learning (Yosinski, Clune, Bengio & Lipson, 2014) can reach high accuracy on the few amounts of data that were available. The architecture used is based on the Siamese neural networks architecture which ranks the similarity between two inputs (Koch, Zemel & Salakhutdinov, 2015) while using the Xception network (Chollet, 2017) as the twin networks. We call it HalalNet. This work was done as part of SYCUT (syriah compliant slaughtering system) which is a monitoring system that monitors the halalness of the slaughtered chicken in a slaughter house. The data used to train and validate HalalNet was collected from the Azain slaughtering site (Semenyih, Selangor, Malaysia) containing images of both halal and non-halal slaughtered chicken

Task allocation based multi-agent reinforcement learning for LoRa nodes in gas wellhead monitoring service

897-903This paper investigates a new alternative approach to handle the tasks allocation problem that associate with numerous Long Range (LoRa) nodes in the High-Pressure High-Temperature (HPHT) gas wellhead monitoring service. A Multi- Agent Reinforcement Learning approach is proposed in this paper to overcome this problem with the Proximal Policy Optimization (PPO) is chosen as the policy gradient method. An action space is the spreading factor and other parameters such as frequency and transmission power has been kept constant. The reward function for the training process will be determined by two parameters which are the acknowledge flag (ACK) and collision between packets. Each node will be distributed across a defined disc radius. Each node will be represented as an agent. Each agent will undergo packet transmission and the packet will be evaluated according to the reward function. The results show that PPO with Multi Agent Reinforcement Learning was able to determine the optimal configuration for each LoRa node. The total reward value corresponds to the total number of nodes. Furthermore, since this study also implements the use of CUDA, the training was able to done in 200 steps and 45 minutes

Possible protective and curative effects of selenium nanoparticles on testosterone-induced benign prostatic hyperplasia rat model

Background: Men over the age of 40 are more likely to develop benign prostatic hyperplasia (BPH). BPH is characterized by proliferation of the prostatic epithelium and stroma. Selenium nanoparticles (SeNPs), is an essential metalloid mineral and antioxidant. In this study, SeNPs were tested for their potential protective and curative impacts on BPH in rats. Materials and methods: 50 male Sprague-Dawley rats were randomly divided into five groups: Group I (Control group); Group II (Orchiectomized group): bilateral orchiectomy was conducted on rats; Group III (BPH group): testosterone (TE) enanthate injection was used to induce BPH; Group IV (Protective group): rats were given SeNP before subjecting rats to BPH; Group V (Curative group): rats were succumbed to BPH, followed by administration of SeNP. Measurement of prostate specific antigen (PSA) and TE in serum was performed and prostates were weighed and prepared for histological, immunohistochemical and ultrastructural examination. Results: In the BPH group, serum TE- and PSA-levels, as well as prostate weight, increased significantly and significant decreases in the protective and curative groups. Reduced acinar lumen, expansion of stroma and epithelial hyperplasia were noticed in the BPH group, which were ameliorated significantly both in protective and curative groups. There was an increase in PCNA immunoreaction in the BPH group and a decrease in both the protective and curative groups. On TEM of BPH group, the nuclei appeared irregular with dilated endoplasmic reticulum, loss of cell boundaries and apical microvilli. The protective group showed more improvement than the curative group. Conclusions: The effects of SeNPs on BPH induced by TE in rats, were both protective and curative, although the protective effects were more pronounced

Task allocation based multi-agent reinforcement learning for LoRa nodes in gas wellhead monitoring service

This paper investigates a new alternative approach to handle the tasks allocation problem that associate with numerous Long Range (LoRa) nodes in the High-Pressure High-Temperature (HPHT) gas wellhead monitoring service. A Multi-Agent Reinforcement Learning approach is proposed in this paper to overcome this problem with the Proximal Policy Optimization (PPO) is chosen as the policy gradient method. An action space is the spreading factor and other parameters such as frequency and transmission power has been kept constant. The reward function for the training process will be determined by two parameters which are the acknowledge flag (ACK) and collision between packets. Each node will be distributed across a defined disc radius. Each node will be represented as an agent. Each agent will undergo packet transmission and the packet will be evaluated according to the reward function. The results show that PPO with Multi Agent Reinforcement Learning was able to determine the optimal configuration for each LoRa node. The total reward value corresponds to the total number of nodes. Furthermore, since this study also implements the use of CUDA, the training was able to done in 200 steps and 45 minutes

Towards multi robot task allocation and navigation using deep reinforcement learning

Developing algorithms for multi robot systems to reach target positions and navigate safely in the environment is an open field of research. Most systems treat Multi Robot Task Allocation (MRTA) and Multi Robot Path Planning (MRPP) as two separate steps each with its own set of algorithms in which the MRTA algorithm assigns each robot to a task and the MRPP algorithm guides each robot through the environment towards the assigned goal position while avoiding both static and dynamic obstacles. In this paper, we present a method that combines both steps by using a deep reinforcement learning model. The model consists of a decentralized sensor level policy which outputs the robot's velocity to guide it through the environment towards the selected goal position and avoiding collisions. The model was trained in a simulation environment and all the robots are homogenous differential drive robots. The objective is to ensure that each robot reaches a unique goal position with the number of goal positions is equal to the number of robots. The results of training the policy in an environment is presented with both static and dynamic obstacles with four robots and four goal positions

A systematic review of deep learning for silicon wafer defect recognition

Advancements in technology have made deep learning a hot research area, and we see its applications in various fields. Its widespread use in silicon wafer defect recognition is replacing traditional machine learning and image processing methods of defect monitoring. This article presents a review of the deep learning methods employed for wafer map defect recognition. A systematic literature review (SLR)has been conducted to determine how the semiconductor industry is being leveraged by advancements in deep learning research for wafer defects recognition and analysis. Forty-four articles from the well-known databases have been selected for this review. The detailed study of the selected articles identified the prominent deep learning algorithms and network architectures for wafer map defect classification, clustering, feature extraction, and data synthesis. The learning algorithms are grouped as supervised learning, unsupervised learning, and hybrid learning. The network architectures include different forms of Convolutional Neural Network (CNN), Generative Adversarial Network (GAN), and (Auto-encoder (AE). Issues of multi-class and multi-label defects have been addressed, solving data unavailability, class imbalance, instance labeling, and unknown defects. As future directions, it is recommended to invest more efforts in the accuracy of the data generation procedures and the defect pattern recognition frameworks for defect monitoring in real industrial environments

Effects of Plyometric-Based Hydro-Kinesiotherapy on Pain, Muscle Strength, Postural Stability, and Functional Performance in Children with Hemophilic Knee Arthropathy: A Randomized Trial

To explore how plyometric-based hydro-kinesiotherapy (Plyo-HKT) would affect pain, muscle strength, postural stability, and functional performance in a convenience sample of children with hemophilic knee arthropathy (HKA). Forty-eight children with HKA (age: 8–16 years) were randomly allocated to the Plyo-HKT group (n = 24; underwent the Plyo-HKT for 45 min, twice/week over 12 wk in succession) or the comparison group (n = 24; performed the standard exercise rehabilitation at an equivalent frequency and duration). Pain, peak concentric torque of quadriceps and hamstring (produced at two angular velocities: 120 and 180 o/sec), dynamic limits of postural stability (DLPS), and functional performance [Functional Independence Score in Hemophilia (FISH) and 6-Minute Walk Test (6-MWT)] were assessed pre- and post-intervention. In contrast with the comparison group, the Plyo-HKT group achieved more favorable pre-to-post changes in pain (p = .028, η2p = 0.10), peak torque of quadriceps [120°/sec (p = .007, η2P = 0.15); 180°/sec (p = .011, η2P = 0.13)] and hamstring [120°/sec (p = .024, η2P = 0.11); 180°/sec (p = .036, η2P = 0.09)], DLPSdirectional [forward (p = .007, η2P = 0.15); backward (p = .013, η2P = 0.12); affected side (p = .008, η2P = 0.14); non-affected side (p = .002, η2P = 0.20)], DLPSoverall (p η2P = 0.32), and functional performance [FISH (p η2p = 0.26); 6-MWT (p = .002, η2p = 0.19)]. Plyo-HKT is likely helpful for reducing pain, improving strength, enhancing postural stability, and boosting functional capabilities in children with HKA. Physical rehabilitation practitioners should, therefore, consider this intervention strategy.</p