Weapons Detection for Security and Video Surveillance Using CNN and YOLO-V5s

In recent years, the number of Gun-related incidents has crossed over 250,000 per year and over 85% of the existing 1 billion firearms are in civilian hands, manual monitoring has not proven effective in detecting firearms. which is why an automated weapon detection system is needed. Various automated convolutional neural networks (CNN) weapon detection systems have been proposed in the past to generate good results. However, These techniques have high computation overhead and are slow to provide real-time detection which is essential for the weapon detection system. These models have a high rate of false negatives because they often fail to detect the guns due to the low quality and visibility issues of surveillance videos. This research work aims to minimize the rate of false negatives and false positives in weapon detection while keeping the speed of detection as a key parameter. The proposed framework is based on You Only Look Once (YOLO) and Area of Interest (AOI). Initially, the models take pre-processed frames where the background is removed by the use of the Gaussian blur algorithm. The proposed architecture will be assessed through various performance parameters such as False Negative, False Positive, precision, recall rate, and F1 score. The results of this research work make it clear that due to YOLO-v5s high recall rate and speed of detection are achieved. Speed reached 0.010 s per frame compared to the 0.17 s of the Faster R-CNN. It is promising to be used in the field of security and weapon detection.     

Gaussian Support Vector Machine Algorithm Based Air Pollution Prediction

Air pollution is one of the major concerns considering detriments to human health. This type of pollution leads to several health problems for humans, such as asthma, heart issues, skin diseases, bronchitis, lung cancer, and throat and eye infections. Air pollution also poses serious issues to the planet. Pollution from the vehicle industry is the cause of greenhouse effect and CO2 emissions. Thus, real-time monitoring of air pollution in these areas will help local authorities to analyze the current situation of the city and take necessary actions. The monitoring process has become efficient and dynamic with the advancement of the Internet of things and wireless sensor networks. Localization is the main issue in WSNs; if the sensor node location is unknown, then coverage and power and routing are not optimal. This study concentrates on localization-based air pollution prediction systems for real-time monitoring of smart cities. These systems comprise two phases considering the prediction as heavy or light traffic area using the Gaussian support vector machine algorithm based on the air pollutants, such as PM2.5 particulate matter, PM10, nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3), and sulfur dioxide (SO2). The sensor nodes are localized on the basis of the predicted area using the meta-heuristic algorithms called fast correlation-based elephant herding optimization. The dataset is divided into training and testing parts based on 10 cross-validations. The evaluation on predicting the air pollutant for localization is performed with the training dataset. Mean error prediction in localizing nodes is 9.83 which is lesser than existing solutions and accuracy is 95%.     

Type II Fuzzy Logic Based Cluster Head Selection for Wireless Sensor Network

Wireless Sensor Network (WSN) forms an essential part of IoT. It is embedded in the target environment to observe the physical parameters based on the type of application. Sensor nodes in WSN are constrained by different features such as memory, bandwidth, energy, and its processing capabilities. In WSN, data transmission process consumes the maximum amount of energy than sensing and processing of the sensors. So, diverse clustering and data aggregation techniques are designed to achieve excellent energy efficiency in WSN. In this view, the current research article presents a novel Type II Fuzzy Logic-based Cluster Head selection with Low Complexity Data Aggregation (T2FLCH-LCDA) technique for WSN. The presented model involves a two-stage process such as clustering and data aggregation. Initially, three input parameters such as residual energy, distance to Base Station (BS), and node centrality are used in T2FLCH technique for CH selection and cluster construction. Besides, the LCDA technique which follows Dictionary Based Encoding (DBE) process is used to perform the data aggregation at CHs. Finally, the aggregated data is transmitted to the BS where it achieves energy efficiency. The experimental validation of the T2FLCH-LCDA technique was executed under three different scenarios based on the position of BS. The experimental results revealed that the T2FLCH-LCDA technique achieved maximum energy efficiency, lifetime, Compression Ratio (CR), and power saving than the compared methods.     

Robust and High Accuracy Algorithm for Detection of Pupil Images

Recently, many researchers have tried to develop a robust, fast, and accurate algorithm. This algorithm is for eye-tracking and detecting pupil position in many applications such as head-mounted eye tracking, gaze-based human-computer interaction, medical applications (such as deaf and diabetes patients), and attention analysis. Many real-world conditions challenge the eye appearance, such as illumination, reflections, and occasions. On the other hand, individual differences in eye physiology and other sources of noise, such as contact lenses or make-up. The present work introduces a robust pupil detection algorithm with and higher accuracy than the previous attempts for real-time analytics applications. The proposed circular hough transform with morphing canny edge detection for Pupillometery (CHMCEP) algorithm can detect even the blurred or noisy images by using different filtering methods in the pre-processing or start phase to remove the blur and noise and finally the second filtering process before the circular Hough transform for the center fitting to make sure better accuracy. The performance of the proposed CHMCEP algorithm was tested against recent pupil detection methods. Simulations and results show that the proposed CHMCEP algorithm achieved detection rates of 87.11, 78.54, 58, and 78 according to Świrski, ExCuSe, Else, and labeled pupils in the wild (LPW) data sets, respectively. These results show that the proposed approach performs better than the other pupil detection methods by a large margin by providing exact and robust pupil positions on challenging ordinary eye pictures.     

Theoretical Investigation of Magnetoelectric Coupling in MFe2O4/PbZ0.5T0.5O3/MFe2O4 (M = Ni,Co) Heterostructure

Journal of Superconductivity and Novel Magnetism - Magnetoelectric materials that allow magnetic-field control of electric polarization are promising candidates for high-density data storage and...     

Hyperparameter Tuned Deep Learning Enabled Cyberbullying Classification in Social Media

Cyberbullying (CB) is a challenging issue in social media and it becomes important to effectively identify the occurrence of CB. The recently developed deep learning (DL) models pave the way to design CB classifier models with maximum performance. At the same time, optimal hyperparameter tuning process plays a vital role to enhance overall results. This study introduces a Teacher Learning Genetic Optimization with Deep Learning Enabled Cyberbullying Classification (TLGODL-CBC) model in Social Media. The proposed TLGODL-CBC model intends to identify the existence and non-existence of CB in social media context. Initially, the input data is cleaned and pre-processed to make it compatible for further processing. Followed by, independent recurrent autoencoder (IRAE) model is utilized for the recognition and classification of CBs. Finally, the TLGO algorithm is used to optimally adjust the parameters related to the IRAE model and shows the novelty of the work. To assuring the improved outcomes of the TLGODL-CBC approach, a wide range of simulations are executed and the outcomes are investigated under several aspects. The simulation outcomes make sure the improvements of the TLGODL-CBC model over recent approaches.     

Quality control for a detected an appropriate queue from K-independent M/M/C/N queueing models

In this paper, we consider a system of K-independent Markovian queues such that each one of them has a Poisson arrival process and exponential service time. We assume that every server has some characteristics such as the speed of the service performance or the service cost. To find an appropriate queue, which meets customer needs for the service performance, we present a new approach that gives a suitable decision to choose an appropriate queue from our system. This allows the customer to deal with minimum cost and faster server under steady state. We solve an interesting discrete stochastic optimization problem where the paid cost by the customer is bounded by a Gaussian distribution. Using these hypotheses, we perform a simulation study by generating the paid cost random values and choosing the minimum value between them. This minimum cost gives the highest service rate, which is used to obtain the optimum values of the system effectiveness measures.     

Systems pharmacology approach to investigate the molecular mechanisms of herb Rhodiola rosea L. radix

Rhodiola rosea L. radix (RRL) is one of the most popular medical herb which has been widely used for the treatment of different diseases effectively, including cardiovascular diseases and nerve system diseases. However, due to the multiple compounds in RRL, the underlying molecular mechanisms of RRL are remained unclear. To decipher the action mechanisms of RRL from a systematic perspective, a systems pharmacology approach integrated absorption, distribution, metabolism, and excretion (ADME) system, drug targeting, and network analysis was introduced. First, by the ADME screening system and the target fishing process, 56 potential active compounds and 62 targets were obtained, respectively. In addition, compound-target network demonstrated that most compounds interacted with multiple targets, indicating that RRL may enhance its therapeutic effects probably through hitting on multiple targets in a holistic level. Moreover, target-pathway network and gene ontology analysis showed that multiple targets of RRL were involved in several biological pathways, i.e. Neuroactive ligand-receptor interaction, calcium signaling pathway, adrenergic signaling in cardiomyocytes, and VEGF signaling pathway, which dissecting the therapeutic effects of RRL on various diseases, such as cardiovascular diseases, depression, adaptation diseases, etc. In summary, this work successfully explains the potential active compounds and the multi-scale curative action mechanisms of RRL for treating various diseases; meanwhile, it implies that RRL could be applied as a novel therapeutic agent in arthritic diseases. Most importantly, this work provides an in silico strategy to understand the action mechanisms of herbal medicines from molecular/system levels, which will promote the new drug development of traditional Chinese medicine.     

A Laser-Induced Mo2CTx MXene Hybrid Anode for High-Performance Li-Ion Batteries

MXenes, a fast-growing family of two-dimensional (2D) transition metal carbides/nitrides, are promising for electronics and energy storage applications. Mo₂CT_x MXene, in particular, has demonstrated a higher capacity than other MXenes as an anode for Li-ion batteries. Yet, such enhanced capacity is accompanied by slow kinetics and poor cycling stability. Herein, it is revealed that the unstable cycling performance of Mo₂CT_x is attributed to the partial oxidation into MoO_x with structural degradation. A laser-induced Mo₂CT_x/Mo₂C (LS-Mo₂CT_x) hybrid anode has been developed, of which the Mo₂C nanodots boost redox kinetics, and the laser-reduced oxygen content prevents the structural degradation caused by oxidation. Meanwhile, the strong connections between the laser-induced Mo₂C nanodots and Mo₂CT_x nanosheets enhance conductivity and stabilize the structure during charge–discharge cycling. The as-prepared LS-Mo₂CT_x anode exhibits an enhanced capacity of 340 mAh g⁻¹ vs 83 mAh g⁻¹ (for pristine) and an improved cycling stability (capacity retention of 106.2% vs 80.6% for pristine) over 1000 cycles. The laser-induced synthesis approach underlines the potential of MXene-based hybrid materials for high-performance energy storage applications.     

Fundamentals,antibacterial, and photocatalysis properties of Ag@Se@PVDF nanocomposite membrane synthesized via laser ablation technique

Journal of Materials Science: Materials in Electronics - Several ratios of silver/selenium (Ag/Se) nanoparticles generated via laser ablation were used to create nanocomposite scaffolds based on...