IoT-Based Water Quality Assessment System for Industrial Waste WaterHealthcare Perspective

The environment, especially water, gets polluted due to industrialization and urbanization. Pollution due to industrialization and urbanization has harmful effects on both the environment and the lives on Earth. This polluted water can cause food poisoning, diarrhea, short-term gastrointestinal problems, respiratory diseases, skin problems, and other serious health complications. In a developing country like Bangladesh, where ready-made garments sector is one of the major sources of the total Gross Domestic Product (GDP), most of the wastes released from the garment factories are dumped into the nearest rivers or canals. Hence, the quality of the water of these bodies become very incompatible for the living beings, and so, it has become one of the major threats to the environment and human health. In addition, the amount of fish in the rivers and canals in Bangladesh is decreasing day by day as a result of water pollution. Therefore, to save fish and other water animals and the environment, we need to monitor the quality of the water and find out the reasons for the pollution. Real-time monitoring of the quality of water is vital for controlling water pollution. Most of the approaches for controlling water pollution are mainly biological and lab-based, which takes a lot of time and resources. To address this issue, we developed an Internet of Things (IoT)-based real-time water quality monitoring system, integrated with a mobile application. The proposed system in this research measures some of the most important indexes of water, including the potential of hydrogen (pH), total dissolved solids (TDS), and turbidity, and temperature of water. The proposed system results will be very helpful in saving the environment, and thus, improving the health of living creatures on Earth

Hybrid detection techniques for 5G and B5G M-MIMO system

Massive-Multiple Inputs and Multiple Outputs (M−MIMO) will have a greater impact in the advanced radio framework. It efficiently increases the capacity, spectral access, and data speed of the framework. However, the detection of the signal becomes complicated due to the use of several antennas at the microcell. Separating such a vast range of connected devices is necessary to enable the detection of transmit antennas in response to various available data sources. In the presented work, novel hybrid algorithms such as QR-maximum likelihood detection (QR-MLD), QR-minimum means square error (MMSE), QR-zero forcing equaliser (ZFE), and QR-beam forming (QR-BF) are implemented for 16x16, 64x64, and 256x256 MIMO structures. The hybrid algorithms obtained an efficient bit error rate (BER) of 10-3 at the SNR of 2.9 dB with trivial complexity. Further, the proposed algorithms are compared with conventional methods. It is be noted that the QR-MLD achieves a gain of 3 dB when compared to the MMSE. It is concluded that the QR-MLD provided optimal performance and significantly enhanced the throughput gain of the framework

Artificial Neural Network-Based Deep Learning Model for COVID-19 Patient Detection Using X-Ray Chest Images

The world is experiencing an unprecedented crisis due to the coronavirus disease (COVID-19) outbreak that has affected nearly 216 countries and territories across the globe. Since the pandemic outbreak, there is a growing interest in computational model-based diagnostic technologies to support the screening and diagnosis of COVID-19 cases using medical imaging such as chest X-ray (CXR) scans. It is discovered in initial studies that patients infected with COVID-19 show abnormalities in their CXR images that represent specific radiological patterns. Still, detection of these patterns is challenging and time-consuming even for skilled radiologists. In this study, we propose a novel convolutional neural network- (CNN-) based deep learning fusion framework using the transfer learning concept where parameters (weights) from different models are combined into a single model to extract features from images which are then fed to a custom classifier for prediction. We use gradient-weighted class activation mapping to visualize the infected areas of CXR images. Furthermore, we provide feature representation through visualization to gain a deeper understanding of the class separability of the studied models with respect to COVID-19 detection. Cross-validation studies are used to assess the performance of the proposed models using open-access datasets containing healthy and both COVID-19 and other pneumonia infected CXR images. Evaluation results show that the best performing fusion model can attain a classification accuracy of 95.49% with a high level of sensitivity and specificity

Towards Secure Data Exchange in Peer-to-Peer Data Management Systems

In a peer-to-peer data management system (P2PDMS) peers exchange data in a pair-wise fashion on-the-fly in response to user queries without any centralized control. When peers exchange highly confidential data over an insecure communication channel, the data might be intercepted and read by intruders. As there is no centralized control for data exchange among peers in a P2PDMS, we cannot assume any central third party security infrastructure (e.g. PKI) to protect confidential data. This paper proposes a security protocol for data exchange in P2PDMSs based on pairing-based cryptography and data exchange policy. The protocol allows the peers to compute their secret session keys dynamically during data exchange session by computing a pairing on an elliptic curve, that is based on the policies between them.We show using a formal verification tool that the proposed protocol is safe, and is robust against different attacks including man-in-the middle, the masquerade, and the reply. Furthermore, the computational and communication overhead of the protocol are analyzed

Modeling the intention and usage of organic pesticide control using value-belief-norm model

The degradation of farming lands caused by excessive pesticide usage is a growing concern. One of the most effective strategies for preventing this disaster from worsening is to commence organic pesticide management at a mass level. Although farmers depend highly on synthetic pesticides and fertilizers to obtain higher yields and profitable returns, the adoption of these synthetic inputs have remained surprisingly low in many agricultural contexts, spanning both developing and developed countries. The goal of this study is to utilize the Value-Belief-Norm (VBN) theory as a framework for understanding the critical sociopsychological factors influencing farmers’ decision to use organic pesticides. Specifically, this study aims to introduce and assess the impact of a new construct, i.e., the social norm withing the VBN framework. Additionally, this study empirically evaluates the core components of VBN theory and their causal relationship. The data was collected from 322 farmers from Zhoukou, Henan province, China using a survey questionnaire. The findings show that farmers’ egoistic values significantly impact the ecological worldview, despite the fact that biospheric values had no discernible effect. The ecological worldview also profoundly influences the farmers’ awareness of consequences and their personal norms. Although the study finds awareness of consequences to have no significant effect on personal norms, it has a substantial positive impact on ascription of responsibility. In addition, ascription of responsibility significantly influences farmers’ personal norms, which substantially impacts the intention to use organic pesticides. The results also reveal that farmers’ intentions significantly impact the usage of organic pesticides. The study’s findings can help strengthen essential factors among farmers that can improve their perception of organic agricultural methods, create strategies for managing controlled agrochemicals, and successfully stop environmental degradation by toxic inputs

Evolving CNN with Paddy Field Algorithm for Geographical Landmark Recognition

Convolutional Neural Networks (CNNs) operate within a wide variety of hyperparameters, the optimization of which can greatly improve the performance of CNNs when performing the task at hand. However, these hyperparameters can be very difficult to optimize, either manually or by brute force. Neural architecture search or NAS methods have been developed to address this problem and are used to find the best architectures for the deep learning paradigm. In this article, a CNN has been evolved with a well-known nature-inspired metaheuristic paddy field algorithm (PFA). It can be seen that PFA can evolve the neural architecture using the Google Landmarks Dataset V2, which is one of the toughest datasets available in the literature. The CNN’s performance, when evaluated based on the accuracy benchmark, increases from an accuracy of 0.53 to 0.76, which is an improvement of more than 40%. The evolved architecture also shows some major improvements in hyperparameters that are normally considered to be the best suited for the task

Image Watermarking Scheme Using LSB and Image Gradient

In the modern age, watermarking techniques are mandatory to secure digital communication over the internet. For an optimal technique, a high signal-to-noise ratio and normalized correctional is required. In this paper, a digital watermarking technique is proposed on the basis of the least significant bit through an image gradient and chaotic map. The image is segmented into noncorrelated blocks, and the gradient of each block is calculated. The gradient of the image expresses the rapid changes in an image. A chaotic substitution box (S-Box) is used to scramble the watermark according to a piecewise linear chaotic map (PWLCM). PWLCM has a positive Lyapunov exponent and better balance property as compared to other chaotic maps. This S-Box technique is capable of producing a disperse sequence with high nonlinearity in the generated sequence. Least significant bit is a simple technique for embedding but it has a high payload capacity and direct pixel manipulation. The embedding payload introduces a tradeoff between robustness and imperceptibility; hence, the image gradient is a technique to identify the best-suited place to embed a watermark and avoid image degradation. By modifying the least significant bits of the original image, the watermark signal is embedded according to the image gradient. In the image gradient, the direction and magnitude decide how much embeding can be done. In comparison with other methods, the experimental results show satisfactory progress in robustness against several image processing and geometrical attacks while maintaining the imperceptibility of the watermark signal

Severity Classification of Diabetic Retinopathy Using an Ensemble Learning Algorithm through Analyzing Retinal Images

Diabetic Retinopathy (DR) refers to the damages endured by the retina as an effect of diabetes. DR has become a severe health concern worldwide, as the number of diabetes patients is soaring uncountably. Periodic eye examination allows doctors to detect DR in patients at an early stage to initiate proper treatments. Advancements in artificial intelligence and camera technology have allowed us to automate the diagnosis of DR, which can benefit millions of patients indeed. This paper inscribes a novel method for DR diagnosis based on the gray-level intensity and texture features extracted from fundus images using a decision tree-based ensemble learning technique. This study primarily works with the Asia Pacific Tele-Ophthalmology Society 2019 Blindness Detection (APTOS 2019 BD) dataset. We undertook several steps to curate its contents to make them more suitable for machine learning applications. Our approach incorporates several image processing techniques, two feature extraction techniques, and one feature selection technique, which results in a classification accuracy of 94.20% (margin of error: ±0.32%) and an F-measure of 93.51% (margin of error: ±0.5%). Several other parameters regarding the proposed method’s performance have been presented to manifest its robustness and reliability. Details on each employed technique have been included to make the provided results reproducible. This method can be a valuable tool for mass retinal screening to detect DR, thus drastically reducing the rate of vision loss attributed to it

Deep Learning Approaches for Detecting Pneumonia in COVID-19 Patients by Analyzing Chest X-Ray Images

The COVID-19 pandemic has wreaked havoc in the daily life of human beings and devastated many economies worldwide, claiming millions of lives so far. Studies on COVID-19 have shown that older adults and people with a history of various medical issues, specifically prior cases of pneumonia, are at a higher risk of developing severe complications from COVID-19. As pneumonia is a common type of infection that spreads in the lungs, doctors usually perform chest X-ray to identify the infected regions of the lungs. In this study, machine learning tools such as LabelBinarizer are used to perform one-hot encoding on the labeled chest X-ray images and transform them into categorical form using Python’s to_categorical tool. Subsequently, various deep learning features such as convolutional neural network (CNN), VGG16, AveragePooling2D, dropout, flatten, dense, and input are used to build a detection model. Adam is used as an optimizer, which can be further applied to predict pneumonia in COVID-19 patients. The model predicted pneumonia with an average accuracy of 91.69%, sensitivity of 95.92%, and specificity of 100%. The model also efficiently reduces training loss and increases accuracy