The 21st century has seen rapid changes in technology, industry, and social patterns. Most industries have moved towards automation, and human intervention has decreased, which has led to a revolution in industries, named the fourth industrial revolution (Industry 4.0). Industry 4.0 or the fourth industrial revolution (IR 4.0) relies heavily on the Internet of Things (IoT) and wireless sensor networks (WSN). IoT and WSN are used in various control systems, including environmental monitoring, home automation, and chemical/biological attack detection. IoT devices and applications are used to process extracted data from WSN devices and transmit them to remote locations. This systematic literature review offers a wide range of information on Industry 4.0, finds research gaps, and recommends future directions. Seven research questions are addressed in this article: (i) What are the contributions of WSN in IR 4.0? (ii) What are the contributions of IoT in IR 4.0? (iii) What are the types of WSN coverage areas for IR 4.0? (iv) What are the major types of network intruders in WSN and IoT systems? (v) What are the prominent network security attacks in WSN and IoT? (vi) What are the significant issues in IoT and WSN frameworks? and (vii) What are the limitations and research gaps in the existing work? This study mainly focuses on research solutions and new techniques to automate Industry 4.0. In this research, we analyzed over 130 articles from 2014 until 2021. This paper covers several aspects of Industry 4.0, from the designing phase to security needs, from the deployment stage to the classification of the network, the difficulties, challenges, and future directions.

Depression is a prevalent sickness, spreading worldwide with potentially serious implications. Timely recognition of emotional responses plays a pivotal function at present, with the profound expansion of social media and users of the internet. Mental illnesses are highly hazardous, stirring more than three hundred million people. Moreover, that is why research is focused on this subject. With the advancements of machine learning and the availability of sample data relevant to depression, there is the possibility of developing an early depression diagnostic system, which is key to lessening the number of afflicted individuals. This paper proposes a productive model by implementing the Long-Short Term Memory (LSTM) model, consisting of two hidden layers and large bias with Recurrent Neural Network (RNN) with two dense layers, to predict depression from text, which can be beneficial in protecting individuals from mental disorders and suicidal affairs. We train RNN on textual data to identify depression from text, semantics, and written content. The proposed framework achieves 99.0% accuracy, higher than its counterpart, frequency-based deep learning models, whereas the false positive rate is reduced. We also compare the proposed model with other models regarding its mean accuracy. The proposed approach indicates the feasibility of RNN and LSTM by achieving exceptional results for early recognition of depression in the emotions of numerous social media subscribers.

Understanding brain diseases such as categorizing Brain-Tumor (BT) is critical to assess the tumors and facilitate the patient with proper cure as per their categorizations. Numerous imaging schemes exist for BT detection, such as Magnetic Resonance Imaging (MRI), generally utilized because of the better quality of images and the reality of depending on non-ionizing radiation. This paper proposes an approach to detect distinctive BT types using Gaussian Convolutional Neural Network (GCNN) on two datasets. One of the datasets is used to classify tumors into pituitary, glioma, and meningioma. The other one separates the three grades of glioma, i.e., Grade-two, Grade-three, and Grade-four. These datasets have ’233’ and ’73’ victims with a total of ’3064’ and ’516’ images on T1-weighted complexity improved pictures for the first and second datasets, separately. The proposed approach achieves an accuracy of 99.8% and 97.14% for the two datasets. The experimental results highlight the efficiency of the proposed approach for BT multi-class categorization.

Despite the numerous and noticeable inherited gains of Mobile Cloud Computing (MCC) in healthcare, its growth is being hindered by privacy and security challenges. Such issues require the utmost urgent attention to realize its full scale and efficient usage. There is a need to secure Health Information worldwide, regionally, and locally. To fully avail of the health services, it is crucial to put in place the demanded security practices for the prevention of security breaches and vulnerabilities. Hence, this research is deliberated on to provide requirement-oriented health information security using the Modular Encryption Standard (MES) based on the layered modeling of the security measures. The performance analysis shows that the proposed work excels, compared to other commonly used algorithms against the health information security at the MCC environment in terms of better performance and auxiliary qualitative security ensuring measures.

Wearable technology has played an essential role in the Mobile Health (mHealth) sector for diagnosis, treatment, and rehabilitation of numerous diseases and disorders. One such neuro-degenerative disorder is Parkinson’s Disease (PD). It is categorized by motor symptoms that affect a patient’s motor skills and non-motor symptoms that affect the general health of a PD patient. The quality of life of a patient with PD is highly compromised. To date, there is no cure for the disease, but early intervention and assistive care can help a PD patient to perform daily activities with considerable ease. Many research works in PD management discuss the challenges that healthcare professionals face in the early detection and management of this disease. Sensor devices have been promising to overcome these challenges to a certain degree because of the low cost and accuracy in measurement, yielding precise conclusive results to detect, monitor, and manage PD. This paper presents a Systematic Literature Review (SLR) that provides an in-depth analysis of the PD symptoms, Motor and Non-Motor Symptoms (NMS), the current diagnosis and management techniques used and their efficacy. The paper also highlights the work of various researchers in wearable sensors and their proposals to improve the quality of life of a PD patient by diagnosing, monitoring, and managing PD symptoms remotely via wearable sensors. Another area of focus is commercially available wearables for PD management and a few promising works in progress. This paper will be beneficial for future researchers to identify existing gaps and provide the clinicians better insight into the disease progression, and avoid complications. This paper analyzes around 50+ articles from 2016 to 2021 and concludes that there is still much room for improvement in wearables for PD management during the research process. While much work has been attributed to PD Motor Symptom management, there is little focus on the management of PD NMS via wearable sensors. Furthermore, this paper also presents future work for PD management.