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Topic modeling is a machine learning algorithm based on statistics that follows unsupervised machine learning techniques for mapping a high-dimensional corpus to a low-dimensional topical subspace, but it could be better. A topic model’s topic is expected to be interpretable as a concept, i.e., correspond to human understanding of a topic occurring in texts. While discovering corpus themes, inference constantly uses vocabulary that impacts topic quality due to its size. Inflectional forms are in the corpus. Since words frequently appear in the same sentence and are likely to have a latent topic, practically all topic models rely on co-occurrence signals between various terms in the corpus. The topics get weaker because of the abundance of distinct tokens in languages with extensive inflectional morphology. Lemmatization is often used to preempt this problem. Gujarati is one of the morphologically rich languages, as a word may have several inflectional forms. This paper proposes a deterministic finite automaton (DFA) based lemmatization technique for the Gujarati language to transform lemmas into their root words. The set of topics is then inferred from this lemmatized corpus of Gujarati text. We employ statistical divergence measurements to identify semantically less coherent (overly general) topics. The result shows that the lemmatized Gujarati corpus learns more interpretable and meaningful subjects than unlemmatized text. Finally, results show that lemmatization curtails the size of vocabulary decreases by 16% and the semantic coherence for all three measurements—Log Conditional Probability, Pointwise Mutual Information, and Normalized Pointwise Mutual Information—from −9.39 to −7.49, −6.79 to −5.18, and −0.23 to −0.17, respectively.

Cardiovascular diseases (CVDs) are a significant cause of death worldwide. CVDs can be prevented by diagnosing heartbeat sounds and other conventional techniques early to reduce the harmful effects caused by CVDs. However, it is still challenging to segment, extract features, and predict heartbeat sounds in elderly people. The inception of deep learning (DL) algorithms has helped detect various types of heartbeat sounds at an early stage. Motivated by this, we proposed an intelligent architecture categorizing heartbeat into normal and murmurs for elderly people. We have used a standard heartbeat dataset with heartbeat class labels, i.e., normal and murmur. Furthermore, it is augmented and preprocessed by normalization and standardization to significantly reduce computational power and time. The proposed convolutional neural network and bi-directional gated recurrent unit (CNN + BiGRU) attention-based architecture for the classification of heartbeat sound achieves an accuracy of 90% compared to the baseline approaches. Hence, the proposed novel CNN + BiGRU attention-based architecture is superior to other DL models for heartbeat sound classification.

A cognitive radio network (CRN) is an intelligent network that can detect unoccupied spectrum space without interfering with the primary user (PU). Spectrum scarcity arises due to the stable channel allocation, which the CRN handles. Spectrum handoff management is a critical problem that must be addressed in the CRN to ensure indefinite connection and profitable use of unallocated spectrum space for secondary users (SUs). Spectrum handoff (SHO) has some disadvantages, i.e., communication delay and power consumption. To overcome these drawbacks, a reduction in handoff should be a priority. This study proposes the use of dynamic spectrum access (DSA) to check for available channels for SU during handoff using a metaheuristic algorithm depending on machine learning. The simulation results show that the proposed “support vector machine-based red deer algorithm” (SVM-RDA) is resilient and has low complexity. The suggested algorithm’s experimental setup offers several handoffs, unsuccessful handoffs, handoff delay, throughput, signal-to-noise ratio (SNR), SU bandwidth, and total spectrum bandwidth. This study provides an improved system performance during SHO. The inferred technique anticipates handoff delay and minimizes the handoff numbers. The results show that the recommended method is better at making predictions with fewer handoffs compared to the other three.

Over the last few decades, the healthcare industry has continuously grown, with hundreds of thousands of patients obtaining treatment remotely using smart devices. Data security becomes a prime concern with such a massive increase in the number of patients. Numerous attacks on healthcare data have recently been identified that can put the patient’s identity at stake. For example, the private data of millions of patients have been published online, posing a severe risk to patients’ data privacy. However, with the advent of Industry 4.0, medical practitioners can digitally assess the patient’s condition and administer prompt prescriptions. However, wearable devices are also vulnerable to numerous security threats, such as session hijacking, data manipulation, and spoofing attacks. Attackers can tamper with the patient’s wearable device and relays the tampered data to the concerned doctor. This can put the patient’s life at high risk. Since blockchain is a transparent and immutable decentralized system, it can be utilized for securely storing patient’s wearable data. Artificial Intelligence (AI), on the other hand, utilizes different machine learning techniques to classify malicious data from an oncoming stream of patient’s wearable data. An amalgamation of these two technologies would make the possibility of tampering the patient’s data extremely difficult. To mitigate the aforementioned issues, this paper proposes a blockchain and AI-envisioned secure and trusted framework (HEART). Here, Long-Short Term Model (LSTM) is used to classify wearable devices as malicious or non-malicious. Then, we design a smart contract that allows only of those patients’ data having a wearable device to be classified as non-malicious to the public blockchain network. This information is then accessible to all involved in the patient’s care. We then evaluate the HEART’s performance considering various evaluation metrics such as accuracy, recall, precision, scalability, and network latency. On the training and testing sets, the model achieves accuracies of 93% and 92.92%, respectively.

The Internet of Things (IoT) is a key enabler technology that recently received significant attention from the scientific community across the globe. It helps transform everyone’s life by connecting physical and virtual devices with each other to offer staggering benefits, such as automation and control, higher productivity, real-time information access, and improved efficiency. However, IoT devices and their accumulated data are susceptible to various security threats and vulnerabilities, such as data integrity, denial-of-service, interception, and information disclosure attacks. In recent years, the IoT with blockchain technology has seen rapid growth, where smart contracts play an essential role in validating IoT data. However, these smart contracts can be vulnerable and degrade the performance of IoT applications. Hence, besides offering indispensable features to ease human lives, there is also a need to confront IoT environment security attacks, especially data integrity attacks. Toward this aim, this paper proposed an artificial intelligence-based system model with a dual objective. It first detects the malicious user trying to compromise the IoT environment using a binary classification problem. Further, blockchain technology is utilized to offer tamper-proof storage to store non-malicious IoT data. However, a malicious user can exploit the blockchain-based smart contract to deteriorate the performance IoT environment. For that, this paper utilizes deep learning algorithms to classify malicious and non-malicious smart contracts. The proposed system model offers an end-to-end security pipeline through which the IoT data are disseminated to the recipient. Lastly, the proposed system model is evaluated by considering different assessment measures that comprise the training accuracy, training loss, classification measures (precision, recall, and F1 score), and receiver operating characteristic (ROC) curve.

Mobile applications have rapidly grown over the past few decades to offer futuristic applications, such as autonomous vehicles, smart farming, and smart city. Such applications require ubiquitous, real-time, and secure communications to deliver services quickly. Toward this aim, sixth-generation (6G) wireless technology offers superior performance with high reliability, enhanced transmission rate, and low latency. However, managing the resources of the aforementioned applications is highly complex in the precarious network. An adversary can perform various network-related attacks (i.e., data injection or modification) to jeopardize the regular operation of the smart applications. Therefore, incorporating blockchain technology in the smart application can be a prominent solution to tackle security, reliability, and data-sharing privacy concerns. Motivated by the same, we presented a case study on public safety applications that utilizes the essential characteristics of artificial intelligence (AI), blockchain, and a 6G network to handle data integrity attacks on the crime data. The case study is assessed using various performance parameters by considering blockchain scalability, packet drop ratio, and training accuracy. Lastly, we explored different research challenges of adopting blockchain in the 6G wireless network.

Using thermal insulation in negative temperature acts to reduce heat flow to the cooled space or to objects that have a temperature below ambient temperature. To achieve economic operation of the space to be cooled insulation thickness and quality is an important factor. In this article we want to compare three products used in thermal insulation at negative temperatures: expanded polystyrene, non-woven and wool coats. The materials will be tested with a mechanical vapor compression refrigerator capable of producing temperatures in the range +4 .. -35 ° C, managed by a programmer Dixel capable of recording values between +40. .. -60 °C. Refrigeration insulation enclosure was made with 100 mm expanded polystyrene. On one side of the enclosure will be a cut of 250 * 250 mm, chosen in a central position where the material will be introduced to be tested. The dimensions of the samples are 250 * 250 * 60 mm. To check the insulation properties of materials it will be used a temperature logger capable of recording with two probes temperatures between +125...-40° C. One of the probes will be inserted inside the refrigerator and the second probe will be positioned to the outside of the test material adhered to an aluminum plate, in order to read a average temperature. The difference in thickness of the insulation shall be filled with non-woven material. Hardening the assembly will be made using a 6 mm thick OSB board. The materials will be tested in an identical ambient temperature and humidity.

This paper presents the simulation method for evaluating the heating system from a church. The inside climate has been evaluated by measures of temperature and humidity taken in the winter season. The aim of the paper is to model and validate the indoor climate measures thought numerical analysis and to evaluate the heating system performance. The paper include a case study over and representative category of buildings, used as worship place that can contain heritage values. Nowadays, to conserve the historical heritage is a fact studied in many countries of Europe.

Based on the monitoring campaigns, we have evaluated the indoor microclimate in a church. Most church buildings are characterised by low thermal comfort due primarily to their architecture, heating huge volume of air. The CFD tools was used for thermal comfort evaluation in case of the existing heating system during the winter season. The simulation model was validated with experimental data and it was used for thermal and air velocity profiles in the occupancy zone of churchgoers. The aim of the paper is the study the feasibility of maintaining the heritage values of the churches while achieving the significant improvement of the thermal comfort.

Globally, there are several critical infrastructure networks (water and gas networks) whose disruption or destruction would significantly affect the maintenance of vital societal functions, such as the health, safety, security, and social or economic well-being of people. They would also have significant local, regional, and national impacts as a result of the inability to maintain those functions, and would have similar cross-border effects. The main objective of this article is to investigate by comparative numerical studies the structural response of three types of buried pipes made of different materials, primarily steel, concrete, and high-density polyethylene, resulting from the impact of the environment through exceptional external actions, such as explosions at the surface of the land in the vicinity of the laying areas. The dynamic transient analysis of the equation of motion with the application of the explicit integration procedure was performed with the ANSYS numerical simulation program. This study allows designers to solve complex problems related to the quality of the laying ground of water networks to canals. The knowledge accumulated gives us the possibility to correctly specify the optimal economic and technical value of the ratio between the laying depth of pipes and their diameter, the importance of the radius ratio of the pipe and the thickness of its wall, and, importantly, the improvement of the quality of the foundation ground. Following the results obtained, it is estimated that the optimal economic and technical value of the ratio between the laying depth of the pipes (H) and their diameter (D) is 3, regardless of the material from which the pipe is made.

At the end of 2019, a variation of a coronavirus, named SARS-CoV-2, has been identified as being responsible for a respiratory illness disease (COVID-19). Since ventilation is an important factor that influences airborne transmission, we proposed to study the impact of heating, ventilation and air-conditioning (HVAC) with a variable air volume (VAV) primary air system, on the dispersion of infectious aerosols, in a cardiac intensive care unit, using a transient simulation with computational fluid dynamics (CFD), based on the finite element method (FEM). We analyzed three scenarios that followed the dispersion of pathogen carrying expiratory droplets particles from coughing, from patients possibly infected with COVID-19, depending on the location of the patients in the intensive care unit. Our study provides the mechanism for spread of infectious aerosols, and possibly of COVID-19 infection, by air conditioning systems and also highlights important recommendations for disease control and optimization of ventilation in intensive care units, by increasing the use of outdoor air and the rate of air change, decreasing the recirculation of air and using high-efficiency particulate air (HEPA) filters. The CFD-FEM simulation approach that was applied in our study could also be extended to other targets, such as public transport, theaters, philharmonics and amphitheaters from educational units.