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Internet of Things (IoT), a strong integration of radio frequency identifier (RFID), wireless devices, and sensors, has provided a difficult yet strong chance to shape existing systems into intelligent ones. Many new applications have been created in the last few years. As many as a million objects are anticipated to be linked together to form a network that can infer meaningful conclusions based on raw data. This means any IoT system is heterogeneous when it comes to the types of devices that are used in the system and how they communicate with each other. In most cases, an IoT network can be described as a layered network, with multiple tiers stacked on top of each other. IoT network performance improvement typically focuses on a single layer. As a result, effectiveness in one layer may rise while that of another may fall. Ultimately, the achievement issue must be addressed by considering improvements in all layers of an IoT network, or at the very least, by considering contiguous hierarchical levels. Using a parallel and clustered architecture in the device layer, this paper examines how to improve the performance of an IoT network's controller layer. A particular clustered architecture at the device level has been shown to increase the performance of an IoT network by 16% percent. Using a clustered architecture at the device layer in conjunction with a parallel architecture at the controller layer boosts performance by 24% overall. [ABSTRACT FROM AUTHOR]

Recommender system (RS) is a unique type of information clarification system that anticipates the user's evaluation of items from a large pool based on the expectations of a single stakeholder. The proposed system is highly useful for getting expected meaning suggestions and guidance for choosing the proper product using artificial intelligence and IoT (Internet of Things) such as chatbot. The current proposed technique makes it easier for stakeholders to make context-based decisions that are optimal rather than reactive, such as which product to buy, news classification based on high filtering views, highly recommended wanted music to choose, and desired product to choose. Recommendation systems are a critical tool for obtaining verified information and making accurate decisions. As a result, operational efficiency would skyrocket, and the risk to the company that uses a recommender system would plummet. This proposed solution can be used in a variety of applications such as commercial hotels OYO and other hotels, hospitals (GYAN), public administrative applications banks HDFC, and ICICI to address potential questions on the spot using intelligence computing as a recommendation system. The existing RS is considering a few factors such as buying records, classification or clustering items, and user's geographic location. Collaborative filtering algorithms (CFAs) are much more common approaches for cooperating to mesh the respective documents they retrieved from the historical data. CFAs are distinguished in plenty of features that are uncommon from other algorithms. In this existing system classification, precision and efficiency and error rate are statistical measurements that need to be enhanced according to the current need to fit for global requirements. The proposed work deals with enhancing accuracy levels of text reviews with the recommender system while interacting by the numerous users for their domains. The authors implemented the recommender system using a user-based CF method and presented the significance of collaborative filtering on the movie domain with a recommender system. This whole experiment has been implanted using the RapidMiner Java-based tool. Results have been compared with existing algorithms to differentiate the efficiency of the current proposed approach. [ABSTRACT FROM AUTHOR]

Infectious disease malaria is a devastating infectious disease that claims the lives of more than 500,000 people worldwide every year. Most of these deaths occur as a result of a delayed or incorrect diagnosis. At the moment, the manual microscope is considered to be the most effective equipment for diagnosing malaria. It is, on the other hand, time-consuming and prone to human error. Because it is such a serious global health issue, it is important that the evaluation process be automated. The objective of this article is to advocate for the automation of the diagnosis process in order to eliminate the need for human intervention in the process. Convolutional neural networks (CNNs) and other deep-learning technologies, such as image processing, are being utilized to evaluate parasitemia in microscopic blood slides in order to enhance diagnostic accuracy. The approach is based on the intensity characteristics of Plasmodium parasites and erythrocytes, which are both known to be variable. Images of infected and noninfected erythrocytes are gathered and fed into the CNN models ResNet50, ResNet34, VGG-16, and VGG-19, which are all trained on the same dataset. The techniques of transfer learning and fine-tuning are employed, and the outcomes are contrasted. The VGG-19 model obtained the best overall performance given the parameters and dataset that were evaluated. [ABSTRACT FROM AUTHOR]

Web services are progressively being used to comprehend service-oriented architectures. Web services facilitate the integration of applications and simplify interoperability. Additionally, it assists in wrapping accessible applications in order for developers to access them using standard languages and protocols. The user faces a difficult challenge in selecting the appropriate service in accordance with the user request as the behavior of the participating service affects the overall performance in discovery, selection, and composition. As a result, it is critical to select a high-quality service provider for these activities. Existing approaches rely on nonfunctional qualities for discovery and selection, but the user cannot always rely on these features, and these QoS values cannot be used to determine the user's or quality perspective. Additionally, the user indicates an interest in a high-quality service based on quality attributes or service with a good reputation throughout the selection process rather than a newly registered service. As a result, a proper bootstrapping mechanism is required to evaluate newly registered services prior to their use by service requestors. This paper proposes a novel bootstrapping mechanism. The contribution of this paper involves (a) a method for evaluating the quality of service (QoS) by focusing on performance-related indicators such as response time, execution time, throughput, latency, and dependability; (b) a methodology for evaluating the QoE attributes based on user reviews that take into account both attributes and opinions; (c) bootstrap the newly registered service based on quality of service and quality of experience; and (d) building a recommender system that suggests the top-rated service for composition. The evaluation results are used to augment currently available online services by providing up-to-date quality of service and quality of experience attributes for discovery, selection, and composition. [ABSTRACT FROM AUTHOR]

To rich good accuracy in the 2D area for wireless sensor network (WSN) nodes, a localization method has to be selected. The objective of this paper is first to select which localization technique is required (Received Signal Strength Indicator (RSSI)) or (Time of Arrival (ToA)) against anchors placement in a 2D area. Depending on whether the anchor nodes are spaced or not and inspired by the idea of using the RSSI method for small distances and the ToA method for greater distances, we will show which method should be used for the positioning process which mainly guarantees a minimal localization error. Second, a two-dimensional localization scheme for WSN which is called Combined Advantages of ToA-RSSI (CA ToA-RSSI), hereafter, ranging methods, is designed in this work, to make the accuracy better during the positioning process. Results provided through MATLAB simulations show that our new technique improves considerably the positioning accuracy compared with the traditional RSSI and ToA ranging method. The proposed scheme can be run under Line of Sight and (LOS) and Nonline of Sight (NLOS) conditions taking into account a difference in the measurement error. [ABSTRACT FROM AUTHOR]

LoRa technology is extensively utilized in the Internet of Things world. It allows a transmission of a low volume of data through small wireless devices. The principle of LoRa networks is to transmit data over the air from sensors with low transmission range, for about tens of kilometers. Those sensors are not expected to be powered by electricity, and they are powered by batteries. We understand that visits to hospitals cannot be eliminated and that visits for full examinations were necessary, but technological progress nowadays could reduce the burden on hospitals thanks to remote controls and treatments in homes using those wireless sensors. So, the use of LoRaWAN protocol could greatly make diagnostic of patients more easily by transmitting data between doctors and patients in a real time manner. The aim of this work is to evaluate the performance of a network that contains numerous mobile sensors. Those sensors connect the doctors, nurse, and patient through a reliable and secure wireless network. Here, we want to evaluate various factors of LoRaWAN protocol that have a big effect on power consumption and data transmission delay.. Moreover, our LoRa-based networking implementation, based on software simulations, appears to be an option that allows for a robust, reliable, and lower overall cost IoT deployment and low bandwidth requirements. With LoRa, we can achieve similar or better link quality to IEEE 802.15.4, with higher data rate and lower costs. [ABSTRACT FROM AUTHOR]

Sensor-based agriculture monitoring systems have limited outcomes on the detection or counting of vegetables from agriculture fields due to the utilization of either conventional color transformations or machine learning-based methods. To overcome these limitations, this research is aimed at proposing an IoT-based smart agriculture monitoring system with multiple algorithms such as detection, quantification, ripeness checking, and detection of infected vegetables. This paper presents smart agriculture monitoring systems for Internet of Things (IoT) applications. The CHT has been applied to detect and quantify vegetables from the agriculture field. Using color thresholding and color segmentation techniques, defected vegetables have also been detected. A machine learning method-convolutional neural network (CNN) has been used for the development and implementation of all algorithms. A comparison between traditional methods and CNN has been simulated in MATLAB to find out the optimal method for its implementation in this agricultural monitoring system. Compared to the traditional methods, the CNN is the optimal method in this research work which performed better over the previously developed algorithms with an accuracy of more than 90%. As an example (case study), a tomato field in Chittagong, Bangladesh, was chosen where a camera-mounted mobile robot captured images from the agriculture field for which the proposed IoT-based smart monitoring system was developed. This system will benefit farmers through the digitally monitored output at an agriculture field in Bangladesh as well as in Malaysia. Since this proposed smart IoT-based system is still driven by bulky, costly, and limited powered sensors, in a future work, for the required power of sensors, this research work is aimed at the design and development of an energy harvester (hybrid) (HEH) based on ultralow power electronics circuits to generate the required power of sensors. Implementation of multiple algorithms using CNN, circular Hough transformation (CHT), color thresholding, and color segmentation methods for the detection, quantification, ripeness checking, and detection of infected crops. [ABSTRACT FROM AUTHOR]

The technology grows quickly in the area of the VLSI physical design; it is crucial to integrate the greater number of transistors and parts into a very small range. Before the placement is completed, the physical and technical positioning of the blocks in the chip area is planned, which is nothing but floor planning. In order to lessen the placement region in the physical layout, floor planning must be carried out effectively. This paper proposes a blended harmony search and particle swarm optimization (BHSPS) algorithm which is the deliberate blend of the harmony search (HS) algorithm, and the particle swarm optimization (PSO) algorithm is proposed to acquire the central goal of the VLSI placement strategy. The objective here is to lessen the field of plan. The MATLAB code for the blended harmony search and particle swarm optimization (BHSPS) algorithm is compiled, and investigations were carried out for better examination through the standard MCNC, i.e., North Carolina Microelectronics Center benchmark circuits. [ABSTRACT FROM AUTHOR]

When used in conjunction with the current floorplan and the optimization technique in circuit design engineering, this research allows for the evaluation of design parameters that can be used to reduce congestion during integrated circuit fabrication. Testing the multiple alternative consequences of IC design will be extremely beneficial in this situation, as will be demonstrated further below. If the importance of placement and routing congestion concerns is underappreciated, the IC implementation may experience significant nonlinear problems throughout the process as a result of the underappreciation of placement and routing congestion concerns. The use of standard optimization techniques in integrated circuit design is not the most effective strategy when it comes to precisely estimating nonlinear aspects in the design of integrated circuits. To this end, advanced tools such as Xilinx VIVADO and the ICC2 have been developed, in addition to the ICC1 and VIRTUOSO, to explore for computations and recover the actual parameters that are required to design optimal placement and routing for well-organized and ordered physical design. Furthermore, this work employs the perimeter degree technique (PDT) to measure routing congestion in both horizontal and vertical directions for a silicon chip region and then applies the technique to lower the density of superfluous routing (DSR) (PDT). Recently, a metaheuristic approach to computation has increased in favor, particularly in the last two decades. It is a classic graph theory problem, and it is also a common topic in the field of optimization. However, it does not provide correct information about where and how nodes should be put, despite its popularity. Consequently, in conjunction with the optimized floorplan data, the optimized model created by the Improved Harmonic Search Optimization algorithm undergoes testing and investigation in order to estimate the amount of congestion that occurs during the routing process in VLSI circuit design and to minimize the amount of congestion that occurs. [ABSTRACT FROM AUTHOR]

For mobile cloud computing (MCC), a local virtual machine- (VM-) based cloudlet is proposed to improve the performance of real-time resource-intensive mobile applications. When a mobile device (MD) discovers a cloudlet nearby, it takes some time to build up a virtual machine (VM) inside the cloudlet before data offloading from the MD to the VM can begin. Live virtual machine migration refers to the process of transferring a running Virtual Machine (VM) from one host to another without interrupting its state. Theoretically, live migration process must not render the instance being migrated unavailable during its execution. However, in practice, there is always a service downtime associated with the process. This paper focuses on addressing the need to reduce the service downtime in case of live VM migration in cloud and providing a solution by implementing and optimizing the multipath transmission control protocol (MPTCP) ability within an Infrastructure as a service (IaaS) cloud to increase the efficiency of live migration. We have also introduced an algorithm, the α-best fit algorithm, to optimize the usage of bandwidth and to effectively streamline the MPTCP performance. [ABSTRACT FROM AUTHOR]

The use of speech as a biomedical signal for diagnosing COVID-19 is investigated using statistical analysis of speech spectral features and classification algorithms based on machine learning. It is established that spectral features of speech, obtained by computing the short-time Fourier Transform (STFT), get altered in a statistical sense as a result of physiological changes. These spectral features are then used as input features to machine learning-based classification algorithms to classify them as coming from a COVID-19 positive individual or not. Speech samples from healthy as well as "asymptomatic" COVID-19 positive individuals have been used in this study. It is shown that the RMS error of statistical distribution fitting is higher in the case of speech samples of COVID-19 positive speech samples as compared to the speech samples of healthy individuals. Five state-of-the-art machine learning classification algorithms have also been analyzed, and the performance evaluation metrics of these algorithms are also presented. The tuning of machine learning model parameters is done so as to minimize the misclassification of COVID-19 positive individuals as being COVID-19 negative since the cost associated with this misclassification is higher than the opposite misclassification. The best performance in terms of the "recall" metric is observed for the Decision Forest algorithm which gives a recall value of 0.7892. [ABSTRACT FROM AUTHOR]

The classroom learning environment facilitates human tutors to interact with every learner and get the opportunity to understand the learner's psychology and then provide learning material (access learner prior knowledge and well align the learning material as per learner requirement) to them accordingly. Implementing this cognitive intelligence in intelligent tutoring system is quite tricky. This research has focused on mimicking human tutor cognitive intelligence in the computer-aided system of offering an exclusive curriculum to the learners. The prime focus of this research article is to evaluate the proposed SeisTutor using Kirkpatrick's four-phase evaluation model. Experimental results depicting the enhanced learning gain through intelligence incorporated SeisTutor as against the intelligence absence are demonstrated. [ABSTRACT FROM AUTHOR]

Education is the cornerstone of improving people's lives and achieving global sustainability. Intelligent systems assist sustainable education with various benefits, including recommending a personalized learning environment to learners. The classroom learning environment facilitates human tutors to interact with every learner and obtain the opportunity to understand the learner's psychology and then provide learning material (access learner previous knowledge and well-align the learning material as per learner requirement) to them accordingly. Implementing this cognitive intelligence in Intelligent Tutoring System is quite tricky. This research focused on mimicking human tutor cognitive intelligence in the computer-aided system of offering an exclusive curriculum or quality education for sustainable learners. The prime focus of this research article was to evaluate the proposed SeisTutor using Kirkpatrick four-phase evaluation model. The experimental results depict the enhanced learning gained through intelligence incorporated SeisTutor against the intelligence absence, as demonstrated. [ABSTRACT FROM AUTHOR]

To improve the quality of knowledge service selection in a cloud manufacturing environment, this paper proposes a cloud manufacturing knowledge service optimization decision method based on users' psychological behavior. Based on the characteristic analysis of cloud manufacturing knowledge service, establish the optimal evaluation index system of cloud manufacturing knowledge service, use the rough set theory to assign initial weights to each evaluation index, and adjust the initial weights according to the user's multiattribute preference to ensure that the consequences are allocated correctly. The system can help counselors acquire psychological knowledge in time and identify counselors with suicidal tendencies to prevent danger. This paper collected some psychological information data and built a knowledge graph by creating a dictionary and generating entities and relationships. The Han language processing word segmentation tool generates keywords, and CHI (Chi-square) feature selection is used to classify the problem. This feature selection is a statistical premise test that is acceptable when the chi-square test results are distributed with the null hypothesis. It includes the Pearson chi-square test and its variations. The Chi-square test has several benefits, including its distributed processing resilience, ease of computation, broad information gained from the test, usage in research when statistical assumptions are not satisfied, and adaptability in organizing information from multiple or many more group investigations. For improving question and answer efficiency, compared with other models, the BiLSTM (bidirectional long short-term memory) model is preferred to build suicidal tendencies. The Han language processing is a method that is used for word segmentation, and the advantage of this method is that it plays a key role in the word segmentation tool and generates keywords, and CHI (Chi-square) feature selection is used to classify the problem. Text classifier detects dangerous user utterances, question template matching, and answer generation by computing similarity scores. Finally, the system accuracy test is carried out, proving that the system can effectively answer the questions related to psychological counseling. The extensive experiments reveal that the method in this paper's accuracy rate, recall rate, and F1 value is much superior to other standard models in detecting psychological issues. [ABSTRACT FROM AUTHOR]

Lung Cancer has been an issue of concern these days as there is an alarming toll of rising deaths every year. A good amount of research is pursued on aspects of the genetic and hereditary and also computational methods to detect Lung cancer. Even though there is a lack of awareness about this disease due to a colossal gap between technical and clinical research areas. Accordingly this research paper presents a comprehensive study on Lung Cancer detection in terms of simulation of medical images and clinical analysis wherein one of the KRAS mutations has been analysed in lung cancer patients and their lung images have been used for developing medical images with better tumour spot detection. The computational technique used for simulation purpose involves morphological image processing methods, which mainly work on the topological and shape content of the images acquired. [ABSTRACT FROM AUTHOR]