With the widespread use of social networks, spam messages against them have become a major issue. Spam detection methods can be broadly divided into expert-based and machine learning-based detection methods. When experts participate in spam detection, the detection accuracy is fairly high. However, this method is highly time-consuming and expensive. Conversely, methods using machine learning have the advantage of automation, but their accuracy is relatively low. This paper proposes a spam-detection framework that combines and fully exploits the advantages of both methods. To reduce the workload of the experts, all messages are first analyzed via a primary machine learning filter, and those that are determined to be normal messages are allowed through, whereas suspicious messages are flagged. The flagged messages are subsequently analyzed by an expert to enhance the overall system accuracy. In the filtering process, costbased machine learning is used to prevent the fatal error of misidentifying a spam message as a normal message. In addition, to obviate the continuously evolving spam trends, a module that periodically updates the expert-diagnosis results on the training dataset is incorporated into the framework. The results of experiments conducted, on an imbalanced dataset of spam tweets and normal tweets in a ratio similar to the actual situation in real life, indicate that the proposed framework has a spam-detection rate of almost 92.8%, which is higher than that of the conventional machine learning technique. Furthermore, the proposed framework delivered stable high performance even in an environment where social network messages changed continuously, unlike the conventional technique, which exhibited large performance deviations.INDEX TERMS Expert decision making, machine learning, real-time spam detection, social network, Twitter spam.

With the widespread use of over-the-top (OTT) media, such as YouTube and Netflix, network markets are changing and innovating rapidly, making it essential for network providers to quickly and efficiently analyze OTT traffic with respect to pricing plans and infrastructure investments. This study proposes a time-aware deep-learning method of analyzing OTT traffic to classify users for this purpose. With traditional deep learning, classification accuracy can be improved over conventional methods, but it takes a considerable amount of time. Therefore, we propose a novel framework to better exploit accuracy, which is the strength of deep learning, while dramatically reducing classification time. This framework uses a two-step classification process. Because only ambiguous data need to be subjected to deep-learning classification, vast numbers of unambiguous data can be filtered out. This reduces the workload and ensures higher accuracy. The resultant method provides a simple method for customizing pricing plans and load balancing by classifying OTT users more accurately.

The over-the-top (OTT) market for media consumption over wired and wireless Internet is growing. It is, therefore, crucial that service providers and carriers participating in the OTT market analyze consumer traffic for pricing, service delivery, infrastructure investments, etc. The OTT market has many consumer groups, but the proportion of users is not consistent in each. Furthermore, as multimedia consumption has increased owing to the COVID-19 epidemic, the OTT market has changed rapidly. If this is not reflected, the analysis will not be accurate. Therefore, we propose a framework that can classify consumers well based on actual OTT market environment conditions. First, by applying our proposed conditional probability-based method to basic machine learning techniques, such as support vector machine, k-nearest neighbor, and decision tree, we can improve the classification performance, even for an imbalanced OTT consumer distribution. Then, it is possible to analyze the changing consumer trends by dynamically retraining the incoming OTT consumer data. Conventional methods result in low classification accuracy in low-number classes, but our method shows an improvement of 5.3–19.2% based on recall. Moreover, conventional methods have shown large fluctuations in performance as the OTT market environment has changed, but our framework consistently maintains high performance.

Currently, Internet of medical things-based technologies provide a foundation for remote data collection and medical assistance for various diseases. Along with developments in computer vision, the application of Artificial Intelligence and Deep Learning in IOMT devices aids in the design of effective CAD systems for various diseases such as melanoma cancer even in the absence of experts. However, accurate segmentation of melanoma skin lesions from images by CAD systems is necessary to carry out an effective diagnosis. Nevertheless, the visual similarity between normal and melanoma lesions is very high, which leads to less accuracy of various traditional, parametric, and deep learning-based methods. Hence, as a solution to the challenge of accurate segmentation, we propose an advanced generative deep learning model called the Conditional Generative Adversarial Network (cGAN) for lesion segmentation. In the suggested technique, the generation of segmented images is conditional on dermoscopic images of skin lesions to generate accurate segmentation. We assessed the proposed model using three distinct datasets including DermQuest, DermIS, and ISCI2016, and attained optimal segmentation results of 99%, 97%, and 95% performance accuracy, respectively.

The Internet of Things (IoT) is a platform that manages daily life tasks to establish an interaction between things and humans. One of its applications, the smart office that uses the Internet to monitor electrical appliances and sensor data using an automation system, is presented in this study. Some of the limitations of the existing office automation system are an unfriendly user interface, lack of IoT technology, high cost, or restricted range of wireless transmission. Therefore, this paper presents the design and fabrication of an IoT-based office automation system with a user-friendly smartphone interface. Also, real-time data monitoring is conducted for the predictive maintenance of sensor nodes. This model uses an Arduino Mega 2560 Rev3 microcontroller connected to different appliances and sensors. The data collected from different sensors and appliances are sent to the cloud and accessible to the user on their smartphone despite their location. A sensor fault prediction model based on a machine learning algorithm is proposed in this paper, where the k-nearest neighbors model achieved better performance with 99.63% accuracy, 99.59% F1-score, and 99.67% recall. The performance of both models, i.e., k-nearest neighbors and naive Bayes, was evaluated using different performance metrics such as precision, recall, F1-score, and accuracy. It is a reliable, continuous, and stable automation system that provides safety and convenience to smart office employees and improves their work efficiency while saving resources.

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Since wireless resources are limited, an efficient way of utilizing wireless resources is needed in selecting IDSs. We propose a monitoring node selection scheme for intrusion detection in congested wireless sensor network. Network congestion is an important issue in mobile network. The network congestion does not guarantee a proper detection rate and congested networks should cause an unreliable network. We consider congested intrusion detection tasks by queuing theory. We confirm that proposed algorithm guarantee QoS of monitoring tasks and reliable sensor networks.