Whenever, the Internet of Things (IoT) applications and devices increased, the capability of the its access frequently stressed. That can lead a significant bottleneck problem for network performance in different layers of an end point to end point (P2P) communication route. So, an appropriate characteristic (i.e., classification) of the time changing traffic prediction has been used to solve this issue. Nevertheless, stills remain at great an open defy. Due to of the most of the presenting solutions depend on machine learning (ML) methods, that though give high calculation cost, where they are not taking into account the fine-accurately flow classification of the IoT devices is needed. Therefore, this paper presents a new model based on the Spike Neural Network (SNN) called IoT-Traffic Classification (IoT-TCSNN) to classify IoT devices traffic. The model consists of four phases: data preprocessing, feature extraction, classier and evaluation. The proposed model performance is evaluated according to evaluation metrics: accuracy, precision, recall and F1-score and energy usage in comparison with two models: ML based Support Vector Machine IoT-TCSVM and ML based Deep Neural Network (IoT-TCDNN). The evaluations result has been shown that IoT-TCSNN consumes less energy in contrast to IoT-TCDNN and IoT-TCSVM. Also, it gives high accuracy in comparison with IoT-TCSVM.
Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Decision Tre
... Show MoreLearning a foreign language is a highly interactive process, and a belief that communicative activities foster a great amount of linguistic production provides language practice and opportunities for negotiation of meaning during communicative exchanges. Thus, this study examines what benefits learner-centered classroom setting offers compared with that of teacher–centered classroom, and how less proficient learners accomplish their tasks and activities with scaffolded help during interaction with the help of proficient classmates and under the guidance of a skilful person, i.e., the teacher. The subjects participating in this study are 30 Iraqi 4th year college students in the Department of English, College of Arts , Univer
... Show MoreBotnet detection develops a challenging problem in numerous fields such as order, cybersecurity, law, finance, healthcare, and so on. The botnet signifies the group of co-operated Internet connected devices controlled by cyber criminals for starting co-ordinated attacks and applying various malicious events. While the botnet is seamlessly dynamic with developing counter-measures projected by both network and host-based detection techniques, the convention techniques are failed to attain sufficient safety to botnet threats. Thus, machine learning approaches are established for detecting and classifying botnets for cybersecurity. This article presents a novel dragonfly algorithm with multi-class support vector machines enabled botnet
... Show MoreWitnessing human societies with the turn of the century atheist twenty huge revolution in information , the result of scientific and technological developments rapidly in space science and communications , and that made the whole world is like a small village not linked by road as it was in ancient times, through the rapid transportation as was the case a few years ago , thanks to the remote sensing devices that roam in space observant everything on the ground , that the information networks that overflowed the world a tremendous amount of information provided for each inhabitants of the earth , which made this information requirement for human life and human survival and well-being , as it has allowed that information to humans opportun
... Show MoreBlogs have emerged as a powerful technology tool for English as a Foreign Language (EFL) classrooms. This literature review aims to provide an overview of the use of blogs as learning tools in EFL classrooms. The study examines the benefits and challenges of using blogs for language learning and the different types of blogs that can be used for language learning. It provides suggestions for teachers interested in using blogs as learning tools in their EFL classrooms. The findings suggest that blogs are a valuable and effective tool for language learning, particularly in promoting collaboration, communication, and motivation.
Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Deci
... Show MoreThis paper offers a systemic review of the deep learning methods to detect violence on campus, which is a critical issue in intelligent surveillance to improve the student safety and prompt cut off of violent accidents. The review reviews studies published 2018-2025, concentrating on model structure to detect fights, bullying, vandalism, and aggressive behavior on problematic campuses due to occlusion and light variations and complicated human interactions. The research design includes a comparative study of different deep learning networks, such as CNNs, RNNs, 3D CNNs, attention-based networks, transformers, graph neural networks, neuro-fuzzy, and multimodal systems and federated learning methods. The paper also assesses benchmark
... Show More