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Developing a Reliable System for Real-Life Emails Classification Using Machine Learning Approach
abdulkareem Radhi
Intelligent Systems and Networks, 2021
Cyber World has become accessible, public, and commonly used to distribute and exchange messages between malicious actors, terrorists, and illegally motivated persons. Electronic mail is one of the most frequently used transfers of information on internet media. E-mails are the most important digital proof that courts in various countries and communities use to condemn and that enables researchers to work continually to improve e-mail analysis using state-of-the-art technology to find digital evidence from e-mails. This work introduces a distinctive technology to analyze emails. It is based on consecutive phases, starting with data processing, extraction, compilation, then implementing the SWARM algorithm to adjust the output and to transfer these electronic mails for realistic and precise results by adjusting the support algorithm of vector machines. For email forensic analysis this system includes all the sentiment terms plus positives and negative cases. It can deal with the machine learning algorithm (Sent WordNet 3.0). Enron Data set is used to test the proposed framework. In the best case, a high accuracy rate is 92%.
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An Approach to Detect Alopecia Areata Hair Disease Using Deep Learning
Namit Khanduja
2021
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A Comprehensive Review on Artificial Intelligence/Machine Learning Algorithms for Empowering the Future IoT Toward 6G Era
Rezwanul Mahmood
IEEE Access, 2022
The evolution of the wireless network systems over decades has been providing new services to the users with the help of innovative network and device technologies. In recent times, the 5G network systems are about to be deployed which creates the opportunity to realize massive connectivity with high throughput, low latency, high energy efficiency and security. It also focuses on providing massive Internet of Things (IoT) network connectivity as well as services for good health, large-scale agricultural and industrial production, intelligent traffic control and electricity generation, transmission and distribution systems. However, the ever-increasing number of user devices is directing the researchers towards beyond 5G systems to allocate these user devices with higher bandwidth. Researches on the 6G wireless network systems have already begun to provide higher bandwidth availability for densely connected larger network devices with QoS surety. Researchers are leveraging artificial intelligence (AI)/machine learning (ML) for enhancing future IoT network operations and services. This paper attempts to discuss AI/ML algorithms that can help in developing energy efficient, secured and effective IoT network operations and services. In particular, our article concentrates on the major issues and factors that influence the design of the communication systems for future IoT with the integration of AI/ML. It also highlights application domains, including smart healthcare, smart agriculture, smart transportation, smart grid and smart industry that can operate efficiently and securely. Finally, this paper ends with the discussion on future research scopes with these algorithms in addressing the open issues of the future IoT network systems.
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Traffic Prediction Using Machine Learning
H.R. deekshetha
Evolutionary Computing and Mobile Sustainable Networks, 2021
The paper deals with traffic prediction that can be done in intelligent transportation systems which involves the prediction between the previous year’s data set and the recent year's data which ultimately provides the accuracy and mean square error. This prediction will be helpful for the people who are in need to check the immediate traffic state. The traffic data is predicated on a basis of 1-hour time gap. Live statistics of the traffic are analyzed from this prediction. So this will be easier to analyze when the user is on driving too. The system compares the data of all roads and determines the most populated roads of the city. I propose the regression model in order to predict the traffic using machine learning by importing Sklearn, Keras, and Tensorflow libraries.
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Managing security in IoT by applying the deep neural network-based security framework
rami malik
Eastern-European Journal of Enterprise Technologies
Security issues and Internet of Things (IoT) risks in several areas are growing steadily with the increased usage of IoT. The systems have developed weaknesses in computer and memory constraints in most IoT operating systems. IoT devices typically cannot operate complicated defense measures because of their poor processing capabilities. A shortage of IoT ecosystems is the most critical impediment to developing a secured IoT device. In addition, security issues create several problems, such as data access control, attacks, vulnerabilities, and privacy protection issues. These security issues lead to affect the originality of the data that cause to affects the data analysis. This research proposes an AI-based security method for the IoT environment (AI-SM-IoT) system to overcome security problems in IoT. This design was based on the edge of the network of AI-enabled security components for IoT emergency preparedness. The modules presented detect, identify and continue to identify the ...
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At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives
Antonio Guerrieri
Sensors
Given its advantages in low latency, fast response, context-aware services, mobility, and privacy preservation, edge computing has emerged as the key support for intelligent applications and 5G/6G Internet of things (IoT) networks. This technology extends the cloud by providing intermediate services at the edge of the network and improving the quality of service for latency-sensitive applications. Many AI-based solutions with machine learning, deep learning, and swarm intelligence have exhibited the high potential to perform intelligent cognitive sensing, intelligent network management, big data analytics, and security enhancement for edge-based smart applications. Despite its many benefits, there are still concerns about the required capabilities of intelligent edge computing to deal with the computational complexity of machine learning techniques for big IoT data analytics. Resource constraints of edge computing, distributed computing, efficient orchestration, and synchronization ...
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Application of Machine Intelligence in IoT-Enabled Healthcare Monitoring Systems: A Case Study-Based Approach
Amartya Chakraborty
Smart and Secure Internet of Healthcare Things, Taylor & Francis, CRC Press, 2022
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Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applications
ali vahedifar
arXiv (Cornell University), 2023
Robust machine learning (ML) models can be developed by leveraging large volumes of data and distributing the computational tasks across numerous devices or servers. Federated learning (FL) is a technique in the realm of ML that facilitates this goal by utilizing cloud infrastructure to enable collaborative model training among a network of decentralized devices. Beyond distributing the computational load, FL targets the resolution of privacy issues and the reduction of communication costs simultaneously. To protect user privacy, FL requires users to send model updates rather than transmitting large quantities of raw and potentially confidential data. Specifically, individuals train ML models locally using their own data and then upload the results in the form of weights and gradients to the cloud for aggregation into the global model. This strategy is also advantageous in environments with limited bandwidth or high communication costs, as it prevents the transmission of large data volumes. With the increasing volume of data and rising privacy concerns, alongside the emergence of large-scale ML models like Large Language Models (LLMs), FL presents itself as a timely and relevant solution. It is therefore essential to review current FL algorithms to guide future research that meets the rapidly evolving ML demands. This survey provides a comprehensive analysis and comparison of the most recent FL algorithms, evaluating them on various fronts including mathematical frameworks, privacy protection, resource allocation, and applications. Beyond summarizing existing FL methods, this survey identifies potential gaps, open areas, and future challenges based on the performance reports and algorithms used in recent studies. This survey enables researchers to readily identify existing limitations in the FL field for further exploration.
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A Blockchain-Based Deep-Learning-Driven Architecture for Quality Routing in Wireless Sensor Networks
Sana Amjad
IEEE Access
Over the past few years, great importance has been given to wireless sensor networks (WSNs) as they play a significant role in facilitating the world with daily life services like healthcare, military, social products, etc. However, heterogeneous nature of WSNs makes them prone to various attacks, which results in low throughput, and high network delay and high energy consumption. In the WSNs, routing is performed using different routing protocols like low-energy adaptive clustering hierarchy (LEACH), heterogeneous gateway-based energy-aware multi-hop routing (HMGEAR), etc. In such protocols, some nodes in the network may perform malicious activities. Therefore, four deep learning (DL) techniques and a real-time message content validation (RMCV) scheme based on blockchain are used in the proposed network for the detection of malicious nodes (MNs). Moreover, to analyse the routing data in the WSN, DL models are trained on a state-of-the-art dataset generated from LEACH, known as WSN-DS 2016. The WSN contains three types of nodes: sensor nodes, cluster heads (CHs) and the base station (BS). The CHs after aggregating the data received from the sensor nodes, send it towards the BS. Furthermore, to overcome the single point of failure issue, a decentralized blockchain is deployed on CHs and BS. Additionally, MNs are removed from the network using RMCV and DL techniques. Moreover, legitimate nodes (LNs) are registered in the blockchain network using proof-of-authority consensus protocol. The protocol outperforms proof-of-work in terms of computational cost. Later, routing is performed between the LNs using different routing protocols and the results are compared with original LEACH and HMGEAR protocols. The results show that the accuracy of GRU is 97%, LSTM is 96%, CNN is 92% and ANN is 90%. Throughput, delay and the death of the first node are computed for LEACH, LEACH with DL, LEACH with RMCV, HMGEAR, HMGEAR with DL and HMGEAR with RMCV. Moreover, Oyente is used to perform the formal security analysis of the designed smart contract. The analysis shows that blockchain network is resilient against vulnerabilities.
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Towards Secure and Intelligent Internet of Health Things: A Survey of Enabling Technologies and Applications
Umar Zaman
Electronics
With the growth of computing and communication technologies, the information processing paradigm of the healthcare environment is evolving. The patient information is stored electronically, making it convenient to store and retrieve patient information remotely when needed. However, evolving the healthcare systems into smart healthcare environments comes with challenges and additional pressures. Internet of Things (IoT) connects things, such as computing devices, through wired or wireless mediums to form a network. There are numerous security vulnerabilities and risks in the existing IoT-based systems due to the lack of intrinsic security technologies. For example, patient medical data, data privacy, data sharing, and convenience are considered imperative for collecting and storing electronic health records (EHR). However, the traditional IoT-based EHR systems cannot deal with these paradigms because of inconsistent security policies and data access structures. Blockchain (BC) techn...
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