<abstract>
<title style='display:none'>Abstract</title>
Complex data systems are incapable of processing large data volumes, rendering the task of retrieving pertinent information unattainable. The advent of the Internet has amplified the significance of accessible and readily available information. Additionally, it receives support from the World Wide Web Consortium (W3C) and global organizations responsible for establishing web standards, such as Web Ontology, Inc. It expands the functionality of the website to facilitate the retrieval, integration, and transmission of information. In recent years, several major organizations have shown a strong inclination towards using semantic technologies for the purpose of collecting Big Data. Undoubtedly, there are other advantages of integrating this into the Creative. It enhances the ability of end-users to manage data from many repositories, focuses on changing the corporate environment and the user experience, and incorporates individual definitions and integrates several data sources. Furthermore, the market’s evolving expectations and contemporary organizational practices require an adaptable but all-encompassing information strategy. Integration of data warehousing may be achieved by the use of scattered corporate ontologies. This study explores the impact of the Semantic Web on enhancing the intelligence of Big Data. It analyses the obstacles and opportunities associated with the integration of Big Data with the Semantic Web.
</abstract>

Orchestrating Distributed Computing and Web Technology with Semantic Web and Big Data

<abstract>
<title style='display:none'>Abstract</title>
Computational Intelligence (CI) is a sub-field of Artificial Intelligence (AI) that concentrates on the development of intelligent techniques and models to solve complex problems. When it comes to data analytics, CI techniques can be highly effective in handling large volumes of data, extracting meaningful patterns, and making accurate predictions. An increasing number of online reviews were being posted on the Internet every day with the rapid growth of Electronic Commerce (E-Commerce) and social networks. In the fastest growing research area, Sentiment Analysis (SA) is one among them that helps consumers in making better decisions relating to purchases through proper analysis and understanding of shared sentiments from social media and the web. In recent times, several approaches were modelled for acquiring insights from such datasets. But still, there comes a problem in managing text of large size; hence, precise polarity recognition of consumer reviews was an exciting and ongoing issue. This article introduces a novel Sentiment Analysis on Product Reviews using Enhanced Grasshopper Optimization with Deep Learning (SAPR-EGODL) approach. The objective of the SAPR-EGODL approach lies in the identification and classification of different sentiment types that exist in product reviews. At the initial stage, data preprocessing takes place which transforms the product review data into meaningful data. Next, the SAPREGODL technique employs Multi-Head Attention-based Bidirectional Long Short Term (MHABLSTM) technique for sentiment classification. In this study, the EGO model is exploited for improving the classification accomplishment of the MHABLSTM technique. A sequence of simulations was accomplished on different datasets for examining the advanced sentiment classification results of the SAPR-EGODL technique. The comprehensive relative research exhibited the promising accomplishment of the SAPR-EGODL model compared to current techniques.
</abstract>

Computational Intelligence-based Data Analytics for Sentiment Classification on Product Reviews

<abstract>
<title style='display:none'>Abstract</title>
The versatility of Wireless Sensor Networks (WSNs) and other unique qualities inspired by real-time collaboration between the sensor nodes make them widely used for a range of applications in monitoring and surveillance. As solutions for malicious node identification involve a single, centralized decision-making process, security in WSNs is becoming more and more important. Errors are difficult to prevent with this paradigm, and repeatability and traceability are problems. Therefore, the detecting ability and integrity of the developed detection method cannot be guaranteed by malicious node identification technology in ordinary WSNs. This article provides insights into this innovative idea as well as a thorough investigation of the use of blockchain technology to WSNs (Blockchain-Based WSN), including a full analysis of a blockchain-based method for identifying rogue nodes. The Blockchain-Based WSN architecture, business-specific applications, and use are all included in this study. The Blockchain-Based WSN architecture model for tracking malicious nodes and the smart contract features of malicious node detection is both included in the explanation of harmful node identification based on Blockchain-Based WSN in this paper. The second part of this study investigates the use of blockchain technology to the management of WSN data, which necessitates online information aggregation and may also require auditing, event logging, and storage for information analysis and offline query processing. The traditional WSN data management approaches are discussed first in this study, followed by the blockchain-based WSN data management choices. The benefaction of blockchain to WSN security management is also included in this survey. Before discussing blockchain-based WSN security management solutions, such as those that provide access control, protect data integrity, ensure anonymity, and prolong the life of WSN nodes, it first looks at the security vulnerabilities of centralized WSN models.
</abstract>

Blockchain Based Wireless Sensor Networks for Detecting Nodes

<abstract>
<title style='display:none'>Abstract</title>
Cyberbullying has become a ubiquitous menace in our digitally connected society, requiring strong detection and classification systems. This study presents a multi-tiered system that reliably detects and classifies instances of cyberbullying on a variety of platforms by utilising cutting-edge machine learning and natural language processing approaches. Our algorithm, which was trained on a wide range of datasets, shows excellent accuracy in differentiating between instances of cyberbullying and non-bullying situations while taking linguistic and cultural quirks into account. Furthermore, our flexible system guarantees applicability by adjusting to changing cyberbullying patterns. By promoting safer and more inclusive digital communities, our research helps to design proactive treatments that lessen the effects of online harassment. This study introduces a robust multi-tiered system designed for the detection and classification of cyberbullying across diverse digital platforms. Leveraging state-of-the-art machine learning and natural language processing techniques, our algorithm, trained on extensive datasets, exhibits exceptional accuracy in distinguishing cyberbullying instances from non-bullying scenarios while accommodating linguistic and cultural nuances. The system’s adaptability to evolving cyberbullying patterns ensures continued efficacy. By fostering safer and more inclusive online environments, our research contributes to proactive measures and mitigates the impact of digital harassment.
</abstract>

ML and Natural Language Processing : Cyberbullying Detection System for Safer and Culturally Adaptive Digital Communities

<abstract>
<title style='display:none'>Abstract</title>
Handwriting Recognition (HWR) is a difficult and varied discipline having applications in a wide range of fields, including banking, education, and administration. This research investigates the two main types of HWR systems: online and offline character recognition. Online HWR entails real-time input utilizing digital pens to capture dynamic handwriting traits. It’s used in contemporary gadgets like tablet computers and for signature verification. Offline HWR, on the other hand, processes scanned documents, making it important in situations such as bank cheque processing and assisting the visually handicapped. The research emphasizes the continuing potential for progress in HWR, notably using machine learning and deep learning approaches. Machine learning, a subset of Artificial Intelligence (AI), is critical in developing character recognition algorithms. The selection of an effective classification model is a vital decision, and the study uses a specific dataset to conduct a comparison analysis of alternative models to help in this process. Such assessments provide useful insights for academics and practitioners, allowing them to make more informed judgements on model development for HWR applications.
</abstract>

Enhancing Handwritten Alphabet Prediction with Real-time IoT Sensor Integration in Machine Learning for Image

<abstract>
<title style='display:none'>Abstract</title>
Communication networks are constantly increasing in size and complexity. Hence, the traditional rule-based algorithms of these networks will probably not operate at their most effective efficiency. Machine learning (ML) is being used these days to solve tough problems in a variety of industries, including banking, healthcare, and enterprise. Communication network performance can be improved using computational models that can deliver ML algorithms. This paper investigates the use of ML models in communication networks for prediction, intruder detection, route and path allocation, quality of service enhancement, and resource management. A review of the current literature suggests that there is a wealth of potential for researchers to leverage ML to solve challenging network performance problems, especially in the development of software-based networks and 5G.
</abstract>

Applications of Machine Learning in Mobile Networking

<abstract>
<title style='display:none'>Abstract</title>
In the current technological environment, the Internet of Things (IoT) has arisen as a significant subject of research because of its importance. A robust cloud computing framework is established via the collaboration of sensors and actuators. This framework has the potential to be used in the creation of autonomous object networks in real-time systems. The Internet of Things (IoT) is playing a significant role in the redefinition of other significant ideas, such as urbanism and smart cities. The Internet of Things (IoT) technology has the potential to improve the quality of life for inhabitants of smart cities, which are relatively new developments. This might be accomplished by providing people with enhanced facilities and services. The Internet of Things is seeing a fast expansion in a variety of sectors, including smart cities, everyday life, and other fields. Through the use of home automation, smart wearables, security solutions, and smart healthcare, the Internet of Things makes it possible to create intelligent environments. In the realm of the Internet of Things, some of the possible topics of study include intelligent transportation systems for cities and intelligent electrical systems. This article offers a succinct summary of the most recent advancements in the Internet of Things (IoT) and the influence those advancements have had on the lives of humans in their day-to-day activities. In addition to this, the Internet of Things (IoT) is also considered in connection to artificial intelligence, autonomous control, and cloud computing. It is important to synchronise a number of technologies, such as wireless sensors, actuators, the internet, and distributed computing, in order to successfully allow upcoming Internet of Things technologies.
</abstract>

Web-based Efficiency of Distributed Systems and IoT on Functionality of Smart City Applications

<abstract>
<title style='display:none'>Abstract</title>
Intelligent traffic congestion control is an integral aspect of making sustainable and efficient smart cities. With the increasing count of vehicles on the road and rapid urbanization, traffic congestion is a main concern nowadays that hinders the growth of the economy and affects the quality of life. In smart cities, an intelligent transportation solution (ITS) is enhance traffic flow by adjusting traffic signal timing and observing traffic patterns. Currently, one of the vital dilemmas in terms of transportation systems was traffic congestion which needs to be resolved for minimizing driver frustration, traffic jams, fuel waste, and accidents. Due to the high count of vehicles, most of the traffic interruptions in metropolitan cities arise. With the advancements in Artificial Intelligence (AI) and Machine Learning (ML), smart environments monitored in smart cities observe the influencing issues of the environment correctly, with the best control of traffic congestion, pollution, and other negative effects. Therefore, this study presents an intelligent traffic congestion control using Black Widow Optimization with Hybrid Deep Learning (ITC-BWOHDL) technique in Smart City Environment. The main aim of the ITC-BWOHDL technique is to utilize feature subset selection with parameter-tuning strategies for effective traffic congestion management. To obtain this, the ITC-BWOHDL technique primarily designs the emperor penguin optimizer-based feature selection (EPO-FS) approach for selecting a useful set of features. For the detection of traffic congestion, the ITCBWOHDL technique makes use of the HDL model which incorporates convolutional neural network (CNN) with gated recurrent unit (GRU) approach. To improve the classification results of the HDL model, the BWO-based hyperparameter tuning process gets executed. For exhibiting the improved classification outcome of the ITC-BWOHDL system, a comprehensive range of experiments was executed. The obtained outcome described the betterment of ITC-BWOHDL method over other existing techniques.
</abstract>

Intelligent Traffic Congestion Control Using Black Widow Optimization with Hybrid Deep Learning on Smart City Environment

<abstract>
<title style='display:none'>Abstract</title>
In this paper, we deploy the public-safety network which is overlapped with 5th-gerneration (5G) commercial network. Note that 5G commercial networks is assumed to operate on 700 MHz frequency band to observe the effect on public-safety network. Our main focus is to evaluate the interference on public-safety user equipment (UEs) (which are distributed outside the tunnel) from the 5G base stations (gNBs) (which are deployed inside the tunnel). Considering the aforementioned scenario, we compare it with the coexisting scenario of public-safety, and railway networks. In second scenario, we deploy the railway eNBs without tunnel and evaluate the co-channel interference on public-safety UEs. Radio access network sharing is an important candidate to consider for coexisting next-generation wireless communication networks. For the coexisting LTE networks (public safety verses railway) and 5G commercial network, which are assumed to utilize the same spectrum, active radio access network sharing should be considered. In this chapter, we evaluate the co-channel interference on two types of scenarios, i.e., (i) coexisting public-safety and railway networks, (ii) coexisting public-safety and 5G commercial networks. Moreover, we compare these scenarios by considering the radio access network sharing and non-radio access network sharing by public-safety UEs to evaluate the co-channel interference. The system-level simulations are performed to evaluate the UE average throughput, UE received interference, and UE outage probability. By activating the radio access network sharing, some improvement in throughput, and reduction in interference and outage performances, can be achieved. Simulation results validates the effectiveness of radio access network sharing for coexisting public-safety networks and 5G commercial networks.
</abstract>

Co-channel Interference Evaluation for Coexisting Public-Safety Networks and 5G Commercial Network

<abstract>
<title style='display:none'>Abstract</title>
Utilising the dispersed resources that are accessible inside a cluster, the dispersed Deep Learning System (DDLS) is able to successfully complete the process of training complex neural network models. This is accomplished by utilising the resources to their full potential. As a consequence of this, the system is able to get insights about neural network models that are becoming more complex. Individuals who work as engineers for distributed deep learning systems are needed to make a variety of decisions in order to properly manage their specialised job within the environment of their choice. This is necessary in order to ensure that the job is efficiently managed. Achieving this is very necessary in order to guarantee that they will be able to carry out their obligations. Throughout the course of the last several years, deep learning programmes have uncovered significant applications in a broad range of different industries. A few examples of these domains include image recognition, natural language processing, semantic understanding, financial analysis, and aided healthcare. These are only few of the topics that fall under this category. All of these factors have led to a significant growth in the amount of information that is being used in each and every application. The continued penetration of information into a number of different sectors, as well as the increasing complexity of computations and the restrictions of models, have all contributed to this. Because of this, there has been a significant increase in the quantity of information that is being employed on a worldwide scale for various purposes.
</abstract>

Modular Platforms based on Clouded Web Technology and Distributed Deep Learning Systems

<abstract>
<title style='display:none'>Abstract</title>
People with disabilities have new and advanced methods to communicate with the applications for virtual keyboards and other communication tools. In this paper, we utilized a novel deep reinforcement learning-based technique for determining the location of the accessible options for gaze-controlled tree-based menu selection system. A virtual English keyboard has been incorporated into the layout of the new user interface, which also includes improved requests for text modification through the gaze. The two methods used to manage the system are: 1) eye tracking for typing on the virtual keyboard and 2) eye tracking with a device for soft-switch utilizing deep reinforcement learning. Simulation results show that DRL based algorithm outperforms other baseline techniques in terms of total loss and accuracy.
</abstract>

Gaze Tracking for Hands-Free Human Using Deep Reinforcement Learning Approach

<abstract>
<title style='display:none'>Abstract</title>
Intelligent manufacturing system incorporates a number of sensors including IoT devices, cameras, and scanners, for capturing real-time data about the manufacturing process. Based on their physical properties, colours, dimensions, or other relevant characteristics, these sensors can be used to track and identify waste objects. Waste object classification in intelligent manufacturing includes the usage of recent systems and technologies to detect and classify waste materials or objects produced during the manufacturing process. The objective is to enable effective waste management and recycling practices, optimizing resource utilization and reducing environmental impact. Manual waste classification is a laborious and expensive task, which results in the development of automatic waste classification models using artificial intelligence (AI) techniques. It remains a challenging process due to the significant variations in the solid waste because of varying shapes, colours, and sizes. Therefore, recent advances in deep learning (DL) methods can be employed to accomplish the solid waste classification process. The study introduces a chaotic African vulture optimization algorithm with a deep learning-based solid waste classification (CAVOA-DLSWC) system. The CAVOA-DLSWC technique aims to automatically detect waste objects and classify them into different categories using DL models. In the presented CAVOADLSWC approach, two major processes are involved such as object classification and detection. For the object detection method, the CAVOA-DLSWC technique uses a lightweight RetinaNet model with CAVOA based hyperparameter tuning process. The CAVOA is derived by integrating the chaotic concepts into the initial iteration values of the AVOA. Once the waste objects are identified, the classification process can be performed by the use of convolutional long short-term memory (CLSTM) network. The experimental values of the CAVOA-DLSWC approach can be tested employing the solid waste database including diverse kinds of waste objects. The comparative results show the remarkable performance of the CAVOA-DLSWC method over other techniques.
</abstract>

Modelling of African Vulture Optimization Algorithm with Deep Learning-based Object Classification for Intelligent Manufacturing Systems

<abstract>
<title style='display:none'>Abstract</title>
Due to growing concerns regarding their use in fields including oceanography, commercial marine operations, and military surveillance, demand in the exploration of underwater sensor networks for marine studies has developed. Network channels for underwater sensor network (USN) rapidly change (spatially and temporally) depending on the surroundings. To increase system efficiency by adjusting transmission parameters to channel fluctuations, it is alluring to utilize adaptive modulation and coding (AMC) for USNs. In order to determine the best link adaptation method based on the channel quality, this article focuses on evaluating a measured sea trial dataset utilizing a rule-based approach (i.e., three-dimensional evaluation, modulation-wise analysis, and a fixed-SNR strategy). To determine the optimum AMC combinations in terms of channel adaptively, we draw a situation of the measured USN data rate versus Bit Error Rate (BER) and Signal to Noise Ratio (SNR). The work further extends to apply machine learning (ML) methods to identify the MCS levels by looking into the channel characteristics due to the non-reversibility limitation of the rule-based strategy. One of the ML methods we used for the investigation, gradient boosted regression tree (GBRT), exhibits impressive accuracy of 99.988% in classifying MCS levels. The MCS levels are related to channel statistics and signal characteristics, particularly those that are susceptible to SNR and BER limitations, using an ensemble of trees that learns from the buoy and base station’s uplink data.
</abstract>

Link Adaptation Strategy for Underwater Acoustic Sensor Networks: A Machine Learning Approach

<abstract>
<title style='display:none'>Abstract</title>
Academics and businesses are very interested in using artificial intelligence (AI) and machine learning (ML) to solve the most challenging problems in a variety of fields. This white paper provides a brief overview of key applications of Artificial Intelligence and Machine Learning in SDN- and NFV-based networks. According to their application history, we categorize the most important developments in this field into different groups and identify the relevant AI methods used. It also covers key challenges and the domain’s future course. We highlight the critical role AI/ML can play in opening the door to self-configuring, self-adapting as well as self-managing networks, but research is inadequate due to the difficulties found in this domain.
</abstract>

Application of Artificial Intelligence and Machine Learning in Software Defined Networks

<abstract>
<title style='display:none'>Abstract</title>
Due to their limited frequency range and fast fading channels, underwater sensor networks (USNs) are vulnerable to collisions of packets. In this paper, we propose a deep reinforcement learning-based relay selection scheme with shortest latency (DRL-SL) for USNs that enables to choose the relay based on the state that comprised of the bit error rate (BER) of the previous transmission, and the jamming power measured by the relay node. The DRL-SL-based relay selection scheme completed in two phases. In the first phase, a deep neural network based learning is performed and second phase is the real-time interaction with the underwater sensor network. Numerical results give the bound on how efficiently the system performs in terms of bit error rate, energy use, and node utility. According to the numerical results, the proposed DRL-SL based relay selection scheme can enhance relay performance in comparison to the benchmark underwater relay techniques.
</abstract>

A Deep Reinforcement-Learning-Based Relay Selection for Underwater Sensors Network

<abstract>
<title style='display:none'>Abstract</title>
This entire system stands upon blockchain technology. The system is initially broken down in sections like different modules for patient, doctor, pharmacy and insurance agent. It is completed utilising a personal blockchain from Corda and Ethereum called Ganache as well as a private blockchain. This supports the development, testing, and general deployment of dApps. An Ethereum development framework is called Truffle Suite. Completely control the lifetime of modern contracts that includes bespoke installations, linking with different libraries and intricate applications on Ethereum. In dApps, Meta Mask is usefull for account creation, login and bitcoin transcations. The storage of all huge files and documents is done using the IPFS technology. Because IPFS is a P2P network circuit for decentralised data storage and the produced hash is simple to store on the blockchain, it is used. ReactJS was used to build the system’s front end. The Remix online compiler for Solidity is used to build and test smart and modern contracts that are written in the Solidity blockchain programming language.
</abstract>

Design and Development of Healthcare System Using Blockchain

<abstract>
<title style='display:none'>Abstract</title>
Two basic strategies have developed by network designers to deal including errors. by adding extra information to data before sending. such approach is to comprise sufficient extra information to make destination to infer what the sent data should be, or it possible to add only sufficient extra information to let destination to infer that errors have been occurred, however not which error then have it ask re-send defected data. Previous strategy utilizes errors correcting mechanism and latter uses errors detecting mechanism. Utilize of errors correcting mechanism is named as Forward Error Correction (FEC). In this thesis, sliding window has been implemented over Multi-Protocol Label Switching (MPLS). Therefore, MPLS send data from one node to other node on network depend on short path labels instead of logical addresses in network layer, in addition to that MPLS is connection oriented compared to IP network. Finally, we have study and analyze sliding technique performance over MPLS from delay, throughput and packet loss point of view. It is show that go back N has belter performance compared to selective repeat in term of throughput.
</abstract>

Effect of Sliding Windows Technique on the Performance of TCP/IP Networks

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Journal of Smart Internet of Things

IoT and Intelligent Technologies

Journal of Smart Internet of Things (JSIoT) is an international double-blind peer-reviewed open-access scientific journal published as a quarterly by Future Science and scientifically...

Journal of Smart Internet of Things (JSIoT) is an international double-blind peer-reviewed open-access scientific journal published as a quarterly...