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Multimedia data (video, audio, images) require storage space and transmission bandwidth when sent through social media networking. Despite rapid advances in the capabilities of digital communication systems, the high data size and data transfer bandwidth continue to exceed the capabilities of available technology, especially among social media users. The recent growth of multimedia-based web applications such as WhatsApp, Telegram, and Messenger has created a need for more efficient ways to compress media data. This is because the transmission speed of networks for multimedia data is relatively slow. In addition, there is a specific size for sending files via email or social networks, because much high-definition multimedia information can reach the Giga Byte size. Moreover, most smart cameras have high imaging resolution, which increases the bit rate of multimedia files of video, audio, and image. Therefore, the goal of data compression is to represent media (video, audio, images, etc.) as accurately as possible with the minimum number of bits (bit rate). Traditional data compression methods are complex for users. They require a high processing power for media data. This shows that most of the existing algorithms have loss in data during the process of compressing and decompressing data, with a high bitrate for media data (video, audio, and image). Therefore, this work describes a new method for media compression systems by discrete Hartley matrix (128) to get a high speed and low bit rate for compressing multimedia data. Finally, the results show that the proposed algorithm has a high-performance speed with a low bit rate for compression data, without losing any part of data (video, sound, and image). Furthermore, the majority of users of social media are satisfied with the data compression interactive system, with high performance and effectiveness in compressing multimedia data. This, in turn, will make it easier for users to easily send their files of video, audio, and images via social media networks. [ABSTRACT FROM AUTHOR]

With the escalation of multimedia data, cloud technology has played a very important role in its management with its promising computing and storage capabilities. Cloud computing leverages high scalability, resources, and on-demand services, which have a remarkable impact on assisting multimedia data storage. Meanwhile, any alteration in the outsourced multimedia data is a severe menace. Numerous researchers have focused on providing validation of integrity through the auditing mechanism. Regrettably, their mechanisms are focused on the traditional public-key cryptosystem and cannot withstand quantum attacks. Additionally, the centralised approach of completely relying on a third-party auditor (TPA) of existing mechanisms may lead to biased auditing results. Hence, employing the advantage of resistance against quantum attack and removing the liability of certificates with lattice cryptography and an identity-based cryptosystem, we have presented a new auditing framework by utilising blockchain technology. Blockchain technology is employed in order to track the activities of the cloud service provider and TPA, resulting less dependency on TPA. Security analysis portrays security of the framework under the random oracle model, which depends is based on the Shortest Integer Solution (SIS) problem of the lattices. Reduced computation, communication and storage overheads with efficient features and performance is observed for the proposed mechanism through analysis conducted with similar mechanisms. As a result, better operability is achieved by the proposed mechanism. [ABSTRACT FROM AUTHOR]

Multimedia data, encompassing images, videos, and audio, has become a cornerstone in various AI-driven applications, particularly in pattern recognition tasks. The increasing complexity and volume of multimedia data necessitate robust and scalable analytical approaches. Traditional pattern recognition techniques often struggle to effectively manage the high-dimensional and multimodal nature of multimedia data. This study addresses the challenge by leveraging linear regression for analyzing multimedia data to enhance AI-driven pattern recognition. The proposed method integrates linear regression models with feature extraction techniques to identify and map underlying patterns within the multimedia data. The process begins with preprocessing steps, including normalization and dimensionality reduction, to ensure data consistency and manage computational complexity. Subsequently, linear regression models are applied to establish relationships between the extracted features and predefined classes or labels. The model's performance is evaluated using precision, recall, and F1-score metrics. Experimental results on a benchmark multimedia dataset reveal that the proposed approach achieves an average accuracy of 92.4%, with a precision of 91.8% and a recall of 93.1%. These results outperform several state-of-the-art methods, demonstrating the model's efficacy in accurately recognizing patterns within diverse multimedia data. Furthermore, the model exhibits scalability, maintaining high performance even when applied to large-scale datasets, thus validating its potential for real-world AI applications. The study concludes that linear regression, when integrated with appropriate feature extraction and preprocessing techniques, offers a viable solution for enhancing AI-driven pattern recognition in multimedia data. [ABSTRACT FROM AUTHOR]

Massive teaching resources will cause serious teaching efficiency problems for online teaching, and traditional online teaching models are even inferior to traditional classroom teaching in terms of teaching effects. Based on this, this paper analyzes massive educational resources and builds a scalable computer interactive education system based on large-scale multimedia data analysis. Moreover, this paper sets the role of the system according to the actual teaching situation, and constructs the functional module of the system structure. In addition, this paper uses computer simulation technology to analyze interactive technology and make technical improvements to make interactive technology the core technology of the computer interactive education system, and get an extensible interactive education system based on the characteristics of network teaching. Then helps to monitor and access the performance of an interactive educational system. Furthermore, this paper designs an experiment to evaluate the performance of the computer interactive education system, which is mainly carried out from two aspects: interactive evaluation and teaching evaluation. From the experimental research results, we can see that this system can effectively improve the quality of teaching. [ABSTRACT FROM AUTHOR]

Traditional physical education teaching methods are facing increasingly severe challenges. This article studies how to use network technology more reasonably in college physical education teaching and better serve college physical education teaching. The purpose is to comprehensively analyze the requirements of physical education teaching and teaching management in universities, and to plan in detail the functions of the physical education teaching management system to better meet the needs of the development of physical education teaching in universities. Starting from the current development status of online teaching management platforms in Chinese universities, this paper conducts research on the existing problems of online teaching management platforms in higher physical education institutions. Using a questionnaire survey method, this study investigates and analyzes the current situation of online teaching platforms and course resources in 12 domestic higher sports colleges and universities. Starting from the actual situation of online education in physical education teaching in universities, a management platform model that organically combines online teaching platforms and digital teaching resource libraries is proposed. This study indicates that multimedia compression technology has important research value and broad application prospects in online physical education teaching. Through the application of this technology, the quality of online physical education teaching can be greatly improved, teaching costs can be reduced, and a more high-quality and efficient learning experience can be provided for students. [ABSTRACT FROM AUTHOR]

In this research, we introduce a novel open-source labeling tool, the Game-Based Assessment (GBA) Labeling Tool, specifically designed to address current challenges for data labeling in GBA scenarios. This web-based application facilitates the annotation of audio, video, and game event data, offering three different types of annotations – global, time instant, and time window annotations – to enhance accuracy in the labeling process. The tool also offers customizable labels and various types of visualizations to support different contexts and scenarios.

Sharing the data in the cloud environment may generate some loopholes and backdoor entries for intruders. In concern to the attributes of storage records, generally vertical and horizontal partitioning is used that can acquire resilient privacy strength. The paper portrays privacy perseverance hierarchy-oriented collaborative architecture to store data over the cloud. To improve privacy and not let attackers break through, an algorithm has been designed, which keeps the sensitive and non-sensitive records isolated by applying different properties such as cryptography & Anonymization series. It includes generalization, l-diversity & t-closeness methods. An archetype model in the cloud environment has been implemented for identifying the validity of the proposed algorithm and optimization of architecture. For the evaluation, the unification level has been incorporated into the progressive algorithm that reduces the time and increases speed for the data restructuring which is used in privacy perseverance architecture. Further, the findings incorporate generalized statistical study to identify the behavior of the properties used and the complexity analysis of the work has been presented. [ABSTRACT FROM AUTHOR]

With the evolution of the Internet and multimedia technologies, delving deep into multimedia data for predicting topic richness holds significant practical implications in public opinion monitoring and data discourse power competition. This study introduces an algorithm for predicting English topic richness based on the Transformer model, applied specifically to the Twitter platform. Initially, relevant data is organized and extracted following an analysis of Twitter's characteristics. Subsequently, a feature fusion approach is employed to mine, extract, and construct features from Twitter blogs and users, encompassing blog features, topic features, and user features, which are amalgamated into multimodal features. Lastly, the combined features undergo training and learning using the Transformer model. Through experimentation on the Twitter topic richness dataset, our algorithm achieves an accuracy of 82.3%, affirming the efficacy and superior performance of the proposed approach. [ABSTRACT FROM AUTHOR]

A trend reported by both academics and practitioners is that advertising on TV has become increasingly energetic. This study investigates the association between the energy level in ad content and consumers' ad-tuning tendency. Using a data set of over 27,000 TV commercials delivered to U.S. homes between 2015 and 2018, the authors first present a framework to algorithmically measure the energy level in ad content from the video of ads. This algorithm-based measure is then compared with human-perceived energy levels showing that the measure is related to the level of arousal stimulated by ad content. By relating the energy levels in ad content with the ad-tuning tendency using two empirical procedures, the authors document the following: overall, more energetic commercials are more likely to be tuned in or less likely to be avoided by viewers. The positive association between energy levels in ad content and ad tuning is statistically significant after controlling for placement and other aspects of commercials. However, the association varies across product categories and program genres. The main implication of this study is that advertisers should pay attention to components of ad content other than loudness, which has been regulated by law. [ABSTRACT FROM AUTHOR]

Suspicious Human activity recognition (SHAR) is crucial for improving surveillance and security systems by recognizing and reducing possible hazards in different situations. This study focuses on the task of precisely identifying potentially suspicious human behaviour by utilizing an innovative approach that harnesses advanced deep learning methods. Despite the abundance of research on the subject of SHAR, current methods frequently need to be revised with restricted levels of precision and efficiency. This research aims to address these constraints by presenting a thorough methodology for detecting and recognizing suspicious human activities. By rigorously collecting and preparing data, as well as training models, we aim to solve the issue of inaccurate and inefficient activity recognition in surveillance systems. By utilizing Convolutional Neural Networks (CNNs) and deep learning structures, such as the proposed time-distributed CNN model and Conv3D model, we attain notably enhanced accuracy rates of 90.14% and 88.23%, respectively, surpassing current research approaches. Moreover, the efficacy of our approach is illustrated by conducting prediction experiments on previously unreported test data and YouTube videos. Through the process of evaluating the trained models on unseen test data, we ascertain their accuracy and ability to apply learned knowledge to new situations. Moreover, the algorithms are utilized to predict dubious human conduct in a YouTube video, demonstrating their practical usefulness in real-life surveillance situations. The results of this study have important consequences for improving surveillance and security systems, allowing for better identification and reduction of possible dangers in various settings. Our methodology enhances the precision and effectiveness of SHAR, advancing the construction of more resilient and dependable surveillance systems and ultimately strengthening public safety and security.

This article proposes the use of deep learning in human-computer interaction and presents a new explainable hybrid framework for recommending relevant hashtags on a set of orpheline tweets, which are tweets with hashtags. The approach starts by determining the set of batches used in the convolution neural network based on frequent pattern mining solutions. The convolutional neural network is then applied to the set of batches of tweets to learn the hashtags of the tweets. An optimization strategy has been proposed to accurately perform the learning process by reducing the number of frequent patterns. Moreover, eXplainable AI is introduced for hashtag recommendations by analyzing the user preferences and understanding the different weights of the deep learning model used in the learning process. This is performed by learning the hyper-parameters of the deep architecture using the genetic algorithm. GPU computing is also investigated to achieve high speed and enable the execution of the overall framework in real time. Extensive experimental analysis has been performed to show that our methodology is useful on different collections of tweets. The experimental results clearly show the efficiency of our proposed approach compared to baseline approaches in terms of both runtime and accuracy. Thus, the proposed solution achieves an accuracy of 90% when analyzing complex Wikipedia data while the other algorithms did not achieve 85% when processing the same amount of data. [ABSTRACT FROM AUTHOR]

Over the past few years, blockchain technology has acquired good faith as a modern, secure distributed network in the form of a ledger to conduct and store records of transactions. Blockchain is an evolving technological innovation that can offer solutions to real-world problems of various domains such as cloud computing, the Internet of Things, supply chain, and multimedia. In multimedia, there is an increased requirement for security due to various threats, including replicating digital data without any information loss and manipulating the same without any detection. Moreover, the security of personal multimedia systems is perceived as one of the fundamental human rights concerns. Therefore, this paper proposed a blockchain-based distributed application to secure personal multimedia data such as images, text, videos, etc. The proposed work maintains personal multimedia data's security, privacy, and integrity to meet multimedia security requirements and address the above issues. The blockchain architecture is developed on Hyperledger Fabric, a permissioned blockchain structure using Hyperledger Composer, and stores multimedia data by utilizing SWARM decentralized storage system. The proposed architecture provides a cross-platform, low-cost, and energy-efficient blockchain prototype to manage and secure personal multimedia data. Many experimental tests are conducted to evaluate the performance of the proposed work based on various parameters such as transaction latency, asset latency, throughput, power consumption, upload, and download time for multimedia data. Finally, the proposed work's robustness, feasibility, and reliability are proved by comparing it with the existing works. Results and discussion show that the proposed scheme is more efficient than the existing schemes. [ABSTRACT FROM AUTHOR]

The use of augmented reality (AR) is the only meaningful way towards the creation and integration of information into a cultural (and not only) analogue leaflet, flyer and poster, which by their nature have limited informational material. The enrichment of information, which an analogue leaflet has, with additional descriptive information (text), multimedia (image, video, sound), 3-D models, etc., gives the end recipient/user a unique experience of approaching and understanding a music event, for example. The Digi-Orch is a research project that aims to create smart printed communication materials (e.g., posters, concert programme booklets) and an accompanying AR application to provide this enhanced audience experience to classical music lovers. The paper presents the ways in which AR can extend and enrich an analogue leaflet/flyer/poster, the features of and how to use the developed AR application, as well as the components, architecture and functions of the system. It also presents the different versions of the AR application within the project, starting with the laboratory versions, continuing with the pilot versions in real conditions (music events) and reaching the final prototype and the educational version of the AR application. [ABSTRACT FROM AUTHOR]

With the evolution of the Internet and multimedia technologies, delving deep into multimedia data for predicting topic richness holds significant practical implications in public opinion monitoring and data discourse power competition. This study introduces an algorithm for predicting English topic richness based on the Transformer model, applied specifically to the Twitter platform. Initially, relevant data is organized and extracted following an analysis of Twitter’s characteristics. Subsequently, a feature fusion approach is employed to mine, extract, and construct features from Twitter blogs and users, encompassing blog features, topic features, and user features, which are amalgamated into multimodal features. Lastly, the combined features undergo training and learning using the Transformer model. Through experimentation on the Twitter topic richness dataset, our algorithm achieves an accuracy of 82.3%, affirming the efficacy and superior performance of the proposed approach.

Cloud-based Healthcare 4.0 systems have research challenges with secure medical data processing, especially biomedical image processing with privacy protection. Medical records are generally text/numerical or multimedia. Multimedia data includes X-ray scans, Computed Tomography (CT) scans, Magnetic Resonance Imaging (MRI) scans, etc. Transferring biomedical multimedia data to medical authorities raises various security concerns. This paper proposes a one-of-a-kind blockchain-based secure biomedical image processing system that maintains anonymity. The integrated Healthcare 4.0 assisted multimedia image processing architecture includes an edge layer, fog computing layer, cloud storage layer, and blockchain layer. The edge layer collects and sends periodic medical information from the patient to the higher layer. The multimedia data from the edge layer is securely preserved in blockchain-assisted cloud storage through fog nodes using lightweight cryptography. Medical users then safely search such data for medical treatment or monitoring. Lightweight cryptographic procedures are proposed by employing Elliptic Curve Cryptography (ECC) with Elliptic Curve Diffie-Hellman (ECDH) and Elliptic Curve Digital Signature (ECDS) algorithm to secure biomedical image processing while maintaining privacy (ECDSA). The proposed technique is experimented with using publically available chest X-ray and CT images. The experimental results revealed that the proposed model shows higher computational efficiency (encryption and decryption time), Peak to Signal Noise Ratio (PSNR), and Meas Square Error (MSE). [ABSTRACT FROM AUTHOR]

In the field of data exchange, the security of information is the critical aspect. This void in the security helped the evolution of steganography to address the problem of security, especially when highly critical information exchange is the priority. Better and advanced level of security has been attained using the techniques of steganography with cryptography. However, it has been observed that steganographic techniques principled on computationally intelligent or inspired by nature algorithms are gaining much attention. The paper proposes a method for image steganography in transform domain using hybridization of two computationally intelligent algorithms, i.e., firefly optimization algorithm and ant colony optimization algorithm with Huffman encoding to address the gap of high payload. Comparative analysis of the proposed steganographic tool with other existing methods and analysis of the values obtained against some key performance terms like PSNR (peak signal to noise ratio), MSE (mean square error), BER (bit error rate) and SSI (structural similarity index) determines the proposed algorithm as a powerful method. The proposed tool achieves the PSNR of 68.06 dB and the embedding capacity of 985,682 bits. [ABSTRACT FROM AUTHOR]

The emergence of computer interactive teaching system greatly facilitates the needs of students to learn anytime and anywhere at this stage. At the same time, the increase in online course video, voice, files and other teaching resources also puts forward higher requirements for the operation of the teaching system. This article addresses the practical needs of online teaching and proposes a scalable computer interactive teaching system designed with the aid of large-scale multimedia data analysis algorithms. The system utilizes a principal component analysis linear data feature extraction algorithm to extract data features and implements data prediction through linear transformation methods for data reconstruction. This approach enables the system to effectively extract and analyze data, while also facilitating efficient data prediction for improved teaching outcomes. With its scalability and advanced data analysis capabilities, this system represents a promising solution for enhancing the effectiveness of online teaching and improving the overall learning experience for students. Most online course resources are displayed in the form of multimedia. Therefore, this paper uses robust component analysis to process multimedia data, especially for some problems such as data missing and multimedia data interference. This paper uses RC ranking-related evaluation method and R-value evaluation method to evaluate the prediction data, which proves that parameter selection plays an important role in controlling sparse regression. The algorithm proposed in this paper was tested using actual video and picture data related to the teaching system resources. The results indicated that the algorithm achieved the best data recognition performance when the number of theme models was set to 100. This finding provides an important reference for the system design. Finally, this paper designs a computer interactive teaching system which includes three modules: administrator, student and teacher, and tests the system, which proves the practical operation possibility and application value of the system. [ABSTRACT FROM AUTHOR]

With the development of multimedia technology and the popularity of smartphones, the collection of multimedia information from various applications has rapidly grown. This information encompasses text, images, video, and audio compositions. This multimedia information has not only transformed the way we communicate and share content but has also posed new challenges and opportunities in fields such as data analysis, content creation, and information management. Consequently, multimedia education has become the focal point for research institutions and businesses. The modern solution faces two main challenges: expensive uptime and comparative speed. Therefore, this study first introduces a washing method called Permutation (OPH) to reduce expensive working time. Furthermore, the study explores innovative techniques to enhance the comparative speed of the modern solution, ultimately aiming to achieve a more efficient and cost-effective system. With the OPH, a permutation wash (GOPH) has been developed, along with several useful techniques to address pressure changes. This development is based on the fact that most multimedia files differ from one another. A new hashing method, known as Hierarchical Permutation Hash (HOPH), has been created to enhance productivity. Extensive testing with real multimedia files clearly demonstrates that HOPH is five to seven times faster than Minis' while maintaining the same level of accuracy. HOPH achieves this remarkable speed improvement by efficiently leveraging hierarchical structures in data, making it a promising solution for a wide range of multimedia applications. [ABSTRACT FROM AUTHOR]

The rapid growth of multimedia data in various domains has necessitated the development of efficient techniques to enhance and optimize its quality. Traditional approaches often struggle to address the complexity and diversity of multimedia data, leading to suboptimal results. This paper presents a novel approach to tackle this challenge by leveraging the power of deep reinforcement learning (DRL). The proposed method utilizes DRL to learn and optimize multimedia data in an improvised manner. By employing a combination of convolutional neural networks and deep Q-networks, the model can effectively extract high-level features and make informed decisions to enhance the quality of multimedia data. The reinforcement learning framework enables the system to learn from its actions, continuously improving its performance through an iterative process. To evaluate the effectiveness of the proposed method, extensive experiments were conducted using a diverse set of multimedia datasets. The results demonstrate significant improvements in various quality metrics, including image resolution, video frame rate, and audio clarity. Additionally, the proposed approach exhibits robustness across different types of multimedia data, ensuring consistent enhancement performance across various domains. Furthermore, the computational efficiency of the proposed method is also highlighted, as it demonstrates faster convergence and lower computational overhead compared to traditional optimization methods. This makes the approach practical for real-time applications where multimedia data needs to be processed efficiently. Overall, this paper introduces an innovative framework that combines deep reinforcement learning with multimedia data optimization. The results indicate its potential for enhancing multimedia data quality, offering a promising solution to the challenges associated with traditional approaches. The proposed method not only improves the visual and auditory aspects of multimedia content but also provides a scalable and efficient solution for real-world applications in domains such as image processing, video streaming, and audio analysis. [ABSTRACT FROM AUTHOR]

Most of the existing techniques to product discovery and recommendations rely on syntactic approaches, thus ignoring valuable and specific semantic information of the underlying standards during the process. The product data comes from different heterogeneous sources and formats (text and multimedia) giving rise to the problem of interoperability. Above all, due to the continuously increasing influx of data, the manual labeling is getting costlier. Integrating the descriptions of different products into a single representation requires organizing all the products across vendors in a single taxonomy. Practically relevant and quality product categorization standards are still limited in number; and that too in academic research projects where we can majorly see only prototypes as compared to industry. This work presents a cost-effective aggregator semantic web portal for product catalogues on the Data Web as a digital marketplace. The proposed architecture creates a knowledge graph of available products through the ETL (Extract-Transform-Load)) approach and stores the resulting RDF serializations in the Jena triple store. User input textual and multimedia specifications for certain products are matched against the available product categories to recommend matching products with price comparison across the vendors. The experimental results show that semantic intelligence technologies could provide the necessary data integration and interoperability for efficient product/service discovery including multimedia. [ABSTRACT FROM AUTHOR]

Most of the time, these classifiers are trained using general-purpose datasets with a lot of classes. Therefore, the performance of these classifiers may not be as good as it could be. Both choosing classifiers based on registrations and dividing them into groups based on the subjects they cover are possible solutions that could lead to better classifier performance. This makes it clear that a classifier division and selection strategy needs for the proposed optimization to work. With the help of this method, the proposed model for feature extraction can choose an appropriate classifier while taking subscription constraints into account. There are subscriptions with the best values of n, and the results of using only n-class classifiers from one domain and ignoring classes from other domains are also given. These are in the same place as the effects of only using n-class classifiers from a certain domain. In this article, these are talked about in the same context as what happens when you only use n-class classifiers from a certain domain. For high-performance use of SAE-based systems, you need to use a classifier selection technique. This method is also needed for the investigation of multimedia events that need the method. To establish the effectiveness of the multimedia event-based system as well as its dependability, we are making use of traditional evaluation methods such as throughput and accuracy. These measures include the following: When compared to the efficiency of the system when using a classifier with a single class, the efficiency of the system diminishes as the number of classes per classifier increases. This is the case regardless of the other measures. This is the situation about both the throughput and the precision of the operation. [ABSTRACT FROM AUTHOR]

With the prosperity of Industry 4.0, numerous emerging industries continue to gain popularity and their market scales are expanding ceaselessly. The Internet of Vehicles (IoV), one of the thriving intelligent industries, enjoys bright development prospects. However, at the same time, the reliability and availability of IoV applications are confronted with two major bottlenecks of time delay and energy consumption. To make matters worse, massive heterogeneous and multi-dimensional multimedia data generated on the IoV present a huge obstacle to effective data analysis. Fortunately, the advent of edge computing technology enables tasks to be offloaded to edge servers, which significantly reduces total overhead of IoV systems. Deep reinforcement learning (DRL), equipped with its excellent perception and decision-making capability, is undoubtedly a dominant technology to solve task offloading problems. In this article, we first employ an optimized Fuzzy C-means algorithm to cluster vehicles and other edge devices according to their respective service quality requirements. Then, we employ an election algorithm to assist in maintaining the stability of the IoV. Last, we propose a task-offloading algorithm based on the Deep Q Network (DQN) to acquire an optimal task offloading scheme. Massive simulation experiments demonstrate the superiority of our method in minimizing time delay and energy consumption. [ABSTRACT FROM AUTHOR]

With the vigorous development of Internet technology and the popularization of smart devices, while the amount of multimedia data exploding, their forms become increasingly diverse. People's demand for information is no longer satisfied with single-modal data retrieval. Realizing cross-modal retrieval through knowledge collaboration of different modalities has become a research hotspot in recent years. On the basis of in-depth understanding and analysis of the research background and progress of cross-modal retrieval, with the key technology of cross-modal retrieval, public subspace modeling as the main line, this paper analyzes three types of methods of cross-modal retrieval technology: traditional statistical analysis, deep learning, and Hash learning. This paper conducts a comprehensive and multi-angle comparative analysis on the research content, key technology, limitations, applicability and characteristics from different angles, and experiments are done for more in-depth comparisons. Finally, the difficulties to be solved in cross-modal retrieval, future exploration directions, mainstream design ideas and development trends in recent years are fully prospected to provide a theoretical basis for further research.

In this modern era, depression is one of the most prevalent mental disorders from which millions of individuals are affected today. The symptoms of depression are heterogeneous and often coincide with other disorders such as bipolar disorder, Parkinson's, schizophrenia, etc. It is a serious mental illness that may lead to other health problems if left untreated. Currently, identifying individuals with depression is totally based on the expertise of the clinician's experience. In order to assist clinicians in identifying the characteristics and classifying depressed people, different types of data modalities and machine learning techniques have been incorporated by researchers in this field. This study aims to find the answers to some important questions related to the trend of publications, data modality, machine learning models, dataset usage, pre-processing techniques and feature extraction and selection techniques that are prevalent and guide the direction of future research on depression diagnosis. This systematic review was conducted using a broad range of articles from two major databases: IEEE Xplore and PubMed. Studies ranging from the years 2011 to April 2021 were retrieved from the databases resulting in a total of 590 articles (53 articles from the IEEE Xplore database and 537 articles from the PubMed database). Out of those, the articles which satisfied the defined inclusion criteria were investigated for further analysis. A total of 135 articles were identified and analysed for this review. High growth in the number of publications has been observed in recent years. Furthermore, significant diversity in the use of data modalities and machine learning classifiers has also been noted in this study. fMRI data with an SVM classifier was found to be the most popular choice among researchers. In most of the studies, data scarcity and small sample size, particularly for neuroimaging data are major concerns. The use of identical data pre-processing tools for similar data modalities can be seen. This study also provides statistical analysis of the current framework with respect to the modality, machine learning classifier, sample size and accuracy by applying one-way ANOVA and the Tukey – Kramer test. The results indicate that an effective fusion of machine learning techniques with a potential data modality has a promising future for assisting clinicians in automatic depression diagnosis. [ABSTRACT FROM AUTHOR]

Recent developments of semiconductor and communication technologies have resulted in the interconnection of numerous devices in offering seamless communication and services, which is termed as Internet of Everything (IoE). It is a subset of Internet of Things (IoT) which finds helpful in several applications namely smart city, smart home, precise agriculture, healthcare, logistics, etc. Despite the benefits of IoE, it is limited to processing and storage abilities, resulting in the degradation of device safety, privacy, and efficiency. Security and privacy become major concerns in the transmission of multimedia data over the IoE network. Encryption and image steganography is considered effective solutions to accomplish secure data transmission in the IoE environment. For resolving the limitations of the existing works, this article proposes an optimal multikey homomorphic encryption with steganography approach for multimedia security (OMKHES-MS) technique in the IoE environment. Primarily, singular value decomposition (SVD) model is applied for the separation of cover images into RGB elements. Besides, optimum pixel selection process is carried out using coyote optimization algorithm (COA). At the same time, the encryption of secret images is performed using poor and rich optimization (PRO) with multikey homomorphic encryption (MKHE) technique. Finally, the cipher image is embedded into the chosen pixel values of the cover image to generate stego image. For assessing the better outcomes of the OMKHES-MS model, a wide range of experiments were carried out. The extensive comparative analysis reported the supremacy of the proposed model over the rennet approaches interms of different measures. [ABSTRACT FROM AUTHOR]

Summary: Several geographic routing protocols have been proposed for data transmission in wireless multimedia sensor networks (WMSNs), in which forwarding decisions are made locally based on one‐hop neighborhood information of each sensor node. These routing protocols involve one of two routing strategies: distance‐based strategy (greedy) which selects the closest neighbor from the base station as next‐hop or direction‐based strategy (compass) that chooses the neighbor with the smallest angle deviation toward the base station as next‐hop. This article proposes a multipath geographic routing protocol for WMSNs that combines the two routing strategies while taking advantage of their benefits, called greedy‐compass geographic multipath routing (GCGM) protocol. In GCGM, for the selection of a neighbor as next‐hop, a weighting coefficient is associated to each of the two strategies. We performed extensive simulations using OMNeT++ simulator with different scenarios to illustrate the performance of our proposal with our proposed weight values and with various weight values. Simulation results showed that our proposal with the proposed weight values provides better performance compared to GCGM with fixed weight values in terms of several performance criteria. We also compared GCGM with other routing protocols. Simulation results showed that GCGM provides better performance compared to TIGMR, LQEAR, and AGEM. [ABSTRACT FROM AUTHOR]

An artificial pancreases (AP) is a device for managing diabetes through automated insulin infusion. The control algorithm is the heart of the AP that computes the insulin infusion based on blood glucose measurements. In this article, we investigate the role of multimedia data to enable the advanced control techniques that could personalize AP in elderly type 2 diabetes patients. The performance is evaluated through in silico studies on a patient simulator wherein the patient model is computed based on the data collected from clinical studies. [ABSTRACT FROM AUTHOR]

Early classification on time series has emerged as an active research area in the field of machine learning. It covers a wide range of applications in agriculture, medical and multimedia systems, including drought prediction, health monitoring, event detection, and many more. The early classification aims to predict the class label of a time series as soon as possible without waiting for the complete series. A critical issue in early classification is the learning of decision policy that determines the adequacy of the collected data required for reliable class prediction. It is more challenging for Multivariate Time Series (MTS) data, where the decision depends on multiple variables to achieve a trade-off between earliness and accuracy. Therefore, this work proposes an optimization-based early classification model for MTS data based on optimal decision rule learning. The proposed model adopts a two-layered approach. The first layer employs the Gaussian process probabilistic classifiers for each variable in MTS that provides the class probabilities at the successive time steps in the series. The second layer defines Early Stopping Rule (ESR) that performs the class prediction task. The ESR learns its parameters through the particle swarm optimization by simultaneously minimizing the misclassification cost and delaying the decision cost. This work has utilized publicly available MTS datasets to validate the proposed early classification model. The experimental results show that the proposed model achieves promising results in terms of accuracy and earliness compared to existing methods. [ABSTRACT FROM AUTHOR]

One of the essential concerns of Internet of Things (IoT) is in industrial systems or data architecture to support the evolutions in transportation and logistics. Considering the Industrial IoT (IIoT) openness, the need for accessibility, availability, and searching of data has rapidly increased. The primary purpose of this research is to propose an Efficient Two-Dimensional Filter (ETDF) to store multimedia data of IIoT applications in a specific format to achieve faster response and dynamic updating. This filter consists of a two-dimensional array and a hash function integrated into a cuckoo filter for efficient use of memory. This study evaluates the scalability of the filter by increasing the number of requests from 10,000 to 100,000. To assess the performance of the proposed filter, we measure the parameters of access time and lookup message latency. The results show that the proposed filter improves the access time by 12%, compared to a Fast Two-Dimensional Filter (FTDF). Moreover, it improves memory usage by 20% compared to FTDF. Experiments indicate a better access time of the proposed filter compared to other filters (i.e., Bloom, quotient, cuckoo, and FTD filters). Insertion and deletion times are essential parameters in comparing filters, so they are also analyzed.

With the emergence of cyber technology, the biggest evolution has been observed in the use of Internet for financial purposes, in particular for the Internet banking sector. However, with the increase in the number of Internet banking users, many security issues have been discovered. In the recent past, there have been many successful cyber-attacks on the Internet banking services (IBS) throughout the world. There exists a huge amount of various data about the security of the banking systems, however, proper analysis of such data using various learning techniques is needed for security assessment. In this research work, we aim to assess the security of IBS by developing a framework based on deep analysis of big data (available in various formats) and the existing security requirements of the country. We propose a framework consisting of 93 data categories to assess the security of the IBS. We evaluate our proposed approach on a case study consisting of the banks providing IBS in Pakistan. A total of 21 Pakistani banks providing Internet banking services are analyzed thoroughly using our proposed framework. The result uncovered many deficiencies in the Internet banking services of the analyzed banks. All these deficiencies are conveyed to the respective banks for verification and helping them to take corrective measures. In addition, a comprehensive set of security recommendations is developed for the banks, their customers and the regularity authority for improving Internet banking security. [ABSTRACT FROM AUTHOR]

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Abstract Process of information extraction (IE) is used to extract useful information from unstructured or semi-structured data. Big data arise new challenges for IE techniques with the rapid growth of multifaceted also called as multidimensional unstructured data. Traditional IE systems are inefficient to deal with this huge deluge of unstructured big data. The volume and variety of big data demand to improve the computational capabilities of these IE systems. It is necessary to understand the competency and limitations of the existing IE techniques related to data pre-processing, data extraction and transformation, and representations for huge volumes of multidimensional unstructured data. Numerous studies have been conducted on IE, addressing the challenges and issues for different data types such as text, image, audio and video. Very limited consolidated research work have been conducted to investigate the task-dependent and task-independent limitations of IE covering all data types in a single study. This research work address this limitation and present a systematic literature review of state-of-the-art techniques for a variety of big data, consolidating all data types. Recent challenges of IE are also identified and summarized. Potential solutions are proposed giving future research directions in big data IE. The research is significant in terms of recent trends and challenges related to big data analytics. The outcome of the research and recommendations will help to improve the big data analytics by making it more productive.

The wide-scale deployments and interconnections of massive diverse sensors in Internet of Things (IoT) enable the mapping of multimedia entity state data from the physical world into the cyberspace. Sensor search technologies further facilitate the access to multimedia entity state data. However, existing sensor search mechanisms overlook the search requirements for historical state information on entities. Besides, previous current state based sensor search mechanisms realize the low-cost sensor search through predicting the sensor current state based on shallow learning theory, which leads to limited prediction accuracy and higher communication overhead. Aiming at these shortages, an efficient dual-mode sensor search mechanism is firstly designed, including the current state based and historical state based search methods, to fulfill the search needs for current and historical state information on entities. Then based on deep learning theory, a high-accuracy data prediction method towards sensor current state is presented to improve the insufficient accuracy of existing prediction methods. Moreover, a lightweight data representation method is devised to fit the sensor historical state, thereby accomplishing space-saving and low computational overhead historical state based sensor search. Simulation results show that the proposed sensor search mechanism combined with the designed prediction method and approximate representation method can effectively enhance the communication overhead, recall ratio and precision ratio performances.