Chronological Concept Research Articles

Hybrid optimization based deep stacked autoencoder for routing and intrusion detection

This research introduced the optimized Deep Stacked Autoencoder (DSA) for performing Intrusion Detection (ID) in the IoT. Firstly, IoT simulation is carried out and then, the information is routed by using the Chronological War Strategy Optimization (CWSO). Here, the CWSO is newly designed by incorporating the chronological concept with the WSO. After the routing, the ID is completed at the Base station (BS) by executing the following steps. Initially, data is obtained from a database, after that, feature normalization is done using min-max normalization. Meanwhile, Canberra distance is applied to execute the feature selection process. Finally, ID is performed using DSA, which is trained using the Competitive Swarm Henry War Strategy Optimization algorithm (CSHWO). The experimental result confirms that the invented scheme accomplished the superior outcome by the energy, f-score, precision, and recall values of 0.379, 0.913, 0.918 and 0.912, respectively.

Stakeholder-engaged research: a multidisciplinary historical analysis

The involvement of stakeholders in research – from design, production and communication to use – is recognised as essential to producing impactful research. However, approaches to involving stakeholders in research vary greatly between different fields. This article conducts a multidisciplinary historical analysis of stakeholder-engaged research to offer an integrated perspective on engagement practices across disciplines. It identifies common influences, objectives, trends and challenges, proposing frameworks to support interdisciplinary analysis and understanding. The analysis identifies interconnected approaches that could benefit from mutual learning and exchange. Approaches emerging from sociopolitical movements orient around objectives of empowerment and emancipation, those responding to complexity tend to focus on producing better research that solves societal problems, while accountability-driven approaches centre on research use and impact. This article broadens the diversity of approaches and epistemologies discussed in the literature by including disciplines neglected in previous reviews. Building on the widely cited three-generations model, it proposes a configuration- and complexity-based framework for comparing stakeholder-engaged research approaches, moving beyond the chronological conception of stakeholder-engaged research development. This multidisciplinary historical analysis provides a valuable resource for scholars and practitioners to understand the range of possible approaches to stakeholder engagement, facilitating approach selection for specific use cases and contexts, as well as opportunities for interdisciplinary learning and collaboration.

Ukrainian art studies of the 20th century: ways of development and main problems of research.

It has been investigated that Ukrainian art history in its historical development has followed a path characteristic of European artistic thought. The tasks faced by Ukrainian art history at the beginning of the 20th century are considered. During 1905–1917, art history in Ukraine acquired signs of structure, the directions of research were quite clearly defined. Based on the analysis of art studies of the 1920s, it was determined that they are characterized by a combination of clear retrospective vision, methodically correct handling of iconographic sources. The fact that the problem of national peculiarities of Ukrainian art history, its origins, driving factors, and priorities is of fundamental importance has been revealed. The article states that the Soviet totalitarian regime initiated a new stage of the «state» approach to the development of national inversions of science, literature and, in particular, art history. It was found out that specific conclusions regarding the national style were removed from many works of domestic art researchers by censorship. The need to organize and harmonize with the national historical concept of chronology and periodization of Ukrainian art history remains an equally urgent task. In this regard, the publication is supplemented with tables with a chronology of cultural events and publications of art-historical publications. It was found that Ukrainian art history actively developed in the context of the general national revival. With the beginning of totalitarian repressions and the liquidation of literary and artistic associations in the Ukrainian SSR (1932), the development process moved to western Ukrainian lands, primarily to Lviv. It was noted that in the post-war period, after well-known socio-political and socio-cultural changes, art history, art criticism, as well as artistic creativity throughout the territory of Ukraine, were united under one ideological slogan of socialist realism. It is emphasized that an important role in this difficult period was played by Ukrainian figures of culture, science and art of the diaspora. It is revealed that Ukrainian art history of the second half of the 20th century developed in difficult socio-political and cultural conditions. It has been found that the second half of the 20th century, marked by the repeated "revival" of the socialist realist method, nevertheless proved the continuity of the development of the national concept in the field of art history. A new stage logically began during the years of independent Ukraine. It is highlighted that the national concept of the historical development of Ukrainian culture and art has become the leading one in the domestic humanitarian field. It is important that the revival of Ukrainian art criticism has begun.

Chronological golden search optimization-based deep learning for classification of heartbeat using ECG signal

ABSTRACT Electrocardiogram (ECG) is the simplest test, used for checking the heart’s rhythm and measuring the heart’s electrical activity. This method is the most probably used technique for identifying heart diseases because of its non-invasive nature. The patient’s status is identified by handling the irregularities in the ECG signals. For computation in the existing system, they use signal processing methods which result in time consumption in real time. In existing system, it is hard to predict the detection of heartbeat early and accurately. In this work, the heartbeat is classified using ECG signal by proposed Chronological Golden Search Optimisation (CGSO)-based Deep Learning (DL). Here, the CGSO algorithm is developed by hybridising the Chronological concept with the Golden search optimisation (GSO) algorithm, which is utilised to train SqueezeNet. After the acquisition of ECG signals, pre-processing is done by a median filter. Then, the features are extracted, and this tends to the data augmentation process. Finally, the heartbeat is classified using SqueezeNet-enabled CGSO. The performance is analysed using ECG heartbeat categorisation data set. The proposed model obtained a specificity of 93.5%, sensitivity of 93.1% and precision of 89.8%. From the results, it is known that the proposed system offers more accurate results.

Chronological horse herd optimization-based gene selection with deep learning towards survival prediction using PAN-Cancer gene-expression data

Cancer has always been one of the major hazards to human life which is also the most difficult part of human disease history. The death rate due to cancer is high. The prediction results are affected because of the major dissimilarities present in clinical results. Hence, it is necessary to enhance the accuracy of cancer survival prediction, which remains a challenging one. To defeat the challenges, this research devises a robust approach, named Deep Recurrent Neural Network-based Chronological Horse Herd Political Optimization (DRNN-based CHHPO) for survival prediction. Here, the gene selection is performed using the proposed Chronological Horse Optimization (CHO) by assuming the parameters of fitness, for example Minkowski distance plus Renyi entropy. The Horse Herd Optimization (HOA) and Chronological concept is merged to form the CHO. With the selected genes, the gene features are strengthened using technical indicators to enhance the overall process. Finally, survival prediction is completed by means of DRNN, which is trained by the CHHPO, which is the amalgamation of Political optimizer (PO) and CHO. Superior presentation with the Prediction Error (PE) and minimal Root Mean Square Error (RMSE) of 0.456 and 0.467 is accomplished by this developed technique.

Hybrid Optimization Algorithm Enabled Deep Learning Approach Brain Tumor Segmentation and Classification Using MRI.

The unnatural and uncontrolled increase of brain cells is called brain tumors, leading to human health danger. Magnetic resonance imaging (MRI) is widely applied for classifying and detecting brain tumors, due to its better resolution. In general, medical specialists require more details regarding the size, type, and changes in small lesions for effective classification. The timely and exact diagnosis plays a major role in the efficient treatment of patients. Therefore, in this research, an efficient hybrid optimization algorithm is implemented for brain tumor segmentation and classification. The convolutional neural network (CNN) features are extracted to perform a better classification. The classification is performed by considering the extracted features as the input of the deep residual network (DRN), in which the training is performed using the proposed chronological Jaya honey badger algorithm (CJHBA). The proposed CJHBA is the integration of the Jaya algorithm, honey badger algorithm (HBA), and chronological concept. The performance is evaluated using the BRATS 2018 and Figshare datasets, in which the maximum accuracy, sensitivity, and specificity are attained using the BRATS dataset with values 0.9210, 0.9313, and 0.9284, respectively.

IPFS based storage Authentication and access control model with optimization enabled deep learning for intrusion detection

Network security has benefited from intrusion detection, which may spot unexpected threats from network traffic. Modern methods for detecting network anomalies typically rely on conventional machine learning models. The human construction of traffic features that these systems mainly rely on, which is no longer relevant in the age of big data, results in relatively low accuracy and certain exceptional features. A storage authentication and access control model based on Interplanetary File System (IPFS) and a network intrusion detection system based on Chronological Anticorona Virus Optimization are hence the main goals of this research (CACVO-based DRN).The setup, user registration, initialization, data encryption and storage, authentication, testing, access control, and decryption stages are used here to perform the blockchain authentication and access control. After then, DRN is used to perform network intrusion detection. To do this, the recorded data log file is initially sent to the feature fusion module, which uses Deep Belief Network and hybrid correlation factors (DBN). After the feature fusion is complete, the proposed optimization technique, CACVO, which was recently developed by fusing the Chronological Concept with Anti Corona virus Optimization (ACVO) algorithm, is used to perform intrusion detection utilizing DRN. The experimental outcome shows that, based on the f-measure value of 0.939 and 0.938, respectively, the developed model achieved greater performance.

IoT enabled lung cancer detection and routing algorithm using CBSOA‐based ShCNN

SummaryThe Internet of Things (IoT) has tremendously spread worldwide, and it influenced the world through easy connectivity, interoperability, and interconnectivity using IoT devices. Numerous techniques have been developed using IoT‐enabled health care systems for cancer detection, but some limitations exist in transmitting the health data to the cloud. The limitations can be accomplished using the proposed chronological‐based social optimization algorithm (CBSOA) that effectively transmits the patient's health data using IoT network, thereby detecting lung cancer in an effective way. Initially, nodes in the IoT network are simulated such that patient's health data are collected, and for transmission of such data, routing is performed in order to transmit the health data from source to destination through a gateway based on cloud service using CBSOA. The fitness is newly modeled by assuming the factors like energy, distance, trust, delay, and link quality. Finally, lung cancer detection is carried out at the destination point. At the destination point, the acquired input data is fed to preprocessing phase to make the data acceptable for further mechanism using data normalization. Once the feature selection is done using Canberra distance, then the lung cancer detection is performed using shepard convolutional neural network (ShCNN). The process of routing as well as training of ShCNN is performed using the CBSOA algorithm, which is devised by the inclusion of the chronological concept into the social optimization algorithm. The proposed approach has achieved a maximum accuracy of 0.940, maximum sensitivity of 0.941, maximum specificity of 0.928, and minimum energy of 0.452.

Deep Maxout Network for human action and abnormality detection using Chronological Poor and Rich Optimization

ABSTRACT Human activity recognition targets to discriminate the various human actions depending on the size, shape and posture exhibited by the humans while performing a particular action. Video-based human action detection easily understands the actions and behaviours of humans in the video sequences. Therefore, it has a part in various applications, like human-machine interaction and healthcare domains. However, human activity recognition in a complex environment leads to poor performance and low accuracy, and it remains a challenging task in human action recognition using video sequences. To overcome such limitations, a Chronological Poor and Rich Optimisation (CPRO)-based Deep Maxout Network is developed for effective human movement and abnormality recognition. The CPRO is devised by integrating the chronological concept into Poor and Rich Optimisation. In the feature extraction process, the features include Histogram of Oriented Gradients, Local Gradient Pattern and hierarchical skeleton are digging out. Moreover, human action and abnormality detection are done utilising Deep Maxout Network trained by the proposed CPRO. The proposed scheme reveals superior results with respect to evaluation measures, such as accuracy, sensitivity and specificity of 0.954, 0.958 and 0.960, respectively.

8th Grade Students’ Perceptions Towards the Concept of Chronology in Social Studies Curriculum

Bu araştırmanın amacı, Sosyal Bilgiler Dersi 5., 6. ve 7. Sınıf Öğretim Programında yer alan ve geliştirme-pekiştirme düzeyinde öğrencilere verilen “kronoloji” kavramının sekizinci sınıf öğrencileri tarafından nasıl algılandığını incelemektir. Olgu bilim deseninde gerçekleştirilen araştırmada doküman analizi, mecazlar yoluyla veri toplama formları ve yarı yapılandırılmış görüşme birlikte kullanılmıştır. Verilerin çözümlenmesinde ve yorumlanmasında içerik analizi kullanılmıştır. Araştırmanın çalışma grubu, maksimum çeşitlilik örnekleme yöntemiyle seçilen 693 sekizinci sınıf öğrencisinden oluşmaktadır. Araştırma sonucunda; alt, orta ve üst gelişmişlik düzey farkı gözetilmeksizin katılımcıların büyük çoğunluğu kronoloji kavramını “önemli tarihi bilgileri sıralayıcı, kolaylaştırıcı, tarihi film şeridi, tarihin haritası”, “belli bir düzende tasarımlanan, netleştiren ve anlamayı kolaylaştıran, önemli bilgiler içeren bir tarih takvimi” ve “tarihi anlamayı ve öğrenmeyi kolaylaştıran bir rehber, önemli tarihi bilgilerin kısa bir özeti ve önemli tarihlerin çekilmiş bir fotoğrafı” olarak algılarken orta ve üst gelişmişlik düzeydeki katılımcıların “tarihi olayların izlediği süreci gösteren, dallanıp budaklanabilen bir tarih ağacı” olarak algıladıkları görülmüştür.

Optimal block search mechanism using deep recurrent neural network for enabling the code-efficiency in HEVC

Purpose High-efficiency video coding (HEVC) is the latest video coding standard that has better coding efficiency than the H.264/advanced video coding (AVC) standard. The purpose of this paper is to design and develop an effective block search mechanism for the video compression-HEVC standard such that the developed compression standard is applied for the communication applications. Design/methodology/approach In the proposed method, an rate-distortion (RD) trade-off, named regressive RD trade-off is used based on the conditional autoregressive value at risk (CaViar) model. The motion estimation (ME) is based on the new block search mechanism, which is developed with the modification in the Ordered Tree-based Hex-Octagon (OrTHO)-search algorithm along with the chronological Salp swarm algorithm (SSA) based on deep recurrent neural network (deepRNN) for optimally deciding the shape of search, search length of the tree and dimension. The chronological SSA is developed by integrating the chronological concept in SSA, which is used for training the deep RNN for ME. Findings The competing methods used for the comparative analysis of the proposed OrTHO-search based RD + chronological-salp swarm algorithm (RD + C-SSA) based deep RNN are support vector machine (SVM), fast encoding framework, wavefront-based high parallel (WHP) and OrTHO-search based RD method. The proposed video compression method obtained a maximum peak signal-to-noise ratio (PSNR) of 42.9180 dB and a maximum structural similarity index measure (SSIM) of 0.9827. Originality/value In this research, an effective block search mechanism was developed with the modification in the OrTHO-search algorithm along with the chronological SSA based on deepRNN for the video compression-HEVC standard.

Chronological Sailfish Optimizer for Preserving Privacy in Cloud Based on Khatri-Rao Product

Abstract The innovative trends of cloud computing acquired the interest of several individuals or enterprises that started outsourcing data to the cloud servers. Recently, numerous techniques are introduced for facilitating privacy protection on untrusted cloud platforms. However, the classical privacy-preserving techniques failed to prevent leakage and incur huge information loss. This paper introduces the efficient technique, named the chronological sailfish optimizer (CSFO) algorithm for privacy preservation in cloud computing. The proposed CSFO is devised by integrating the chronological concept in SailFish optimizer. The input data are fed to a privacy-preservation process wherein hamming weight-based RSA and Khatri-Rao products are utilized for data privacy. Here, the hamming weighted-based RSA is determined by combining the sha256 algorithm with the hamming weight with Rivest–Shamir–Adleman (HRSA) system. Hence, an optimization-driven algorithm is utilized to evaluate optimal matrix generation to handle both the utility and the sensitive information. Here, the fitness function is newly devised considering, realism, privacy and fitness. The experimentation is performed using four datasets, like Pathway Interaction Database, Hungarian, Cleveland and Switzerland. The proposed CSFO provided superior performance with maximal privacy of 0.2173, maximal realism 0.9456 and maximal fitness of 0.5416.

Chronological salp swarm algorithm based deep belief network for intrusion detection in cloud using fuzzy entropy

AbstractCloud computing is susceptible to the existing information technology attacks, as it extends and uses the traditional operating system, information technology infrastructure, and applications. However, in addition to the existing threats, the cloud computing environment faces various security issues in detecting anomalous network behaviors. In order to resolve the security issues, an effective intrusion detection system named Chronological Salp Swarm Algorithm‐based Deep Belief Network is proposed for detecting the suspicious intrusions in a cloud environment. Accordingly, the proposed Chronological Salp Swarm Algorithm‐based Deep Belief Network is developed by integrating the Chronological concept with the Salp Swarm Algorithm. The optimal solution for detecting the intrusion is revealed using the fitness function, which accepts the minimal error vale as the optimum solution. Here, the weights are optimally tuned by the proposed algorithm to generate an effective and optimal solution for detecting the intruders. The proposed Chronological Salp Swarm Algorithm‐based Deep Belief Network obtained better performance through the facility of exploitation and the exploration in search space. The performance of the proposed method is analyzed using two datasets, namely KDD cup dataset and BoT‐IoT dataset, the comparative analysis is performed with the existing methods, such as Host based intrusion detection system, Deep learning, and Deep Neural Network + Genetic Algorithm. The proposed Chronological Salp Swarm Algorithm‐based Deep Belief Network obtained better performance in terms of accuracy, sensitivity, and specificity, with the values of 0.9618%, 0.9702%, and 0.9307% using KDD cup dataset, and 0.9764%, 0.9824%, and 0.9309% using BoT‐IoT dataset.

Cultural and Economic Type and Archaeological Appearance of the Cimmerians (a Brief Historiographical Review)

The article discusses various opinions related to the solution of the “Cimmerian problem”, set out in the Russian-language scientific literature in the period of the XX-beginning of the XXI centuries. Special attention is paid to the historiography of the question of the cultural and economic type of the Cimmerians and the approach of various researchers to its definition. The author also considers the question of the material culture of the Cimmerians and the archaeological sites associated with them, their interpretations and various hypotheses proposed at different times by researchers of the archeology of the Early Iron Age, chronological concepts. The author supports the hypothesis of T.M. Kuznetsova about the insufficient argumentation of the statement about the Cimmerians-nomads and the hypothesis of V.R. Erlikh about the identification with the Cimmerians of archaeological sites of the classical Novocherkassk stage of the pre-Scythian period, which is characterized by a peculiar and massive complex of horse equipment and weapons of soldiers-riders. In a wide chronological range, these sites can be dated no earlier than the last quarter of the 8th, and probably no later than the middle of the 7th century BC. The horizon of the few monuments of the “Jabotinsky type” in the south of Eastern Europe, which does not have a local substrate, is considered to belong to the early Scythians no earlier than the end of the first quarter of the 7th century, before the beginning of their cam-paigns in Transcaucasia, the countries of Near Asia in the 7th century BC.

Chronological penguin Adam-based deep long short-term memory classifier for stock market prediction

This paper proposes an effective method named chronological penguin Adam-based deep long short-term memory (CPAdam-based deep LSTM) to forecast the future prices in the stock market. Initially, the technical indicators are extracted from the stock market input data, and the wrapper approach is used to select the suitable features. Then, the deep long short-term memory (deep LSTM) classifier performs the stock market prediction based on the memory cell-associated, which acts as an accumulator to store the state information. The deep LSTM classifier is trained by the proposed CPAdam algorithm, which is developed by integrating the features of the chronological concept, PeSOA, and Adam optimisation algorithm. The experimentation is performed using two datasets, namely Reliance Communications, and Relaxo Footwear, in which the proposed CPAdam-based deep LSTM offers better performance using dataset 2 in terms of the metrics, like MSE, prediction accuracy, RMSE, return-per trade, and winning rate.

Chronological penguin Adam-based deep long short-term memory classifier for stock market prediction

This paper proposes an effective method named chronological penguin Adam-based deep long short-term memory (CPAdam-based deep LSTM) to forecast the future prices in the stock market. Initially, the technical indicators are extracted from the stock market input data, and the wrapper approach is used to select the suitable features. Then, the deep long short-term memory (deep LSTM) classifier performs the stock market prediction based on the memory cell-associated, which acts as an accumulator to store the state information. The deep LSTM classifier is trained by the proposed CPAdam algorithm, which is developed by integrating the features of the chronological concept, PeSOA, and Adam optimisation algorithm. The experimentation is performed using two datasets, namely Reliance Communications, and Relaxo Footwear, in which the proposed CPAdam-based deep LSTM offers better performance using dataset 2 in terms of the metrics, like MSE, prediction accuracy, RMSE, return-per trade, and winning rate.

A Fragment from Chronicle of George Synkellos in Slavonic Translation

The only fragment from the Chronicle of George Synkellos in Slavic translation is found in a chronographic compilation known in five Russian witnesses of the 15th – 16th cc. A large and coherent excerpt from the Chronography of Julius Africanus that survived in about 100 fragments scattered in Latin, Greek and Eastern traditions became a basis of the compilation. Africanus’ excerpt reveals the Christian history of the world from the Creation to the Resurrection of Christ and occupies about two thirds of the whole text. It is complemented by the end of Synkellos’ Chronicle that stops with Diocletian’s reign and by the beginning of the Chronicle of his follower Theophanes the Confessor, which brings the narrative to the foundation of Constantinople. The missionary pathos of the compilation leaves no doubt and makes us think that it occurred on Byzantine soil in the first half of the 9th c. after the end of the iconoclasm. The Linguistic features of the Slavonic text prove that the translation was made in Bulgaria in the early 10th century during the reign of Simeon the Great (893–927). The paper explores the traces of the editorial work of the compilers, who were supposed to bring into line the two historical narratives that disagree in their historical and chronological concepts and refer to different sources. The problem deserves attention given the fact that in the beginning of the last century V. Istrin erroneously identified the compilation as an abridged and even draft version of the Chronicle of Synkellos.

Chronological and exponential based Lion optimisation for optimal resource allocation in cloud

Cloud computing is a service-oriented architecture, which has a pool of resources. This paper introduces the resource allocation scheme for cloud computing by introducing an optimisation algorithm named Chronological E-Lion algorithm. The proposed Chronological E-Lion algorithm is developed by integrating the chronological concept to the EWMA-based Lion algorithm. The fitness function of the proposed Chronological E-Lion algorithm considers various parameters, like cost, profit, CPU allocation, memory allocation, MIPS, and frequency scaling, for optimally allocating the resources. The performance of the proposed method is analysed using three different problem instances based on the evaluation metrics, profit, CPU allocation rate, and memory allocation rate. From the simulation results, it can be concluded that the proposed Chronological E-Lion algorithm achieved improved performance with the values of 45.93, 0.16, and 0.0093, for profit, CPU utilisation rate, and memory utilisation rate.

Nucleus and cytoplasm-based segmentation and actor-critic neural network for acute lymphocytic leukaemia detection in single cell blood smear images.

Acute lymphoblastic leukaemia (ALL), which is due to the malfunctioning in the bone marrow, is common among people all over the world. The haematologist suffers a lot to discriminate the presence of leukaemia in the patients using the blood smears. To overcome the inaccuracy and reliability issues, this paper proposes an automatic method of leukaemia detection, named chronological Sine Cosine Algorithm-based actor-critic neural network (Chrono-SCA-ACNN). Initially, the blood smear images are segmented using the proposed entropy-based hybrid model, from which the image-level features and statistical features are extracted from the segments. Then, the selected features are applied to the proposed classifier, which detects the leukaemia. In the proposed Chrono-SCA-ACNN, the optimal weights are selected by the proposed Chrono-SCA, which is the integration of the chronological concept in the SCA. Finally, the experimentation is performed using the ALL-IDB2 database, and the effectiveness of the proposed method over the existing methods is evaluated. From the analysis, the accuracy of the proposed method is found to be 0.99, which proves that it outperforms the existing classification methodologies. Graphical abstract Block diagram of proposed Leukaemia detection. The main aim of the paper is to segment and classify the WBCs for ALL detection in single cell blood smear images. Initially, the blood smear image is subjected to pre-processing in order to enhance the quality of the input image so as to make it effective for the further processes associated with Leukaemia detection. The pre-processed image is applied to the segmentation process that segments the cytoplasm and nucleus using the Entropy-based hybrid model. The entropy-based hybrid model is developed using the FCM and active contour to segment the cytoplasm and nucleus that is fused using the entropy. The segments are subjected to the feature extraction that extracts the statistical features and the color histogram-based features from the segments. The features are presented to the Actor-Critic Neural Network and the weights of the Neural Network (NN) are optimally tuned using the proposed Chrono-SCA. The block diagram of the proposed method of leukaemia detection is depicted in Fig. 1.

Optimization based interesting region identification for video watermarking

The advancements in multimedia watermarking technologies have gained lots of attention in the past few years. A digital video watermarking is the data embedded in the video and sent to the recipient. Privacy is considered as the major issue in digital media while hiding the data by maintaining the video quality. This paper presents an optimization algorithm for video watermarking based on interesting regions. Here, the optimal regions for the video watermarking are selected using the proposed Chronological-Moth Search (Chronological-MS) approach, which is established by modifying the Moth Search (MS) algorithm using chronological concept. The employed fitness function contains cost function, which uses several parameters, like energy, edge, pixel intensity, brightness, and coverage. At first, the input video file is subjected to feature extraction, and the extracted features are employed for selecting the optimal regions using the proposed Chronological-MS. After that, the wavelet transform is given to the original image to obtain the wavelet coefficients. Here, the privacy message is divided into binary images using bit plane technique. Thus, the embedding process is carried out for hiding the secret message with the use of interesting regions identified using the proposed Chronological-MS algorithm and is retrieved at the extraction phase. The performance of the proposed Chronological-MS shows superior performance with correlation coefficient and PSNR of 1.00 and 98.741 dB, respectively.