Secure biometric systems based on bio-signals and DNA encryption of optical spectrograms.

Recently, biometrics has become widely used in applications to verify an individual's identity. To address security issues, biometrics presents an intriguing window of opportunity to enhance the usability and security of the Internet of Things (IoT) and other systems. It can be used to secure a variety of newly emerging IoT devices. However, biometric scenarios need more protection against different hacking attempts. Various solutions are introduced to secure biometrics. Cryptosystems, cancelable biometrics, and hybrid systems are efficient solutions for template protection. The new trend in biometric authentication systems is to use bio-signals. In this paper, two proposed authentication systems are introduced based on bio-signals. One of them is unimodal, while the other is multimodal. Protected templates are obtained depending on encryption. The deoxyribonucleic acid (DNA) encryption is implemented on the obtained optical spectrograms of bio-signals. The authentication process relies on the DNA sensitivity to variations in the initial values. In the multimodal system, the singular value decomposition (SVD) algorithm is implemented to merge bio-signals. Different evaluation metrics are used to assess the performance of the proposed systems. Simulation results prove the high accuracy and efficiency of the proposed systems as the equal error rate (EER) value is close to 0 and the area under the receiver operator characteristic curve (AROC) is close to 1. The false accept rate (FAR), false reject rate (FRR), and decidability (D) are also estimated with acceptable results of 1.6 × 10-8, 9.05 × 10-6, and 29.34, respectively. Simulation results indicate the performance stability of the proposed systems in the presence of different levels of noise.

E2E-RDS: Efficient End-to-End Ransomware Detection System Based on Static-Based ML and Vision-Based DL Approaches.

Nowadays, ransomware is considered one of the most critical cyber-malware categories. In recent years various malware detection and classification approaches have been proposed to analyze and explore malicious software precisely. Malware originators implement innovative techniques to bypass existing security solutions. This paper introduces an efficient End-to-End Ransomware Detection System (E2E-RDS) that comprehensively utilizes existing Ransomware Detection (RD) approaches. E2E-RDS considers reverse engineering the ransomware code to parse its features and extract the important ones for prediction purposes, as in the case of static-based RD. Moreover, E2E-RDS can keep the ransomware in its executable format, convert it to an image, and then analyze it, as in the case of vision-based RD. In the static-based RD approach, the extracted features are forwarded to eight various ML models to test their detection efficiency. In the vision-based RD approach, the binary executable files of the benign and ransomware apps are converted into a 2D visual (color and gray) images. Then, these images are forwarded to 19 different Convolutional Neural Network (CNN) models while exploiting the substantial advantages of Fine-Tuning (FT) and Transfer Learning (TL) processes to differentiate ransomware apps from benign apps. The main benefit of the vision-based approach is that it can efficiently detect and identify ransomware with high accuracy without using data augmentation or complicated feature extraction processes. Extensive simulations and performance analyses using various evaluation metrics for the proposed E2E-RDS were investigated using a newly collected balanced dataset that composes 500 benign and 500 ransomware apps. The obtained outcomes demonstrate that the static-based RD approach using the AB (Ada Boost) model achieved high classification accuracy compared to other examined ML models, which reached 97%. While the vision-based RD approach achieved high classification accuracy, reaching 99.5% for the FT ResNet50 CNN model. It is declared that the vision-based RD approach is more cost-effective, powerful, and efficient in detecting ransomware than the static-based RD approach by avoiding feature engineering processes. Overall, E2E-RDS is a versatile solution for end-to-end ransomware detection that has proven its high efficiency from computational and accuracy perspectives, making it a promising solution for real-time ransomware detection in various systems.

Vision Transformers in Image Restoration: A Survey.

The Vision Transformer (ViT) architecture has been remarkably successful in image restoration. For a while, Convolutional Neural Networks (CNN) predominated in most computer vision tasks. Now, both CNN and ViT are efficient approaches that demonstrate powerful capabilities to restore a better version of an image given in a low-quality format. In this study, the efficiency of ViT in image restoration is studied extensively. The ViT architectures are classified for every task of image restoration. Seven image restoration tasks are considered: Image Super-Resolution, Image Denoising, General Image Enhancement, JPEG Compression Artifact Reduction, Image Deblurring, Removing Adverse Weather Conditions, and Image Dehazing. The outcomes, the advantages, the limitations, and the possible areas for future research are detailed. Overall, it is noted that incorporating ViT in the new architectures for image restoration is becoming a rule. This is due to some advantages compared to CNN, such as better efficiency, especially when more data are fed to the network, robustness in feature extraction, and a better feature learning approach that sees better the variances and characteristics of the input. Nevertheless, some drawbacks exist, such as the need for more data to show the benefits of ViT over CNN, the increased computational cost due to the complexity of the self-attention block, a more challenging training process, and the lack of interpretability. These drawbacks represent the future research direction that should be targeted to increase the efficiency of ViT in the image restoration domain.

Impact of Fireworks Industry Safety Measures and Prevention Management System on Human Error Mitigation Using a Machine Learning Approach.

In the fireworks industry (FI), many accidents and explosions frequently happen due to human error (HE). Human factors (HFs) always play a dynamic role in the incidence of accidents in workplace environments. Preventing HE is a main challenge for safety and precautions in the FI. Clarifying the relationship between HFs can help in identifying the correlation between unsafe behaviors and influential factors in hazardous chemical warehouse accidents. This paper aims to investigate the impact of HFs that contribute to HE, which has caused FI disasters, explosions, and incidents in the past. This paper investigates why and how HEs contribute to the most severe accidents that occur while storing and using hazardous chemicals. The impact of fireworks and match industry disasters has motivated the planning of mitigation in this proposal. This analysis used machine learning (ML) and recommends an expert system (ES). There were many significant correlations between individual behaviors and the chance of HE to occur. This paper proposes an ML-based prediction model for fireworks and match work industries in Sivakasi, Tamil Nadu. For this study analysis, the questionnaire responses are reviewed for accuracy and coded from 500 participants from the fireworks and match industries in Tamil Nadu who were chosen to fill out a questionnaire. The Chief Inspectorate of Factories in Chennai and the Training Centre for Industrial Safety and Health in Sivakasi, Tamil Nadu, India, significantly contributed to the collection of accident datasets for the FI in Tamil Nadu, India. The data are analyzed and presented in the following categories based on this study's objectives: the effect of physical, psychological, and organizational factors. The output implemented by comparing ML models, support vector machine (SVM), random forest (RF), and Naïve Bayes (NB) accuracy is 86.45%, 91.6%, and 92.1%, respectively. Extreme Gradient Boosting (XGBoost) has the optimal classification accuracy of 94.41% of ML models. This research aims to create a new ES to mitigate HE risks in the fireworks and match work industries. The proposed ES reduces HE risk and improves workplace safety in unsafe, uncertain workplaces. Proper safety management systems (SMS) can prevent deaths and injuries such as fires and explosions.

Cancelable biometric system for IoT applications based on optical double random phase encoding.

The security issue is essential in the Internet-of-Things (IoT) environment. Biometrics play an important role in securing the emerging IoT devices, especially IoT robots. Biometric identification is an interesting candidate to improve IoT usability and security. To access and control sensitive environments like IoT, passwords are not recommended for high security levels. Biometrics can be used instead, but more protection is needed to store original biometrics away from invaders. This paper presents a cancelable multimodal biometric recognition system based on encryption algorithms and watermarking. Both voice-print and facial images are used as individual biometrics. Double Random Phase Encoding (DRPE) and chaotic Baker map are utilized as encryption algorithms. Verification is performed by estimating the correlation between registered and tested models in their cancelable format. Simulation results give Equal Error Rate (EER) values close to zero and Area under the Receiver Operator Characteristic Curve (AROC) equal to one, which indicates the high performance of the proposed system in addition to the difficulty to invert cancelable templates. Moreover, reusability and diversity of biometric templates is guaranteed.

Fan-beam projection for modulation classification in optical wireless communication systems.

The focus of most research nowadays in the field of communication technology is on increasing bandwidth in wireless connectivity. Adaptive modulation can be used to enhance efficiency of communication systems. Adaptive modulation requires modulation format identification (MFI) at the receiver side to avoid the overhead required to determine the modulation type at the receiver. We present an MFI algorithm based on fan-beam projection to generate patterns from the constellation diagrams that are more discriminative. The constellation diagrams are obtained as images for eight different modulation formats (2/4/8/16 - PSK and 8/16/32/64 - QAM). Different classifiers such as AlexNet, VGG16, and VGG19 are studied and compared for the task of MFI. Evaluation of this proposed algorithm is performed by estimating the classification accuracy at different optical signal-to-noise ratios (OSNRs) ranging from 5 to 30 dB. The simulation results reveal that the proposed algorithm succeeds in identifying the wireless optical modulation format blindly with a classification accuracy up to 100% even at low OSNR values less than 8 dB compared with the related work.

A Crypto-Steganography Approach for Hiding Ransomware within HEVC Streams in Android IoT Devices.

Steganography is a vital security approach that hides any secret content within ordinary data, such as multimedia. This hiding aims to achieve the confidentiality of the IoT secret data; whether it is benign or malicious (e.g., ransomware) and for defensive or offensive purposes. This paper introduces a hybrid crypto-steganography approach for ransomware hiding within high-resolution video frames. This proposed approach is based on hybridizing an AES (advanced encryption standard) algorithm and LSB (least significant bit) steganography process. Initially, AES encrypts the secret Android ransomware data, and then LSB embeds it based on random selection criteria for the cover video pixels. This research examined broad objective and subjective quality assessment metrics to evaluate the performance of the proposed hybrid approach. We used different sizes of ransomware samples and different resolutions of HEVC (high-efficiency video coding) frames to conduct simulation experiments and comparison studies. The assessment results prove the superior efficiency of the introduced hybrid crypto-steganography approach compared to other existing steganography approaches in terms of (a) achieving the integrity of the secret ransomware data, (b) ensuring higher imperceptibility of stego video frames, (3) introducing a multi-level security approach using the AES encryption in addition to the LSB steganography, (4) performing randomness embedding based on RPS (random pixel selection) for concealing secret ransomware bits, (5) succeeding in fully extracting the ransomware data at the receiver side, (6) obtaining strong subjective and objective qualities for all tested evaluation metrics, (7) embedding different sizes of secret data at the same time within the video frame, and finally (8) passing the security scanning tests of 70 antivirus engines without detecting the existence of the embedded ransomware.

A Novel Dynamic Bit Rate Analysis Technique for Adaptive Video Streaming over HTTP Support.

Recently, there has been an increase in research interest in the seamless streaming of video on top of Hypertext Transfer Protocol (HTTP) in cellular networks (3G/4G). The main challenges involved are the variation in available bit rates on the Internet caused by resource sharing and the dynamic nature of wireless communication channels. State-of-the-art techniques, such as Dynamic Adaptive Streaming over HTTP (DASH), support the streaming of stored video, but they suffer from the challenge of live video content due to fluctuating bit rate in the network. In this work, a novel dynamic bit rate analysis technique is proposed to model client-server architecture using attention-based long short-term memory (A-LSTM) networks for solving the problem of smooth video streaming over HTTP networks. The proposed client system analyzes the bit rate dynamically, and a status report is sent to the server to adjust the ongoing session parameter. The server assesses the dynamics of the bit rate on the fly and calculates the status for each video sequence. The bit rate and buffer length are given as sequential inputs to LSTM to produce feature vectors. These feature vectors are given different weights to produce updated feature vectors. These updated feature vectors are given to multi-layer feed forward neural networks to predict six output class labels (144p, 240p, 360p, 480p, 720p, and 1080p). Finally, the proposed A-LSTM work is evaluated in real-time using a code division multiple access evolution-data optimized network (CDMA20001xEVDO Rev-A) with the help of an Internet dongle. Furthermore, the performance is analyzed with the full reference quality metric of streaming video to validate our proposed work. Experimental results also show an average improvement of 37.53% in peak signal-to-noise ratio (PSNR) and 5.7% in structural similarity (SSIM) index over the commonly used buffer-filling technique during the live streaming of video.

Efficient implementation of optical scanning holography in cancelable biometrics.

This paper presents a new trend in biometric security systems, which is cancelable multi-biometrics. In general, traditional biometric systems depend on a single biometric for identification. These traditional systems are subject to different types of attacks. In addition, a biometric signature may be lost in hacking scenarios; for example, in the case of intrusion, biometric signatures can be stolen forever. To reduce the risk of losing biometric signatures, the trend of cancelable biometrics has evolved by using either deformed or encrypted versions of biometrics for verification. In this paper, several biometric traits for the same person are treated to obtain a single cancelable template. First, optical scanning holography (OSH) is applied during the acquisition of each biometric. The resulting outputs are then compressed simultaneously to generate a unified template based on the energy compaction property of the discrete cosine transform (DCT). Hence, the OSH is used in the proposed approach as a tool to generate deformed versions of human biometrics in order to get the unified biometric template through DCT compression. With this approach, we guarantee the possibility of using multiple biometrics of the same user to increase security, as well as privacy of the new biometric template through utilization of the OSH. Simulation results prove the robustness of the proposed cancelable multi-biometric approach in noisy environments.

Optical wireless communication performance enhancement using Hamming coding and an efficient adaptive equalizer with a deep-learning-based quality assessment.

Optical wireless communication (OWC) technology is one of several alternative technologies for addressing the radio frequency limitations for applications in both indoor and outdoor architectures. Indoor optical wireless systems suffer from noise and intersymbol interference (ISI). These degradations are produced by the wireless channel multipath effect, which causes data rate limitation and hence overall system performance degradation. On the other hand, outdoor OWC suffers from several physical impairments that affect transmission quality. Channel coding can play a vital role in the performance enhancement of OWC systems to ensure that data transmission is robust against channel impairments. In this paper, an efficient framework for OWC in developing African countries is introduced. It is suitable for OWC in both indoor and outdoor environments. The outdoor scenario will be suitable to wild areas in Africa. A detailed study of the system stages is presented to guarantee the suitable modulation, coding, equalization, and quality assessment scenarios for the OWC process, especially for tasks such as image and video communication. Hamming and low-density parity check coding techniques are utilized with an asymmetrically clipped DC-offset optical orthogonal frequency-division multiplexing (ADO-OFDM) scenario. The performance versus the complexity of both utilized techniques for channel coding is studied, and both coding techniques are compared at different coding rates. Another task studied in this paper is how to perform efficient adaptive channel estimation and hence equalization on the OWC systems to combat the effect of ISI. The proposed schemes for this task are based on the adaptive recursive least-squares (RLS) and the adaptive least mean squares (LMS) algorithms with activity detection guidance and tap decoupling techniques at the receiver side. These adaptive channel estimators are compared with the adaptive estimators based on the standard LMS and RLS algorithms. Moreover, this paper presents a new scenario for quality assessment of optical communication systems based on the regular transmission of images over the system and quality evaluation of these images at the receiver based on a trained convolutional neural network. The proposed OWC framework is very useful for developing countries in Africa due to its simplicity of implementation with high performance.

Performance Analysis of IoT and Long-Range Radio-Based Sensor Node and Gateway Architecture for Solid Waste Management.

Long-range radio (LoRa) communication is a widespread communication protocol that offers long range transmission and low data rates with minimum power consumption. In the context of solid waste management, only a low amount of data needs to be sent to the remote server. With this advantage, we proposed architecture for designing and developing a customized sensor node and gateway based on LoRa technology for realizing the filling level of the bins with minimal energy consumption. We evaluated the energy consumption of the proposed architecture by simulating it on the Framework for LoRa (FLoRa) simulation by varying distinct fundamental parameters of LoRa communication. This paper also provides the distinct evaluation metrics of the the long-range data rate, time on-air (ToA), LoRa sensitivity, link budget, and battery life of sensor node. Finally, the paper concludes with a real-time experimental setup, where we can receive the sensor data on the cloud server with a customized sensor node and gateway.

Discrete Transforms and Matrix Rotation Based Cancelable Face and Fingerprint Recognition for Biometric Security Applications.

The security of information is necessary for the success of any system. So, there is a need to have a robust mechanism to ensure the verification of any person before allowing him to access the stored data. So, for purposes of increasing the security level and privacy of users against attacks, cancelable biometrics can be utilized. The principal objective of cancelable biometrics is to generate new distorted biometric templates to be stored in biometric databases instead of the original ones. This paper presents effective methods based on different discrete transforms, such as Discrete Fourier Transform (DFT), Fractional Fourier Transform (FrFT), Discrete Cosine Transform (DCT), and Discrete Wavelet Transform (DWT), in addition to matrix rotation to generate cancelable biometric templates, in order to meet revocability and prevent the restoration of the original templates from the generated cancelable ones. Rotated versions of the images are generated in either spatial or transform domains and added together to eliminate the ability to recover the original biometric templates. The cancelability performance is evaluated and tested through extensive simulation results for all proposed methods on a different face and fingerprint datasets. Low Equal Error Rate (EER) values with high AROC values reflect the efficiency of the proposed methods, especially those dependent on DCT and DFrFT. Moreover, a comparative study is performed to evaluate the proposed method with all transformations to select the best one from the security perspective. Furthermore, a comparative analysis is carried out to test the performance of the proposed schemes with the existing schemes. The obtained outcomes reveal the efficiency of the proposed cancelable biometric schemes by introducing an average AROC of 0.998, EER of 0.0023, FAR of 0.008, and FRR of 0.003.

Securing web applications against XSS and SQLi attacks using a novel deep learning approach.

Modern web application development involves handling enormous amounts of sensitive and consequential data. Security is, therefore, a crucial component of developing web applications. A web application's security is concerned with safeguarding the data it processes. The web application framework must have safeguards to stop and find application vulnerabilities. Among all web application attacks, SQL injection and XSS attacks are common, which may lead to severe damage to Web application data or web functionalities. Currently, there are many solutions provided by various study for SQLi and XSS attack detection, but most of the work shown have used either SQL/XSS payload-based detection or HTTP request-based detection. Few solutions available can detect SQLi and XSS attacks, but these methods provide very high false positive rates, and the accuracy of these models can further be improved. We proposed a novel approach for securing web applications from both cross-site scripting attacks and SQL injection attacks using decoding and standardization of SQL and XSS payloads and HTTP requests and trained our model using hybrid deep learning networks in this paper. The proposed hybrid DL model combines the strengths of CNNs in extracting features from input data and LSTMs in capturing temporal dependencies in sequential data. The soundness of our approach lies in the use of deep learning techniques that can identify subtle patterns in the data that traditional machine learning-based methods might miss. We have created a testbed dataset of Normal and SQLi/XSS HTTP requests and evaluated the performance of our model on this dataset. We have also trained and evaluated the proposed model on the Benchmark dataset HTTP CSIC 2010 and another SQL/XSS payload dataset. The experimental findings show that our proposed approach effectively identifies these attacks with high accuracy and a low percentage of false positives. Additionally, our model performed better than traditional machine learning-based methods. This soundness approach can be applied to various network security applications such as intrusion detection systems and web application firewalls. Using our model, we achieved an accuracy of 99.84%, 99.23% and 99.77% on the SQL-XSS Payload dataset, Testbed dataset and HTTP CSIC 2010 dataset, respectively.

Enhancing automated vehicle identification by integrating YOLO v8 and OCR techniques for high-precision license plate detection and recognition.

Vehicle identification systems are vital components that enable many aspects of contemporary life, such as safety, trade, transit, and law enforcement. They improve community and individual well-being by increasing vehicle management, security, and transparency. These tasks entail locating and extracting license plates from images or video frames using computer vision and machine learning techniques, followed by recognizing the letters or digits on the plates. This paper proposes a new license plate detection and recognition method based on the deep learning YOLO v8 method, image processing techniques, and the OCR technique for text recognition. For this, the first step was the dataset creation, when gathering 270 images from the internet. Afterward, CVAT (Computer Vision Annotation Tool) was used to annotate the dataset, which is an open-source software platform made to make computer vision tasks easier to annotate and label images and videos. Subsequently, the newly released Yolo version, the Yolo v8, has been employed to detect the number plate area in the input image. Subsequently, after extracting the plate the k-means clustering algorithm, the thresholding techniques, and the opening morphological operation were used to enhance the image and make the characters in the license plate clearer before using OCR. The next step in this process is using the OCR technique to extract the characters. Eventually, a text file containing only the character reflecting the vehicle's country is generated. To ameliorate the efficiency of the proposed approach, several metrics were employed, namely precision, recall, F1-Score, and CLA. In addition, a comparison of the proposed method with existing techniques in the literature has been given. The suggested method obtained convincing results in both detection as well as recognition by obtaining an accuracy of 99% in detection and 98% in character recognition.

Deep learning innovations in diagnosing diabetic retinopathy: The potential of transfer learning and the DiaCNN model.

Diabetic retinopathy (DR) is a significant cause of vision impairment, emphasizing the critical need for early detection and timely intervention to avert visual deterioration. Diagnosing DR is inherently complex, as it necessitates the meticulous examination of intricate retinal images by experienced specialists. This makes the early diagnosis of DR essential for effective treatment and prevention of eventual blindness. Traditional diagnostic methods, relying on human interpretation of medical images, face challenges in terms of accuracy and efficiency. In the present research, we introduce a novel method that offers superior precision in DR diagnosis, compared to traditional methods, by employing advanced deep learning techniques. Central to this approach is the concept of transfer learning. This entails the utilization of pre-existing, well-established models, specifically InceptionResNetv2 and Inceptionv3, to extract features and fine-tune selected layers to cater to the unique requirements of this specific diagnostic task. Concurrently, we also present a newly devised model, DiaCNN, which is tailored for the classification of eye diseases. To prove the efficacy of the proposed methodology, we leveraged the Ocular Disease Intelligent Recognition (ODIR) dataset, which comprises eight different eye disease categories. The results are promising. The InceptionResNetv2 model, incorporating transfer learning, registered an impressive 97.5% accuracy in both the training and testing phases. Its counterpart, the Inceptionv3 model, achieved an even more commendable 99.7% accuracy during training, and 97.5% during testing. Remarkably, the DiaCNN model showcased unparalleled precision, achieving 100% accuracy in training and 98.3% in testing. These figures represent a significant leap in classification accuracy when juxtaposed with existing state-of-the-art diagnostic methods. Such advancements hold immense promise for the future, emphasizing the potential of our proposed technique to revolutionize the accuracy of DR and other eye disease diagnoses. By facilitating earlier detection and more timely interventions, this approach stands poised to significantly reduce the incidence of blindness associated with DR, thus heralding a new era of improved patient outcomes. Therefore, this work, through its novel approach and stellar results, not only pushes the boundaries of DR diagnostic accuracy but also promises a transformative impact in early detection and intervention, aiming to substantially diminish DR-induced blindness and champion enhanced patient care.

Securing Internet-of-Medical-Things networks using cancellable ECG recognition.

Reinforcement of the Internet of Medical Things (IoMT) network security has become extremely significant as these networks enable both patients and healthcare providers to communicate with each other by exchanging medical signals, data, and vital reports in a safe way. To ensure the safe transmission of sensitive information, robust and secure access mechanisms are paramount. Vulnerabilities in these networks, particularly at the access points, could expose patients to significant risks. Among the possible security measures, biometric authentication is becoming a more feasible choice, with a focus on leveraging regularly-monitored biomedical signals like Electrocardiogram (ECG) signals due to their unique characteristics. A notable challenge within all biometric authentication systems is the risk of losing original biometric traits, if hackers successfully compromise the biometric template storage space. Current research endorses replacement of the original biometrics used in access control with cancellable templates. These are produced using encryption or non-invertible transformation, which improves security by enabling the biometric templates to be changed in case an unwanted access is detected. This study presents a comprehensive framework for ECG-based recognition with cancellable templates. This framework may be used for accessing IoMT networks. An innovative methodology is introduced through non-invertible modification of ECG signals using blind signal separation and lightweight encryption. The basic idea here depends on the assumption that if the ECG signal and an auxiliary audio signal for the same person are subjected to a separation algorithm, the algorithm will yield two uncorrelated components through the minimization of a correlation cost function. Hence, the obtained outputs from the separation algorithm will be distorted versions of the ECG as well as the audio signals. The distorted versions of the ECG signals can be treated with a lightweight encryption stage and used as cancellable templates. Security enhancement is achieved through the utilization of the lightweight encryption stage based on a user-specific pattern and XOR operation, thereby reducing the processing burden associated with conventional encryption methods. The proposed framework efficacy is demonstrated through its application on the ECG-ID and MIT-BIH datasets, yielding promising results. The experimental evaluation reveals an Equal Error Rate (EER) of 0.134 on the ECG-ID dataset and 0.4 on the MIT-BIH dataset, alongside an exceptionally large Area under the Receiver Operating Characteristic curve (AROC) of 99.96% for both datasets. These results underscore the framework potential in securing IoMT networks through cancellable biometrics, offering a hybrid security model that combines the strengths of non-invertible transformations and lightweight encryption.

Integrating Digital Twins with IoT-Based Blockchain: Concept, Architecture, Challenges, and Future Scope.

In recent years, there have been concentrations on the Digital Twin from researchers and companies due to its advancement in IT, communication systems, Cloud Computing, Internet-of-Things (IoT), and Blockchain. The main concept of the DT is to provide a comprehensive tangible, and operational explanation of any element, asset, or system. However, it is an extremely dynamic taxonomy developing in complication during the life cycle that produces an enormous quantity of the engendered data and information from them. Likewise, with the development of the Blockchain, the digital twins have the potential to redefine and could be a key strategy to support the IoT-based digital twin's applications for transferring data and value onto the Internet with full transparency besides promising accessibility, trusted traceability, and immutability of transactions. Therefore, the integration of digital twins with the IoT and blockchain technologies has the potential to revolutionize various industries by providing enhanced security, transparency, and data integrity. Thus, this work presents a survey on the innovative theme of digital twins with the integration of Blockchain for various applications. Also, provides challenges and future research directions on this subject. In addition, in this paper, we propose a concept and architecture for integrating digital twins with IoT-based blockchain archives, which allows for real-time monitoring and control of physical assets and processes in a secure and decentralized manner. We also discuss the challenges and limitations of this integration, including issues related to data privacy, scalability, and interoperability. Finally, we provide insights into the future scope of this technology and discuss potential research directions for further improving the integration of digital twins with IoT-based blockchain archives. Overall, this paper provides a comprehensive overview of the potential benefits and challenges of integrating digital twins with IoT-based blockchain and lays the foundation for future research in this area.

Hybrid Multilevel Thresholding Image Segmentation Approach for Brain MRI.

A brain tumor is an abnormal growth of tissues inside the skull that can interfere with the normal functioning of the neurological system and the body, and it is responsible for the deaths of many individuals every year. Magnetic Resonance Imaging (MRI) techniques are widely used for detection of brain cancers. Segmentation of brain MRI is a foundational process with numerous clinical applications in neurology, including quantitative analysis, operational planning, and functional imaging. The segmentation process classifies the pixel values of the image into different groups based on the intensity levels of the pixels and a selected threshold value. The quality of the medical image segmentation extensively depends on the method which selects the threshold values of the image for the segmentation process. The traditional multilevel thresholding methods are computationally expensive since these methods thoroughly search for the best threshold values to maximize the accuracy of the segmentation process. Metaheuristic optimization algorithms are widely used for solving such problems. However, these algorithms suffer from the problem of local optima stagnation and slow convergence speed. In this work, the original Bald Eagle Search (BES) algorithm problems are resolved in the proposed Dynamic Opposite Bald Eagle Search (DOBES) algorithm by employing Dynamic Opposition Learning (DOL) at the initial, as well as exploitation, phases. Using the DOBES algorithm, a hybrid multilevel thresholding image segmentation approach has been developed for MRI image segmentation. The hybrid approach is divided into two phases. In the first phase, the proposed DOBES optimization algorithm is used for the multilevel thresholding. After the selection of the thresholds for the image segmentation, the morphological operations have been utilized in the second phase to remove the unwanted area present in the segmented image. The performance efficiency of the proposed DOBES based multilevel thresholding algorithm with respect to BES has been verified using the five benchmark images. The proposed DOBES based multilevel thresholding algorithm attains higher Peak Signal-to-Noise ratio (PSNR) and Structured Similarity Index Measure (SSIM) value in comparison to the BES algorithm for the benchmark images. Additionally, the proposed hybrid multilevel thresholding segmentation approach has been compared with the existing segmentation algorithms to validate its significance. The results show that the proposed algorithm performs better for tumor segmentation in MRI images as the SSIM value attained using the proposed hybrid segmentation approach is nearer to 1 when compared with ground truth images.

A comprehensive taxonomy on multimedia video forgery detection techniques: challenges and novel trends.

Thousands of videos are posted on websites and social media every day, including Twitter, Facebook, WhatsApp, Instagram, and YouTube. Newspapers, law enforcement publications, criminal investigations, surveillance systems, Banking, the museum, the military, imaging in medicine, insurance claims, and consumer photography are just a few examples of places where important visual data may be obtained. Thus, the emergence of powerful processing tools that can be easily made available online poses a huge threat to the authenticity of videos. Therefore, it's vital to distinguish between true and fake data. Digital video forgery detection techniques are used to validate and check the realness of digital video content. Deep learning algorithms lately sparked a lot of interest in the field of digital forensics, such as Recurrent Neural Networks (RNN), Deep Convolutional Neural Networks (DCNN), and Adaptive Neural Networks (ANN). In this paper, we give a soft taxonomy as well as a thorough overview of recent research on multimedia falsification detection systems. First, the basic knowledge needed to comprehend video forgery is provided. Then, a summary of active and passive video manipulation detection approaches is provided. Anti-forensics, compression video methods, datasets required for video forensics, and challenges of video detection approaches are also addressed. Following that, we presented an overview of deepfake, and the datasets required for detection were also provided. Also, helpful software packages and forensics tools for video detection are covered. In addition, this paper provides an overview of video analysis tools that are used in video forensic applications. Finally, we highlight research difficulties as well as interesting research avenues. In short, this survey provides detailed information and a broader investigation to extract data and detect fraud video contents under one umbrella.

Quad-port MIMO antenna with high isolation characteristics for sub 6-GHz 5G NR communication.

A four-port MIMO antenna with high isolation is presented. The antenna is primarily envisioned to cover the n48 band of Frequency Range-1 (FR-1) with TDD duplex mode. The engineered antenna has electrical dimensions of 90 × 90 × 1.57 mm3. The size miniaturization of a single antenna unit is achieved through an optimized placement of slots and extended arms. The quad-antennas are then placed orthogonally to achieve antenna diversity. The antenna resonates at 3.56 GHz and 5.28 GHz having 2:1 VSWR fractional bandwidth of 1.82% and 2.12%. The proposed resonator provides 88.34% and 79.28% efficiency at lower and upper bands, respectively. The antenna is an exceptional radiator regarding MIMO diversity performance owing to high inter-element isolation. The values of envelope correlation coefficient < 0.05, channel capacity loss is nearly 0.1 bits/sec/Hz, and total active reflection coefficient is - 24.26. The full ground plane profile aids in high directivity and cross-pol isolation. The antenna exhibits a gain of 4.2 dBi and 2.8 dBi, respectively, justifying intended application requirements. There is a good coherence between simulation and experimental results. The self-decoupled antenna poses its application in 5G and WLAN Communication Applications.