DNA encoding schemes herald a new age in cybersecurity for safeguarding digital assets.

With the urge to secure and protect digital assets, there is a need to emphasize the immediacy of taking measures to ensure robust security due to the enhancement of cyber security. Different advanced methods, like encryption schemes, are vulnerable to putting constraints on attacks. To encode the digital data and utilize the unique properties of DNA, like stability and durability, synthetic DNA sequences are offered as a promising alternative by DNA encoding schemes. This study enlightens the exploration of DNA's potential for encoding in evolving cyber security. Based on the systematic literature review, this paper provides a discussion on the challenges, pros, and directions for future work. We analyzed the current trends and new innovations in methodology, security attacks, the implementation of tools, and different metrics to measure. Various tools, such as Mathematica, MATLAB, NIST test suite, and Coludsim, were employed to evaluate the performance of the proposed method and obtain results. By identifying the strengths and limitations of proposed methods, the study highlights research challenges and offers future scope for investigation.

Multimodal medical image fusion in NSST domain with structural and spectral features enhancement.

For the past 25 years, medical imaging has been extensively used for clinical diagnosis. The main difficulties in medicine are accurate disease recognition and improved therapy. Using a single imaging modality to diagnose disease is challenging for clinical personnel. In this paper, a novel structural and spectral feature enhancement method in NSST Domain for multimodal medical image fusion (MMIF) is proposed. Initially, the proposed method uses the Intensity, Hue, Saturation (IHS) method to generate two pairs of images. The input images are then decomposed using the Non-Subsampled Shearlet Transform (NSST) method to obtain low frequency and high frequency sub-bands. Next, a proposed Structural Information (SI) fusion strategy is employed to Low Frequency Sub-bands (LFS's). It will enhance the structural (texture, background) information. Then, Principal Component Analysis (PCA) is employed as a fusion rule to High Frequency Sub-bands (HFS's) to obtain the pixel level information. Finally, the fused final image is obtained by employing inverse NSST and IHS. The proposed algorithm was validated using different modalities containing 120 image pairs. The qualitative and quantitative results demonstrated that the algorithm proposed in this research work outperformed numerous state-of-the-art MMIF approaches.

A novel CT image de-noising and fusion based deep learning network to screen for disease (COVID-19).

A COVID-19, caused by SARS-CoV-2, has been declared a global pandemic by WHO. It first appeared in China at the end of 2019 and quickly spread throughout the world. During the third layer, it became more critical. COVID-19 spread is extremely difficult to control, and a huge number of suspected cases must be screened for a cure as soon as possible. COVID-19 laboratory testing takes time and can result in significant false negatives. To combat COVID-19, reliable, accurate and fast methods are urgently needed. The commonly used Reverse Transcription Polymerase Chain Reaction has a low sensitivity of approximately 60% to 70%, and sometimes even produces negative results. Computer Tomography (CT) has been observed to be a subtle approach to detecting COVID-19, and it may be the best screening method. The scanned image's quality, which is impacted by motion-induced Poisson or Impulse noise, is vital. In order to improve the quality of the acquired image for post segmentation, a novel Impulse and Poisson noise reduction method employing boundary division max/min intensities elimination along with an adaptive window size mechanism is proposed. In the second phase, a number of CNN techniques are explored for detecting COVID-19 from CT images and an Assessment Fusion Based model is proposed to predict the result. The AFM combines the results for cutting-edge CNN architectures and generates a final prediction based on choices. The empirical results demonstrate that our proposed method performs extensively and is extremely useful in actual diagnostic situations.

Historical Text Image Enhancement Using Image Scaling and Generative Adversarial Networks.

Historical documents such as newspapers, invoices, contract papers are often difficult to read due to degraded text quality. These documents may be damaged or degraded due to a variety of factors such as aging, distortion, stamps, watermarks, ink stains, and so on. Text image enhancement is essential for several document recognition and analysis tasks. In this era of technology, it is important to enhance these degraded text documents for proper use. To address these issues, a new bi-cubic interpolation of Lifting Wavelet Transform (LWT) and Stationary Wavelet Transform (SWT) is proposed to enhance image resolution. Then a generative adversarial network (GAN) is used to extract the spectral and spatial features in historical text images. The proposed method consists of two parts. In the first part, the transformation method is used to de-noise and de-blur the images, and to increase the resolution effects, whereas in the second part, the GAN architecture is used to fuse the original and the resulting image obtained from part one in order to improve the spectral and spatial features of a historical text image. Experiment results show that the proposed model outperforms the current deep learning methods.

A review of airport dual energy X-ray baggage inspection techniques: Image enhancement and noise reduction.

In this paper, we present a review of the research literature regarding applying X-ray imaging of baggage scrutiny at airport. It discusses multiple X-ray imaging inspection systems used in airports for detecting dangerous objects inside the baggage. Moreover, it also explains the dual energy X-ray image fusion and image enhancement factors. Different types of noises in digital images and noise models are explained in length. Diagrammatical representations for different noise models are presented and illustrated to clearly show the effect of Poisson and Impulse noise on intensity values. Overall, this review discusses in detail of Poisson and Impulse noise, as well as its causes and effect on the X-ray images, which create un-certainty for the X-ray inspection imaging system while discriminating objects and for the screeners as well. The review then focuses on image processing techniques used by different research studies for X-ray image enhancement, de-noising, and their limitations. Furthermore, the most related approaches for noise reduction and its drawbacks are presented. The methods that may be useful to overcome the drawbacks are also discussed in subsequent sections of this paper. In summary, this review paper highlights the key theories and technical methods used for X-ray image enhancement and de-noising effect on X-ray images generated by the airport baggage inspection system.

Novel multiparticulate pH triggered delayed release chronotherapeutic drug delivery of celecoxib-ß-cyclodextrin inclusion complexes by using Box-Behnken design.

This study aimed to prepare novel colon targeted celecoxib-ß-cyclodextrin (CXB-ß-CD) inclusion complex loaded eudragit S 100 (ES100) microparticles for chronotherapy of rheumatoid arthritis (RA) which is an innovative approach, never reported before, for the fabrication of CXB-ß-CD complex in the form of microparticles and its colon targeting. CXB was complexed with ß-cyclodextrin by kneading technique and we evaluated the effect of ß-CD on saturation solubility of CXB. Microparticles were developed by oil-in-oil emulsion solvent evaporation technique and formulation variables (polymer conc, surfactant conc and stirring speed) were optimized by using three-factor three-level Box-Behnken design (BBD). SEM imaging revealed smooth, uniform and spherical shape microparticles. There was 7.3 fold increases in saturation solubility of CXB-ß-CD inclusion complex in distilled water as compared to pure CXB. Particle size was in the range of 50.42 µm to 238.38 µm with entrapment efficiency of 68.47% to 91.65%. Biphasic drug release pattern was found i.e initially delayed release in stomach and small intestine followed by fast release at colonic pH. Response variable results achieved from optimized formulation were very close to the response values suggested by BBD signifying the actual reliability and robustness of BBD in the fabrication of colon targeted CXB-ß-CD microparticles. The comparison of CXB-ß-CD optimized formulation with optimized formulation containing pure CXB showed increase in drug release due to enhancement of water solubility of CXB-ß-CD inclusion complex. So, it can be concluded that CXB-ß-CD loaded ES100 microparticles can be successfully fabricated with enhanced solubility for the chronotherapy of rheumatoid arthritis.

A spatial fuzzy C-means algorithm for segmentation of brain MRI images.

Brain and its structure are extremely complex with deep levels of details. Applying image processing methods of brain image can be very useful in many practical domains. Magnetic Resonance Imaging (MRI) is widely used imaging technique and has particular advantage by possessing the capability of providing highly detailed images of brain soft tissues than any other imaging techniques. The real challenge at hand for researchers is to perform precise segmentation while overcoming the effects of noise and other imaging artifacts like intensity in homogeneity introduced in medical images during image acquisition process. In this research work, a directional weighted optimized Fuzzy C-Means (dwsFCM) method has been proposed for segmentation of brain MR images. This method works by incorporating the spatial information of the pixels of the images and assigning the directional weights to the neighborhood. In order to validate the proposed segmentation framework, a comprehensive set of experiments have been performed on publically available standard simulated as well as real datasets. The experimental results showed 95% of accuracy and the performance of the proposed segmentation framework is much better and the framework suppress the sufficient amount of noise especially rician noise and reproduce good segmentation by overcoming the effect of intensity in homogeneity.

MRI Imaging, Comparison of MRI with other Modalities, Noise in MRI Images and Machine Learning Techniques for Noise Removal: A Review.

BACKGROUND: Medical imaging is to assume greater and greater significance in an efficient and precise diagnosis process. DISCUSSION: It is a set of various methodologies which are used to capture internal or external images of the human body and organs for clinical and diagnosis needs to examine human form for various kind of ailments. Computationally intelligent machine learning techniques and their application in medical imaging can play a significant role in expediting the diagnosis process and making it more precise. CONCLUSION: This review presents an up-to-date coverage about research topics which include recent literature in the areas of MRI imaging, comparison with other modalities, noise in MRI and machine learning techniques to remove the noise.

Patterning titania with the conventional and modified micromolding in capillaries technique from sol-gel and dispersion solutions.

We report TiO2 patterns obtained by a soft-lithographic technique called 'micromolding in capillaries' using sol-gel and dispersion solutions. A comparison between patterning with a sol-gel and dispersion solutions has been performed. The patterns obtained from sol-gel solutions showed good adhesion to the substrate and uniform shapes, but large shrinkage, whereas those obtained from dispersion solution had high solid content, but exhibited poor adhesion and non-uniform shapes. A fabrication method of a layer-by-layer structured pattern is also demonstrated. This type of pattern may find application in sensors, waveguides and other photonics elements. The occurrence of an undesirable residue layer, which hinders the fabrication of isolated patterns, is highlighted and a method of prevention is suggested.