Search

The technique of concealing information within photos, videos, music etc. is called image steganography. Steganography excels in that it does not disclose the presence of any data as opposed to other methods such as cryptology which only try to prevent reading the said data. A major factor in assessing the steganography algorithms is its security. Steganography has subsequently earned remarkable ground in the drawn-out battle with steganalysis. Steganography must be able to resist identification by steganalysis algorithms in order to boost the reliability of image steganography. Conventional steganography methods embed secret data into an image, which ultimately leaves a mark of alteration that can be detected by increasingly advanced AI-based steganalysis algorithms. Within this study, the authorscompare a method of concealing secret information within photos utilizing generative adversarial networks with conventional cryptography techniques. It is hypothesized that this technique has a clear potential to challenge best-in-class steganalysis measurements by producing high performing and increasingly stable payloads.

Blockchain is a progressive innovation technology that permits individuals to record message exchanges or transactions on an advanced, decentralized, distributed ledger, with no central controlling authority as in case of financial banking systems. Recorded transactions are seen to all users within the blockchain network and cannot be modified by any user or node. Blockchain has become popular with many other applications including IoT, healthcare, industry, supply chain management etc. Blockchain technology is ready to change almost every aspect of our advanced digital lives. By obviating third parties, blockchains guarantee to make our frameworks more effective. By going around oversight, they guarantee to make our frameworks more impartial. Also, if appropriately executed, they could make our frameworks more dependable and secure. The purpose of this review is to understand blockchain technology and challenges associated with its security and privacy.

Communication plays a very important role in the everyday life of human beings. Keeping this in mind, this project aims to aid the deaf and the dumb in communicating with others. Sign language is used for this form of communication and it is an incredible advancement that has grown over the years. This project helps in improving the communication with the deaf and the dumb using flex sensor technology. We convert the sign language so that it is understood by common people and will help them to communicate without any barriers. The main purpose of this project is to eliminate the barrier between the deaf and dumb and the others using an automated system to recognize the hand gestures and interpret them appropriately.,The system uses a combination of sensors fixed onto a glove to capture various sign gestures. The sensor values for these signs are then recorded by the Arduino and processed using K nearest neighbor machine learning algorithm. There are many challenges that come with the usage of the sign language. Not everyone knows how to interpret the sign language. Hence, we use a sensor based glove to overcome this issue. Currently, the system is able to successfully classify 21 letters and basic phrases with an accuracy of 93% for the chosen value of K in the nearest neighbor algorithm.

The technique of concealing information within photos, videos, music etc. is called image steganography. Steganography excels in that it does not disclose the presence of any data as opposed to other methods such as cryptology which only try to prevent reading the said data. A major factor in assessing the steganography algorithms is its security. Steganography has subsequently earned remarkable ground in the drawn-out battle with steganalysis. Steganography must be able to resist identification by steganalysis algorithms in order to boost the reliability of image steganography. Conventional steganography methods embed secret data into an image, which eventually leaves a sign of alteration that can be detected by increasingly propelled AI-based steganalysis algorithms. In this survey, various methods for hiding sensitive data within pictures using a combination of different soft computation techniques have been explored. This is aimed at evading detection by modern steganalysis tools and achieving higher quality output.

The rapid influx of people moving into the urban regions of the world continuously adds to the ever persistent trend of population increase in the large but small number of cities in the world, the cities face the challenge to renew and refit itself for its continued productivity and innovation sustaining its desirable future. With a predicted 70% of the world’s population moving to cities, the rapid transition to a highly urbanized population and city, creates many challenges for the planning, development and operation of cities. Thus to encounter and prepare for this rapid progression, the model of a “Smart City” developed around the concept of developing a communicative environment capable of collecting, analyzing and interacting via data from different devices around the city. This model helps in developing an adaptable system that seamlessly separates information and morphs to support different scenarios dealing with domains of infrastructure, traffic control, garbage collection, administration, services and connectivity. This paper portrays the several domains of a smart city, describing the present technologies and its ability to scale up within that domain. Finally a model city is framed with certain preset parameters, and to this city concepts introduces are applied thus serving as a framework that can be seamlessly translated to the real world.

Measurement of the ovarian parameters is one of the primary features observed in all gynecological scans. The ovarian parameters, major axis, minor axis are measured manually by the expert and the shape of the ovary is analyzed subjectively. Manual measurement is time consuming and Doctor requires approximately 20-25 minutes for each patient for complete diagnosis. With this constraint the doctor can effectively examine around 20 patients per day. With dearth of experts, it is required to reduce the diagnosis time so that more patients can get consultation. Hence there is a need for computer-assisted diagnostic support system for detection of ovarian features to aid the experts in faster diagnosis [1]. In this paper, we propose an improved algorithm (anisotropic diffusion filter, CLAHE enhancement, and global enhancement) for automated computerassisted measurement of ovarian size and shape parameters to help expert to do a quick diagnosis. The algorithm has a preprocessing stage, processing stage followed by ovarian parameter extraction. The proposed algorithm is tested on 50 Transvaginal ultrasound images of ovaries. The experimental results are validated against the manual measurements done by the expert and the results obtained by our algorithm are in good agreement with the experts inputs. The proposed algorithm could achieve an average Error Percentage of 5.27% for Major-Axis length (EM1) and average Error Percentage of 6.1% for Minor-Axis length (EM2).

Digital Imaging and Communications in Medicine (DICOM) is a standard for handling, storing, printing, and transmitting information in medical imaging. DICOM viewer tool is required to view and analyze these DICOM images. There are many commercially available algorithms which process Dicom images; however these algorithms are typically tuned for particular image types. This paper describes a universal transcoder for reading Dicom images and compressing these using lossy as well as lossless schemes such as Dicom rle, jpeg, and jpeg 2000. The compression technique to be used is either automatically selected based on image parameters, or as specified by the user. This paper also describes results from research conducted in the conversion of images from DICOM format into any other image format, reducing the dependence of specialized DICOM viewer tools for viewing medical images. The idea is to convert DICOM formatted files into bitmap format first, and then convert these bitmaps into other common image files. Our transcoder outputs an image that is best suited for viewing on wireless handhelds (mobile phones) as well as built-in viewers on computers, which facilitates further analysis and research to be conducted on these images.