AES-Based Encoding and Decoding Images using MATLAB
A Novel Technique of Sign Language Recognition System using Machine Learning for Differently Abled Person
Implementation of Machine Learning Techniques for Depression in Text Messages: A Survey
A Study of Ransomware Attacks on Windows Platform
Techniques of Migration in Live Virtual Machine and its Challenges
Efficient Agent Based Priority Scheduling and LoadBalancing Using Fuzzy Logic in Grid Computing
A Survey of Various Task Scheduling Algorithms In Cloud Computing
A Viable Solution to Prevent SQL Injection Attack Using SQL Injection
A Computational Intelligence Technique for Effective Medical Diagnosis Using Decision Tree Algorithm
Integrated Atlas Based Localisation Features in Lungs Images
This paper mainly deals with mixed-image secret writing techniques. From the point of view of information security, recording secret images are extremely important. Security is the most complex aspect of networks and network applications. The web and networked applications are growing in the blink of an eye, which increases the importance and, therefore, the value of the information transmitted through the web or alternative media. Encryption can be related to improve image security by scanning pixels. Encryption will protect privacy on its own. It works in a wide variety of fields, such as network communications, medical photography, and military communications. Due to the natural selection of images, such as high duplication of information and the ability of mass dissemination of information, image encryption is different from the text. Text encryption is the conversion of information into an encoded cipher code that cannot be easily decrypted by outsiders.
Sign language is a language used by deaf and dumb people to communicate through hand gestures or facial expressions combined with non-manual elements. Various automotive tools and software have been developed by many developers, but they require hardware and an Internet connection, which adds to the cost of the software. In this paper, the presented software captures the hand gesture, and with the help of various machine learning optimization algorithms such as Stochastic Gradient Descent (SGD) and Adam (optimizer), the accuracy will be determined to give predictive value. With the help of computers, it could be a new way of learning for deaf and dumb people. During such a pandemic, various group learning apps and software have been developed for the purpose of conducting online classes, but they are useful for ordinary people. Using this technique will help deaf and dumb people with online learning.
Depression is a disease or problem associated with high levels of stress seen in humans. It is uncomfortable in talking to parents, psychologists, and healthcare professionals in general. So a virtual platform is much more suitable for sharing your emotions, for example, a chatbot that provides the user with a comfort zone, acting as a friend or well-wisher. Extracting and identifying emotions from text messages to detect depressive mood is a challenging task because it involves removing natural language ambiguities. Over the past decade, researchers have proposed various state-ofthe- art methods for detecting depressive moods in text. This paper aims to analyze such methods and present a comparison based on detection accuracy. The virtual platform provides an end-user interface for communication. The system understands the meaning and context of a sentence using Natural Language Processing (NLP), word embedding, and machine learning techniques. NLP does the preprocessing and extracts the mental health-related keywords. Word embedding converts the extracted keywords into embedding vectors that can be understood by Machine learning algorithms, it can also analyze and extract users' feelings by examining and calculating levels of depression and classifying the user as depressed or not. This paper showed that the support vector machine is the preferred algorithm over other machine learning algorithms and provides higher accuracy.
Ransomware is a type of new malware that is extremely dangerous and causes serious problems, affecting several organizations and individuals around the world. Ransomware attacks nearly doubled in the first half of 2021, according to statists. In 2020, there were approximately 304 million ransomware attacks worldwide in different parts of the world. The increase was 62% compared to last year and is the second largest increase since 2016. Many researchers are already talking about ransomware and its impact. However, much more research into ransomware is needed to provide further in-depth analysis and study of ransomware. This paper focuses specifically on the impact of ransomware on Windows platforms. Since Windows is the most widely used and well-known platform, it was chosen for the analysis. It monitors the infection process, how it occurs, as well as the various methods used by ransomware families to encrypt. In conclusion, this paper suggests that securing Windows is possible if system files and registry are closely monitored.
Cloud computing is the on-demand availability of computer system resources. Most technology industries are moving to the cloud. Cloud structures can be costly for users. Virtualization is used in cloud computing that helps the cloud at a low cost. Migrating virtual machines (VMs) helps to manage computation. Migration of virtual machines is a core feature of virtualization. The technique of migrating a running virtual machine from one physical host to another with minimal downtime is called "live virtual machine migration." This paper discusses the migration technique, i.e., migration before and after copying, and also issues related to live migration. This paper presents a better approach to the VM migration method and future challenges by differentiating from the previous live VM migration method.