Blockchain Scalability Analysis and Improvement of Bitcoin Network through Enhanced Transaction Adjournment Techniques
Data Lake System for Essay-Based Questions: A Scenario for the Computer Science Curriculum
Creating Secure Passwords through Personalized User Inputs
Optimizing B-Cell Epitope Prediction: A Novel Approach using Support Vector Machine Enhanced with Genetic Algorithm
Gesture Language Translator using Morse Code
Efficient Agent Based Priority Scheduling and LoadBalancing Using Fuzzy Logic in Grid Computing
A Survey of Various Task Scheduling Algorithms In Cloud Computing
Integrated Atlas Based Localisation Features in Lungs Images
A Computational Intelligence Technique for Effective Medical Diagnosis Using Decision Tree Algorithm
A Viable Solution to Prevent SQL Injection Attack Using SQL Injection
Blockchain is the foundation of cryptocurrencies, and most cryptocurrency technologies are decentralized. This study explains the topic of scalability in blockchains and presents a comparative analysis of numerous blockchain metrics with real-time data. This study performed this using a blockchain simulation (BlockSim) and then looked at effective techniques that may be utilized to overcome the limitation by comparing the simulator and real-world circumstances. This paper proposes an effective algorithm that improves the scalability of the Bitcoin network through efficient transaction deferment. This study also proposes an algorithm that improves the current Bitcoin protocols using inventory messaging (INV) and transaction deferment or adjournment. These improvements are compatible with the existing Bitcoin Network protocols. The improved algorithm was simulated using BlockSim and the AnyLogic Multi Paradigm Simulation Engine. The simulators were configured with 1000 nodes interconnected in a Bitcoin-like peer-to-peer network. The result of the simulation shows that the adjourned transaction protocol provides for a controlled reduction in the number of messages required to propagate a transaction at the cost of a modest increase in transaction propagation time. This system can manage the tradeoff between the quantity of messages and propagation latency by adjusting the threshold and timeout values, thereby improving the network's overall scalability.
Essay questions are a common form of evaluation that instructors and tutors use at all learning levels to gauge students' comprehension skills in relation to the material covered in the course or curriculum. Instructors encounter challenges when constructing questions that are impartial, often framing them under time constraints, which may cause students to perform poorly. This study introduces EQDL, an online database of essay questions that tutors may quickly and conveniently access. An iterative software engineering concept is utilized to accomplish this goal. A variety of data sources, including the ACM or IEEE computer science curriculum, were considered when creating the questions. The Fisher-Yates Shuffle algorithm and generative artificial intelligence were two of the approaches used for gathering data sources. This study uses the Computer Science Department of the Federal University of Technology, Minna, Nigeria's upper levels of the curriculum, particularly the final year, as a scenario. Web-based technologies were employed to design the front end, while the back end was designed using MySQL and other supporting libraries. The proposed system reduces the burden of crafting entirely new questions since data lake questions are easily accessible. Researchers might consider the curriculum for other levels to expand the scope and volume of data in the lake. Furthermore, more advanced technologies can be used to create a mobile-based system.
Individuals using various online services are increasingly concerned with securing and protecting their personal information from unauthorized access. There are numerous authentication systems available to safeguard data, with password-based authentication being one of the most common. Given the rise in information sharing, internet usage, e-commerce, and data transfer, ensuring the security and effectiveness of passwords has become crucial. However, the complexity of strong passwords leads to users forgetting them. To address this issue, this study proposes a novel algorithm for generating robust passwords, differing from existing random password generators. This study utilizes user-provided information to create passwords that are easier for users to remember. This system is tested with various synthetic input data and verified the strength of the generated passwords using four popular online password checkers. The results indicate that the passwords are highly reliable. Furthermore, this system effectively defends against two common types of password attacks: the dictionary attack and the brute force attack. This system is implemented in Python. The passwords generated by this system are not only easy for users to recall but also possess strong, secure characteristics that make them resistant to cracking attempts.
In the evolving area of immunoinformatics, accurate prediction of B-cell epitopes is vital for vaccine improvement and healing interventions. This study offers a novel predictive pipeline that employs a Support Vector Machine (SVM) model optimized by way of Genetic Algorithms (GA) to decorate the accuracy and reliability of B-cellular epitope predictions. By systematically extracting key capabilities, inclusive of β-turns, antigenicity, and hydrophobicity, from peptide and protein sequences, this study applied a robust statistics preprocessing approach that consists of labeling, normalization, and dataset splitting. The performance of the proposed SVM model is carefully evaluated towards traditional methods, including Random Forest (RF) and K-Nearest Neighbors (KNN). The proposed SVM model completed an accuracy of 92.5%, a precision of 89.3%, a bear-in-mind of 91.0%, and an F1 rating of 90.1%. In comparison, the RF model obtained an accuracy of 85.0%, at the same time as the KNN version reached an accuracy of 82.5%. Visualizations, together with function importance plots, ROC curves, and confusion matrices, illustrate the model's advanced performance and its capacity for real-international packages. This study's findings underscore the importance of integrating superior machine learning strategies in immunological research and offer a complete framework for future research in epitope prediction.
Gesture Language Translator using Morse Code is an innovative assistive technology designed to address the communication challenges faced by people with auditory or speech impairments. These individuals struggle with expressing themselves verbally or understanding spoken language, leading to social isolation and frustration. communication aids frequently lack the real-time responsiveness and intuitive interaction necessary for effective communication. This study aims to bridge this gap by leveraging a sensor-embedded glove that translates user gestures into Morse code, a universally recognized system. The translated Morse code is then converted into both auditory and visual feedback, enabling users to communicate seamlessly and in real time. The development of this device involves the integration of various hardware and software components to create a cohesive and functional prototype. Key hardware elements include sensors embedded in a glove, microcontrollers for processing signals, and output modules for delivering auditory and visual feedback. On the software side, this system entails designing robust. This study not only aims to develop a practical communication tool but also seeks to promote inclusivity and accessibility in communication. By enabling individuals with auditory or speech impairments to express themselves effectively, the Gesture Language Translator fosters participation in social interactions and activities. The iterative design process, involving user feedback and rigorous testing, ensures the device meets the specific needs of its users. Additionally, exploring the potential of incorporating Hindi Sign Language into the system could significantly enhance its accessibility for individuals who primarily communicate in Hindi. While this may require additional research and development, it presents an opportunity to bridge language barriers and promote inclusivity on a larger scale.
