Paper Title

SIMULATION APPROACH FOR FRICTION STIR WELDED AA6351 ALUMINIUM ALLOY AT DIFFERENT LOAD INTERVALS

Abstract

Friction Stir Welding (FSW) is an advanced solid-state joining process that has gained widespread adoption for welding aluminum alloys, particularly in applications where high joint strength, reliability, and precision are paramount. The aluminum alloy AA6351 is frequently used in industries such as aerospace, automotive, and marine due to its exceptional mechanical properties, including high tensile strength, good corrosion resistance, and excellent weld ability. However, the quality and performance of FSW joints are significantly influenced by process parameters, with load intervals defined by tool rotational speed, traverse speed, and different tools being among the most critical factors. This study presents a detailed exploration of the impact of varying load intervals on the FSW of AA6351 aluminum alloy, using a simulation-based approach to understand the underlying structural pattern of the weld. Using structural analysis, the study simulates the FSW process under different load intervals, providing insights into the weight distribution, and resultant microstructural changes within the weld zone. The simulation results are used to predict the mechanical properties, including tensile strength and micro hardness, of the welded joints.
KEYWORDS : ANSYS, Structural analysis, Load conditions

Paper Title

ROLE OF GOVERNMENT REGULATIONS IN AVIATION SAFETY

Abstract

The primary goal behind this study was to identify themes in expert opinion to determine if regulation adversely impacts the profitability of airlines. Safety and compliance officers for airlines were the selected study population due to their understanding of balancing safety with sustained profitability. A survey was utilized for the collection of data, and a qualitative method was used to derive themes from respondent answers. Qualitative analysis of the topic was necessary due to the complex macro-economic factors impacting airlines. Through the expert-accreditation approach, valuable themes were discovered that clearly indicated the perceived impact of regulation on the profitability of airlines. When analyzed, this data lends credence to the supposition that regulatory reform is necessary in the aviation industry. We describe the current status of knowledge regarding the contribution of aviation to anthropogenic climate forcing. The emissions and associated radiative forcings from aviation are compared to those from other modes of transport. The different analytical metrics used to quantify climate forcing are presented showing their relevancies and uncertainties. Furthermore, the data can assist regulators and airline lobbyists in determining the most beneficial manner reform can be implemented in the aviation industry.
KEYWORDS : airlines, aviation industry, transport, qualitative method, Federal Aviation Administration (FAA), federal government maintains.

Paper Title

A STUDY ON CYBERSECURITY IN THE DIGITAL AGE

Abstract

In an era defined by the digital revolution, cybersecurity has emerged as an indispensable shield guarding our online world. With cyber threats becoming increasingly sophisticated, understanding cybersecurity\'s importance and adopting best practices is more crucial than ever. Technology has become an integral part of our daily lives, and the importance of cybersecurity cannot be overstated. With the exponential growth of digital platforms and increasing sophistication of cyber threats individuals and organizations must prioritize their cybersecurity measures. The abstract highlight that cyber security in the digital space is an ever-evolving multifaceted field. It is critical for individuals organizations and Nations to remain visual and adaptive in the face of an increasingly sophisticated and interconnected cyber threat landscape. A comprehensive approach integrating technology, policy, and human awareness is essential to effectively secure our digital future. Cybersecurity has become an essential pillar in today\'s digital age where technology and connectivity are ubiquitous. In a world where personal data, sensitive information and digital infrastructure are at constant risk, understanding and applying sound cyber security principles becomes critical. It is very important for various reasons like protection against evolving threats, preservation of privacy, business continuity, national security etc. Cybersecurity acts as a protective shield against a wide variety of cyber attacks. Some of the types of attacks they address include: Malware, Ransomware, Phishing, DDOS attacks, Zero-Day attacks.
KEYWORDS : Cyber security, Cyber Attacks and Threats and Malware.

Paper Title

DEVELOPMENT AND VALIDATION OF MACHINE LEARNING MODELS FOR RISK PREDICTION IN PUBLIC HEALTH AND SAFETY MANAGEMENT

Abstract

Despite their value, historical facts and expert opinion could not paint the whole picture. Applying AI provides a state-of-the-art way to analyze large and complex data sets, which improves dynamic risk prediction. This study presents contextual studies done in several public locations to provide a detailed explanation of how these models might be used in practice. With the use of machine learning, risk appetite models may significantly enhance health and security policies by helping with better asset allocation and efficient preventative measures. However, we also address issues with data security, making sure models are understandable, and the necessity to update models periodically. Typical limitations of traditional methods of risk assessment include human biases and data inertia. Approaches directed and unaided learning to see how well they can identify potential dangers, such as industrial accidents, public health emergencies, and ecological disasters.
KEYWORDS : machine learning, public locations, state-of-the-art, public health emergencies, ecological disasters.

Paper Title

EXPLORING MATHEMATICAL TOOLS IN DESIGNING EFFICIENT TIMETABLE ALGORITHMS

Abstract

The problems that can be caused by having too many soft limitations in a timetable should be minimized as much as possible by a university\'s course schedule. Even though UCTP is widely considered to be one of the most intriguing difficulties that schools are currently facing, some of them are still generating their timetables manually using basic office software like spreadsheets. A large number of educational establishments at the higher education level employ a varied teaching staff that works together to accomplish the educational goals. However, one of the challenges that is faced by the majority of educational institutions is developing a schedule for these lecturers that is free from any potential conflicts. A table that details several events along with the times at which they take place is called a schedule. The curriculum for the forthcoming semester is something that the teaching staff and administrative staff at a variety of educational institutions spend a significant amount of time planning each year. The activities that must be accomplished for the faculty course assignment, for instance, are laid out as linear programmers. Linear programming, which entails the creation of mathematical models or algorithms, can be used to solve the problem of the faculty course assignment. One of the newest tools for dealing with problems that come up with academic course assignments is integer programming. For the purpose of this study, we dealt with the difficulties that arose from faculty course scheduling by employing a method that was based on fuzzy preferences. As a consequence of this, the objective of this research is to build algorithms that may be utilized to optimize resource timetables while producing course schedules for faculty members.
KEYWORDS : mathematical models, algorithms, timetables, educational institutions

Paper Title

DEEP LEARNING APPROACHES FOR ANOMALY DETECTION IN NETWORK TRAFFIC

Abstract

A great deal of attention has been given to deep learning in the field of network and information security. Any intrusion and anomaly in the network can significantly impact many areas, such as security of the private and social data, national security, social and financial concerns, etc. Therefore, network and information security are a broad research domain for which researchers are actively utilizing the functionally improved, emerging deep learning technique and report the improved result. In this review paper, we have analysed several deep learning methods in the area of network anomaly, intrusion detection, network traffic analysis and its classification. We have presented a comprehensive review of widely known deep learning approaches. And then, we conclude with open research challenges and unresolved issue for further study. Network intrusion detection is a key pillar towards the sustainability and normal operation of information systems. Complex threat patterns and malicious actors are able to cause severe damages to cyber-systems. In this work, we propose novel Deep Learning formulations for detecting threats and alerts on network logs that were acquired by pf Sense, an open-source software that acts as firewall on FreeBSD operating system. Pf Sense integrates several powerful security services such as firewall, URL filtering, and virtual private networking among others. The main goal of this study is to analyse the logs that were acquired by a local installation of pf Sense software, in order to provide a powerful and efficient solution that controls traffic flow based on patterns that are automatically learnt via the proposed, challenging DL architectures.
KEYWORDS : Semi-supervised anomaly detection; deep feature learning; convolutional neural networks; suricata network logs anomaly detection