EDeLeaR: Edge-based Deep Learning with Resource Awareness for Efficient Model Training and Inference for IoT and Edge Devices

Deep learning has emerged as a powerful technique for processing and extracting insights from complex data. However, the resource-constrained nature of edge devices poses significant challenges to the deployment of deep learning models at the network edge. This research proposes a novel algorithm called EDeLeaR, which stands for Edge-based Deep Learning with Resource-awareness, to enable efficient model training and inference in edge computing environments. EDeLeaR leverages adaptive resource allocation and optimization techniques to maximize the utilization of limited computational resources while preserving model accuracy and minimizing latency. This paper presents the design, implementation, and evaluation of EDeLeaR, showcasing its effectiveness through comprehensive experiments on real-world edge devices.   &nbsp

Copy-Move Image Forgery Detection Using CNN and SIFT Algorithm

The widespread use of digital image alteration emphasizes how urgently reliable detection methods are needed to protect the originality and integrity of visual content. In this paper, we present an original technique for copy-move image forgery detection that combines Scale-Invariant Feature Transform (SIFT) with Convolutional Neural Networks . Our approach uses SIFT for reliable key-point descriptor extraction and Error Level Analysis (ELA) preprocessing to improve potentially changed regions. In parallel, a CNN model is trained using characteristics extracted from ELA representations to distinguish between modified and unmanipulated images. Although our hybrid methodology shows promising results in identifying copy-move forgeries, it is important to recognize the limits of current methods and systems.These drawbacks include the inability to grasp the results, scalability problems, dependency on handcrafted characteristics, computational complexity, limited generalization, partial copy-move vulnerabilities, and lack of interpretability. Our suggested method`s incorporation of SIFT is essential for identifying forgeries, especially in situations where copy-move manipulation is involved. By offering robust and unique descriptors that are independent of scale, rotation, and translation, SIFT features provide precise recognition of replicated areas in a picture. This method improves the model`s capacity to identify minute changes and visualise the location of forgery by utilizing SIFT in conjunction with CNN. This helps to maintain the visual authenticity and reliability of digital content. &nbsp

4G Mobile Network Planning in Seiyun City to Upgrade the Existed Networks

This paper presents the results of a research effort on the present and future of the current government cellular communications network in the city of Seiyun in terms of coverage, signal strength, and service provided. In recent times, Seiyun city has experienced an increase in population, a reduction in network coverage, and insufficient capacity from corporate stations to service certain areas. Many 4G stations have been planned and established in Seiyun as a result of the growing demand for mobile data consumption brought on by new applications like video chats, live streaming of sporting events, faster online browsing, and other heavily streamed material. The goal of the fourth generation of radio technologies, or 4G, is to increase speed and capacity. It can handle broader channels up to 20 MHz, shorter transmission times, and better wireless access technologies. For this research, we have designed a 4G network that will be established over the course of the next ten years, and even beyond. Furthermore, this research has involved comparing the signal levels and throughput values of our network, which covers the entire city of Seiyun, with current networks. &nbsp

A Review on Harmonizing Psychological Factors into Cyber Space

The rapidly evolving digital environment has changed how people think, communicate, and behave, calling for an interdisciplinary understanding of how people interact with machines. This manuscript investigates the newly-emerging discipline of cyberpsychology, which combines sociology, technology, and psychology to study how individuals interact with the digital environment. The review follows the evolution of cyberpsychology through time, starting in the early days of the internet and ending in the present day with digital mental health interventions and widespread connectedness. The "digital modulation hypothesis," which contends that fundamental human abilities like emotional acuity, empathy, and impulse control continue to exist online but are expressed adaptively within the constraints and affordances of virtual environments, is the main thesis. The literature on subjects like moral reasoning, self-regulation in digital environments, and cyber-disinhibition is critically analysed in this work. It also emphasises how they interact. It also discusses issues like risk perception, emotional resilience, and algorithmic bias, highlighting the interaction between psychology and cybersecurity. The analysis concludes by outlining potential future paths for cyberpsychology, including the influence of new technologies, the development of digital health, and the moral use of behavioural data in cybersecurity. &nbsp

A Deep Learning Based Deepfake AI (Images & Videos) Detection Tool

Lenscan.ai represents a pivotal advancement in countering the rising threat posed by deepfake technology. This state-of-the-art AI tool integrates sophisticated computer vision and audio analysis algorithms to detect anomalies that signal deepfake manipulation in digital media. By scrutinizing visual indicators such as facial expressions and lip movements, coupled with auditory features like voice characteristics, Lenscan.ai employs a comprehensive, multi-modal approach to accurately identify falsified content. Its versatility extends across diverse media formats and platforms, playing a crucial role in mitigating risks across journalism, entertainment, and national security sectors. As deepfake methods become increasingly sophisticated, Lenscan.ai continues to evolve, ensuring it remains at the forefront of safeguarding the integrity and reliability of digital content. By doing so, it addresses the urgent need to combat misinformation, thereby preserving trust in the digital landscape and upholding the authenticity of information shared globally. &nbsp

Protection of Research Data and Devices from Malware Attacks Using Endpoint Security System in Network

In ealier era, computer were used by limited organizations with limited Internet accessibility therefore data and device security were not big issue. Gradulay, uses of computer increased exponentialy therefore day-by-day it posing new data security challenges. As the uses of Intrenet increases, the challenges of critical data security also increased. Every 39 second, one cyber attack is occurring and thousands website hacked daily. Every establishment uses the computational devices with Internet for running and expnading their business. The computational devices are generting huge volume of data which is very sensitive and essenatial to run the organization. All the devices which are connected in open network are prone to attack by harmful viruses. In this hyper-connected world, protecting the devices and data from loss and unauthorized access are big challenge. The Process of protecting the data from destructive threats, unauthorised access and data corruption is known as data security. In past, the Antivirus software on individual desktop computer were sufficient to protect the device but for various network devices, which are connected in Local Area Network (LAN) is required integrated centralized solution along with management tools. Many antivirus tools sometime failed to detect the advance thretas due to that risk of vulnerability and data lost may increased. End point securiy is an process to protect the all devices which is connected in network. As the Internet uses grown, the types of the threats and techniques changed due to that these Antivirus tools could not prove full efficient to protect the sesitive data. The antivirus software can be used for individual machine but in organization Network, dedicated Endpoint Security System (EPS) is necessary for quickly detecting the malware and common security threats in advance. The EPS runs on organization security policy.   In this paper we are discussing about the implementation of Endpoint Security system for protecting computational devices and protecting the scientific and bussiness data at CWPRS Local Area Network (CLAN). The Endpoint Security System has been successfully implemented for protecting the scientific data and devices from external possible thretas

Design and Analysis the Radar Cross Section of Complex Targets for Military and Civilian Applications

In aerospace industry, the importance of RCS in the aerospace industry. State the purpose of the comparative study and analysis of various geometries contributing to RCS prediction. Analyze how changes in frequency affect RCS and discuss the relationship between radar range and RCS. MATLAB to create models of different geometries. Outline the parameters studied (e.g., frequency of operations, radar range) and the tools used for simulation (e.g., MATLAB). This research paper describe design features that minimize RCS (e.g., absorbent paint, smooth surfaces) and list and describe the different geometries analyzed in the study. The software tools used (e.g., MATLAB, commercial RCS simulation software). Use MATLAB’s plotting functions to visualize the results and analyze the variation of RCS and visual aids to demonstrate the variation of RCS with different parameters. &nbsp

HeathDetect7: Multiple Disease Identification Using Machine Learning

Numerous machine learning models in healthcare focus on single disease detection, yet there`s a growing need for systems that predict multiple diseases using a unified interface. This research addresses this gap by leveraging machine learning techniques to analyse diverse medical datasets and provide personalized risk assessments for diseases such as COVID-19, brain tumours, breast cancer, heart disease, diabetes, Alzheimer`s, and pneumonia. These diseases are causing many deaths globally, often due to the lack of timely check-ups and medical interventions. This problem is intensified by inadequate medical infrastructure and a low ratio of doctors to the population. By incorporating medical imaging data and clinical parameters, this study offers a comprehensive approach to disease identification, enabling early intervention and improved health outcomes. The project`s user-friendly interface allows individuals to input their medical information easily and receive timely assessments. Various classification algorithms, such as Random Forest, eXtreme Gradient Boosting (XGBoost), Convolutional Neural Networks (CNN), and Visual Geometry Group-16 (VGG-16), are explored to achieve accurate disease prediction. The ultimate goal is to create a web application that leverages machine learning to forecast several diseases, contributing to proactive healthcare management, and empowering individuals to monitor their health proactively and make informed decisions about their well-being. &nbsp

Credit Card Fraud Identification Using Calibrated K nearest Neighbor

Credit card fraud has become a significant concern in today`s digital world, leading to substantial financial losses for individuals and businesses alike. Detecting fraudulent transactions accurately and efficiently is crucial for maintaining the security of financial systems. The proposed method combines the power of KNN, a popular classification algorithm, with calibration techniques to enhance the fraud identification performance. Calibration is employed to adjust the probabilities assigned by the KNN algorithm, allowing for more accurate classification decisions and better control over the false positive rate. To evaluate the effectiveness of the proposed approach, comprehensive experiments are conducted on a benchmark credit card fraud dataset. The results demonstrate that the calibrated KNN method outperforms the traditional KNN classifier in terms of both accuracy and other performance parameters. The calibrated KNN approach achieves higher fraud detection rates and produces well-calibrated probability estimates, reducing the risk of false alarms or missed fraud cases. This research contributes to the advancement of credit card fraud detection systems and provides valuable insights for financial institutions and individuals concerned with safeguarding against fraudulent activities

The Neural Frontier: AI`s Relentless Encroachment into the Human Mind

The intersection of neuroscience and artificial intelligence (AI) represents a turning point in technological progress, presenting previously unheard-of chances to better comprehend and improve human cognition while also posing significant ethical issues. The ethical implications of AI-driven neuro-technologies are examined in this work, "The Neural Frontier: AI`s Relentless Encroachment into the Human Mind," through a tripartite technique that includes a systematic literature review, an interdisciplinary ethical analysis, and a speculative risk assessment. Our findings highlight the importance of brain privacy and cognitive liberty as fundamental rights, highlighting the need for strong governance frameworks and data security measures. The possibility of AI systems perpetuating biases in neuroscientific applications, the potential for unequal access to cognitive augmentation to exacerbate societal disparities, and the blurring of moral duty as AI influences human cognition are among the major ethical problems that we uncover. The study makes the case for the creation of frameworks for equitable cognitive augmentation, neuro-ethically-aligned AI, and adaptive governance models. It highlights how important it is to collaborate across disciplines, involve the public, and incorporate neuro-ethics into scientific education. We are not just developing technology but also redefining the limits of human identity and awareness as we traverse this cerebral frontier, striking a balance between the enormous potential of AI in neuroscience and the necessity to protect human dignity, autonomy, and cognitive liberty. In an era of unparalleled technological advancement, this undertaking necessitates moral discernment, scientific probity, and a dedication to safeguarding the fundamental principles that characterize our humanity. &nbsp