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Cached-N-Proxy: An Intelligent Proxy Algorithm for Preventing Insider Email Threats to Mail Servers
Insider threats are serious security risks that come from people who work for or are contracted by an organization, such as partners, employees, or contractors. These people use their authorized access to commit hostile acts against the infrastructure, data, or assets of the company. Serious ramifications could result from these dangers, such as financial losses, reputational harm, data breaches, and possible threats to national security. Enterprises must strengthen their defenses with strong intrusion detection and prevention systems because of the growing attack surface for insider threats caused by the increasing adoption of digital technology and remote work habits. Organizations must use a combination of preventive strategies and detection mechanisms, such as privileged access management (PAM), role-based access control, data loss prevention (DLP) techniques, two-factor authentication, and thorough insider threat awareness training, to effectively combat insider threats. 2024 IEEE. -
Integrated Automated Attendance System with RFID, Wi-Fi, and Visual Recognition Technology for Enhanced Classroom Security and Precise Monitoring
The integrated automated smart attendance system utilizes RFID, Wi-Fi, and visual recognition technologies to elevate classroom security and ensure precise monitoring of attendance records. It consolidates cutting-edge components such as RFID tags, ESP8266 Wi-Fi modules, ESP-32 CAM modules, solenoid locks, servo motors, and PIR sensors to devise a strong remedy. RFID technology enables accurate attendance tracking by assigning tags to students and faculty members. The Wi-Fi and visual recognition components enhance the system's functionalities, facilitating wireless connectivity, instantaneous data transfer, and validation of identities. Solenoid locks and servo motors ensure controlled access, responding to validated attendance records. PIR sensors detect motion, contrasting between genuine presence and proximity. The paper's methodology delineates the necessary hardware and software requirements, procedures for system initialization, testing phases, establishment of server connectivity, implementation of access control mechanisms, and formulation of end-of-session protocols. It highlights the successful integration and validation of hardware components, backend connectivity, identity confirmation, attendance recording, data encryption, and session termination procedures. The research aims to modernize attendance tracking in educational settings, improving efficiency, accuracy, and security while appreciating the need for further adaptation to suit diverse educational environments for broader adoption and sustained advancement. 2024 IEEE. -
English to Hindi Translation System Using Hybrid Techniques
Good communication is critical for overcoming cultural and linguistic divides in today's internationalized society. An essential communication component is the Translation of written materials, primarily academic papers, from one language into another. This abstract focuses on the research involved in translating academic publications from Hindi to English. Translating Hindi academic papers into English is naturally hard due to the significant linguistic and cultural differences between the two languages. The proposed work provided an analytical analysis of various models used in language translation, including the seq-to-seq model, MT5, and LSTM, with the help of BLEU score, Learning rate, and average loss. MT5 model outshines others in terms of an average loss of 4.75; meanwhile, LSTM has an average loss of 5.56, and the seq-to-seq model has an average loss of 6.09, implying weaker Translation. 2024 IEEE. -
Durability Studies and Stress Strain Characteristics of hooked end steel fiber reinforced ambient cured geopolymer concrete
For conventional concrete, the use of fibers has proven to improve the strength properties of the material. However, in the case of ambient cured geopolymer concrete, there are limited studies that explore the application of fibers, in particular, the use of hooked end steel fibers. Further, it is important to study the durability properties of geopolymer concrete with fibers, since it will influence the service life of the structures in practice. Therefore, in the present study, fiber-reinforced geopolymer concrete was synthesized using fly ash, GGBS, hooked end steel fibers, and alkaline solution made with Na2SiO3 and NaOH. The percentage of steel fibers varied in the range of 0.5% to 2% with an increment of 0.5% by volume fraction of the binder. The precursor materials were characterized using techniques such as X-ray fluorescence (XRF), X-ray diffraction (XRD), and scanning electron microscope (SEM). Durability studies like water absorption, drying shrinkage, sulphate attack were studied. In addition, the elastic constants were determined through stress strain behaviour of geopolymer concrete in uniaxial compression. The results of the experimental study showed that the addition of hooked end steel fibers influences the strength of geopolymer concrete up to an optimal percentage, which was found to be 1%. Furthermore, in terms of durability properties, the addition of fibers exhibited better results in terms of resistance to water absorption and chemical attack, and this was validated by the microstructural studies, where the specimens with hooked end steel fibers revealed much denser hardened geopolymer matrix when compared to the mixes without fibers. Published under licence by IOP Publishing Ltd. -
Full Reference Image Quality Assessment (FR-IQA) of Pre-processed Structural Magnetic Resonance Images
Deep learning-based Artificial Intelligence algorithms have surpassed human-level performance in many fields including medicine. Specifically in diagnosis using radiology images, deep neural networks empowered AI to excel by educating intricate nonlinear relationships which is a core part of the complicated radiology problems. However, these models require a massive amount of quality data for training. The accuracy of the deep learning model is based on the amount of training data and the quality of the trained data being fed. So, preprocessing the data from different capturing devices is inevitable. This study aimed to highlight some of the image quality metrics that can be used to quantify the efficiency of the chosen preprocessing pipeline. By quantifying the result of each preprocess step, the user can choose an optimal set of preprocesses that can greatly improve the image quality, leading to a high and accurate diagnosis through a deep learning model. Thus, this study detailed how the full reference image quality metrics can be used to validate the performance of sMRI preprocess tasks. 2024 IEEE. -
Epilepsy Detection Using Supervised Learning Algorithms
In the current scenario, people are suffering and isolated by themselves by seizure detection and prediction in epilepsy. Also, it is highly essential that it needs to be identified through wearable devices. Researchers discussed this issue and outlined future developments in this field, suggesting that Machine Learning (ML) techniques could radically change how we diagnose and manage patients with epilepsy. However, as data availability has increased, Deep Learning (DL) techniques have become the most cutting-edge approach to adopt and use with wearable devices. On the other hand, large amounts of data are needed to train DL models, making overfitting problematic. DL models are created with open-source toolboxes and Python, allowing researchers to create automated systems and broaden computational accessibility. This work thoroughly overviews deep learning (DL) methods and neuroimaging modalities for automated epileptic seizure identification. It covers several MRI and EEG techniques for epileptic seizure diagnosis and treatment programmes designed to treat these seizures. The study also covers the difficulties in precise detection, the benefits and drawbacks of DL-based strategies, potential DL models and upcoming research in this area. 2024 IEEE. -
Detecting Deepfake Voices Using a Novel Method for Authenticity Verification in Voice-Based Communication
The widespread use of deepfake technology in recent years has given rise to grave worries about the alteration of audio-visual material. The integrity of voice-based communication is particularly vulnerable to the threat posed by deepfake voice synthesis. The development of cutting-edge methods for the identification of deepfake voices is examined in this paper, which also offers a thorough analysis of current approaches, their advantages, and disadvantages. The research presents a novel method for detecting deepfakes in voice recordings that uses signal processing, machine learning, and audio analysis to separate synthetic voices from authentic voices. By achieving superior accuracy in differentiating between real and deepfake voices, and proposed method supplies a strong barrier against the misuse of voice synthesis technology for malicious purposes, also go over the research some of the possible uses for this technology, like voice authentication system security and social media platform content moderation. The paper's insights will support continued efforts to strengthen the authenticity of voice communication in the digital age and reduce the risks associated with deepfake voice synthesis. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Theoretical Framework for Integrating IoT and Explainable AI in a Smart Home Intrusion Detection System
Using IoT devices in smart homes brings benefits and security dangers. This study extensively examines various intrusion detection methods within smart home environments. It also suggests a novel hybrid intrusion detection theoretical framework integrating IoT data with Explainable Artificial Intelligence (XAI) approaches. Using information from multiple IoT devices, including motion sensors, door/window sensors, cameras, and temperature sensors, our theoretical framework can create a comprehensive image of the home environment. By effectively detecting new threats, it offers anomaly detection utilizing unsupervised learning approaches to discover potential breaches without tagged data. 2024 IEEE. -
Wideband Compact Two-Element Millimeter Wave MIMO Antenna for Communication Systems
This article presents the wide band two-element MIMO antenna with an I-shaped decoupling structure in the ground plane. It is to enhance the isolation on the MIMO antenna. The dimension is 7.5 17.5 mm2. The measured bandwidth is 2 GHz (22.25-24.25 GHz) with a maximum gain of 4.5 dBi and bidirectional radiation. MIMO antenna satisfies three diversity metrics. 2024 IEEE. -
Machine Learning's Transformative Role in Human Activity Recognition Analysis
Human action recognition (HAR) is a burgeoning field of computer vision that seeks to automatically understand and classify the intricate movements performed by humans. From the graceful leaps of a ballerina to the decisive strides of a surgeon, HAR aims to decipher the language of motion, unlocking a plethora of potential applications. This abstract delves into the core of HAR, highlighting its key challenges and promising avenues for advancement. We begin by outlining the various modalities used for action recognition, such as RGB videos, depth sensors, and skeletal data, each offering unique perspectives on the human form. Next, we delve into the diverse set of algorithms employed for HAR, ranging from traditional machine learning techniques to the burgeoning realm of deep learning. We explore the strengths and limitations of each approach, emphasizing the crucial role of feature extraction and model selection in achieving accurate recognition. Challenges in Human Action Recognition (HAR), such as intra-class variations, inter-class similarities, and environmental factors. Ongoing efforts include robust feature development and contextual integration. The paper envisions HAR's future impact on healthcare, robotics, video surveillance, and augmented reality, presenting an invitation to explore the transformative world of human action recognition and its potential to enhance our interaction with technology. 2024 IEEE. -
A Hybrid Evolutionary Deep Learning Model Integrating Multi-Modal Data for Optimizing Ovarian Cancer Diagnosis
This research intends to enhance ovarian cancer detection by the combination of state-of-the-art machine learning algorithms with extensive multi-modal datasets. The Convolutional Neural Network (CNN), K-Nearest Neighbors (KNN), and VGG16 models were thoroughly assessed, displaying remarkable precision, recall, F1 scores, and overall accuracy. Notably, VGG16 emerged as a strong performance with a precision of 0.97, recall of 0.96, F1 score of 0.97, and accuracy reaching 98.65%. The addition of confusion matrices enables a thorough insight on each model's classification performance. Leveraging multiple datasets, spanning CT and MRI scans with demographic and biographical facts, promotes the holistic knowledge of ovarian cancer features. While the suggested Hybrid Evolutionary Deep Learning Model was not deployed in this work, the results underscore the potential for its development in future research. These discoveries signify a huge leap forward in early detection capabilities and individualized treatment techniques for ovarian cancer patients. As technology and medicine combine, this study tracks a road for breakthrough diagnostic approaches, empowering clinicians and encouraging favourable results in the continuing struggle against ovarian cancer. 2024 IEEE. -
A Novel Preprocessing Technique to Aid the Detection of Infected Areas of CT Images in COVID-19 Patients Artificial Intelligence (AI) for Communication Systems
An innovative preprocessing method for discerning infected areas in CT images of COVID-19 is described in this abstract. The methodology being suggested exploits the capabilities of artificial intelligence (AI) to improve disease detection communication systems. By employing sophisticated AI algorithms to preprocess CT images, the method seeks to increase the precision and effectiveness of COVID-19-associated area detection. The incorporation of artificial intelligence (AI) into communication systems facilitates enhanced image analysis, resulting in improved diagnostic capabilities and treatment strategizing. The study's findings demonstrate the potential of preprocessing techniques powered by artificial intelligence in augmenting communication systems with the aim of enhancing healthcare outcomes. 2024 IEEE. -
Nine Level Quadra Boost Inverter with Modified Level Shifted Pulse Width Modulation Technique
This research initiatives to introduce a switched capacitor based nine level boost inverter (SC-9LBI) powered by modified level shifted pulse width modulation (PWM) technique. The SC-9LBI equipped with single DC source along with three capacitors and eight controlled switches to develop nine level inverter output voltage. The suggested inverter configuration has the ability of boosting the inverter input voltage into 1:4 ratio. Also, this research involves modified level shifted PWM technique to enhance the quality of inverter output voltage. The effectiveness of the NLMLI is assessed through parameters such as harmonic distortion, peak voltage, and output voltage root mean square value (rms). Simulation studies have been conducted using MATLAB/Simulink to evaluate the proposed inverter's performance. 2024 IEEE. -
Stock Price Prediction Using RNNs: A Comparative Analysis of RNN, LSTM, GRU, and BiRNN
Stock price prediction is a crucial area of financial market research, having significant implications for investors, traders, and analysts. However, given the dynamic and intricate nature of financial markets - which are impacted by a wide range of variables such as economic statistics, geopolitical developments, and market sentiment - accurately projecting stock prices is intrinsically difficult. Conventional techniques frequently fail to fully capture these dynamics, producing predictions that are not ideal. Recurrent Neural Networks (RNNs), one of the most recent developments in machine learning, provide potential methods to overcome these obstacles. Despite their potential, the effectiveness of different RNN architectures in stock price prediction remains an area of active research. This study compares four Recurrent Neural Network (RNN) architectures - Simple RNN, Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional Recurrent Neural Network (BiRNN) - for forecasting the Nifty 50 index values on the Indian National Stock Exchange (NSE) from the year 2000 to 2021. Using a comprehensive dataset spanning two decades, we assess each model's performance using the metrics Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE). The data shows that the BiRNN model regularly outperforms the other models in all criteria i.e., MAE, MAPE, and MSE, indicating higher predictive accuracy. This study adds to the existing research by offering useful insights into the usefulness of RNN models, especially that of the BiRNN model for predicting stock prices, specifically in the setting of the Nifty 50 index. Our findings emphasize BiRNN's potential as a stock price prediction model and open new options for future research in this area. 2024 IEEE. -
Sugarcane Leaf Disease Classification Using Convolutional Neural Network
Indian sugarcane is of good quality, and the country exports significant quantities of sugar and sugarcane-based products to various countries worldwide. However, the quality of the sugarcane can vary depending on the specific variety and growing conditions, and exporters need to ensure that the product meets the quality standards of the importing country. To maintain the quality of sugarcane during transportation, it is crucial to ensure that it is harvested at the right time and handled carefully during loading and unloading. However, the farmers cultivating the sugarcane face a significant issue dealing with bacterial infection in the plants. In order to stop the disease from spreading further, we use Convolutional Neural Networks in our article to extract information from sugarcane leaves and construct an algorithm that accurately classifies bacterial leaves. Using an image dataset that includes pictures of both healthy and ill plants, identifying the image's key characteristics, and using the image to help the classifier provide a reliable result are all included in the total process. Our study will save farmers time and effort by identifying the decaying plants by looking for bacterial patches on the leaves. 2024 IEEE. -
Detecting Infectious Disease Based on Social Media Data Using BERT Model
Seasonal diseases are those diseases that are widespread during a particular time of the year including monsoons, winter etc. In the absence of preventative measures, the human race remains vulnerable to the hazardous effects of seasonal diseases following regular patterns of increased inci- dence and transmission which remains a global concern. Dengue, Influenza, etc. are such types of diseases where every year many people get affected globally. The primary focus of this research paper is to understand the opinion of people regarding the seasonal diseases. The research paper covers sentiment analysis on textual data from social media where people have vocalized their sentiments or thinking regarding seasonal diseases and seasonal infectious diseases. Influenza, Dengue, Malaria, Japanese Encephalitis, and Chikungunya are the seasonal diseases that have been covered in this research paper. To achieve this, the language model Bidirectional Encoder Representations from Transformers (BERT) was used to verify the sentiments about the seasonal diseases. The result of the investigation hold the potential to significantly enhance our comprehension of societal sentiments, discerning between states of tranquility and concern among individuals. The outcome of the study will help healthcare department to plan the necessary actions. 2024 IEEE. -
Analyzing Job Satisfaction, Job Performance, and Attrition in International Business Machines Corporation through Python
Since workers significantly impact the firm's operation, businesses invest heavily in them. They must deliver better and more excellent performance to compete with the increasing competition. Employee performance is becoming more and more important for business success and staying ahead of the competition, so companies are putting more money into things like training, growth centers, and careers. The target audience was the employees working in International Business Machines Corporation. The data was analyzed through the process of Exploratory Data Analysis using Python. There is a 0.002297 link between Job Satisfaction and Performance Rating, and a 0.002572 correlation between Work Life Balance and Performance Rating. The relationship between work-life balance and job involvement is -0.01462, indicating a negative impact on work-life balance for people who are heavily interested in their occupations. The study would help Human Resources Managers formulate their policies and understand the employees better in the current environment. Here, Job Satisfaction and Performance Rating served as mediators, and the findings show that their influence on Attrition is minimal at this firm. 2024 IEEE. -
High Gain Miniature Antenna Arrays for 2.4 GHz Applications
In this paper, miniature corporate feed Four Element Array (FEA), Eight Element Array (EEA) and Sixteen Element Array (SEA) are presented. The proposed antenna arrays are created on Rogers Duroid 5880 substrate with permittivity 2.2 and thickness of 0.782 mm. Initially, a single element antenna was created, then it was used in a corporate feed network designed for the 4-element array. As an extension, the 4-element array was used as a template and created an 8-element array and 16-element array to achieve high gain and directivity at 2.4 GHz. The proposed FEA, EEA, and SEA exhibit reflection coefficients of -25.55 dB, -37.14 dB, and -30.61 dB respectively. The peak gains obtained are 11.5 dB, 13.67 dB, and 16.76 dB respectively for FEA, EEA, and SEA. Also, the directivity has improved corresponding to the increase in the number of elements. Therefore, it can be a suitable candidate for applicationswhere extended range and coverage with better signal quality and higher data transfer rates is a priority. 2024 IEEE. -
A Textual Analysis of Panchatantra, Enhanced by Technology from the Psychological Perspective
This research paper offers a textual analysis of the portrayal of animals in the Panchatantra tales, leveraging technology, Natural Language Processing (NLP) for enhanced insights. The study focuses on the interplay of anthropomorphism and stereotypes within these narratives, delving into the diverse stereotypes associated with specific animals in the stories. This analysis enhances our understanding of animal portrayal in children's literature. Natural Language Processing (NLP) techniques like textual classification and thematic analysis have been employed to examine the underlying archetypes embedded within the tales to comprehend stereotypes. Through a close examination of literary examples employing AntConc, a corpus analysis software, this paper provides readers with a nuanced understanding of how anthropomorphism and stereotypes influence human perceptions of animals and contribute to our understanding of the natural world. 2024 IEEE. -
Sentiment Analysis of Lenders Motivation to Use a Peer-To-Peer (P2P) Lending Platform: LenDenClub.Com
Peer-To-Peer lending platforms are becoming a good investment avenue for lenders to invest their money in borrowers, who need money for a different purpose. As lending and borrowing of money is facilitated by the P2P lending platform, it becomes necessary for the platform to understand the users and accordingly fine tune the 'User Interface' (UI) and 'User Experience' (UX) of the platform. For lending and borrowing to take place through a platform it is necessary to have an 'n' number of lenders who are ready to lend money to an 'x' number of borrowers. This study is specifically done to understand lenders' motivation to use P2P lending platforms. This is a unique research work as sentiment analysis of lenders' motivation to use these platforms has not been explored earlier. The sentiment analysis technique was used to examine lenders' sentiments towards the use of P2P lending platforms. The research results show that, ~ 70 percent of lenders showed motivation to use P2P lending platforms as an investment avenue in the future. As the P2P lending platforms are relatively new more research can be carried out in future. 2024 IEEE.