Global Journal of Engineering Innovations and Interdisciplinary Research
The Global Journal of Engineering Innovations and Interdisciplinary Research (GJEIIR) (ISSN: 3066-1226) is a peer-reviewed, open-access journal dedicated to advancing knowledge and fostering innovation across diverse fields of engineering and interdisciplinary research. Our mission is to provide a global platform for researchers, academics, and practitioners to share groundbreaking ideas, novel methodologies, and impactful research findings that address real-world challenges. Aims: To promote the dissemination of high-quality research in engineering disciplines and interdisciplinary fields. To bridge the gap between theoretical advances and practical applications by encouraging collaboration across disciplines. To support innovation that addresses global challenges such as sustainability, technology integration, and societal impact. Scope: GJEIIR welcomes original research articles, review papers, and case studies that contribute to the following areas, but not limited to: Engineering Innovations: Civil, Mechanical, Electrical, and Electronic Engineering Computer Science and Information Technology Renewable Energy and Sustainable Technologies Robotics, Automation, and Artificial Intelligence Interdisciplinary Research: Smart Cities and Urban Development Biomedical Engineering and Health Informatics Nanotechnology and Materials Science Environmental Engineering and Climate Adaptation Emerging Trends: Internet of Things (IoT) and Industry 4.0 Quantum Computing and Advanced Cryptography Blockchain Applications in Engineering Ethical and Social Implications of Technological Innovations The journal encourages submissions from both established and emerging researchers worldwide, fostering diversity and inclusivity in its contributions. Each manuscript is rigorously peer-reviewed to ensure scholarly excellence, originality, and relevance. Audience: Our primary audience includes researchers, educators, industry professionals, and policymakers seeking to explore and apply engineering innovations and interdisciplinary solutions for global development. By publishing in GJEIIR, authors will benefit from wide visibility, timely dissemination of their work, and the opportunity to contribute to a growing body of knowledge that inspires positive change across industries and societies. Join us in shaping the future of engineering and interdisciplinary research! Manuscripts could be submitted through our online portal or as directly to the editorial office as attachment to email at publications@scienceexcel.org
Deep Neural Networks and Bayesian Approaches for Energy Dissipation Modeling in Non-Linear System
Energy dissipation in non-linear systems plays a crucial role in diverse fields, including quantum thermodynamics, fluid dynamics, and climate modeling. Traditional mathematical models often struggle to capture the stochastic and high-dimensional nature of entropy production and energy loss in these systems. This research presents a hybrid machine learning framework that integrates Deep Neural Networks (DNNs) for learning complex dissipation patterns and Bayesian inference methods for probabilistic reasoning and uncertainty quantification. The proposed approach is validated across multiple domains: (1) Quantum systems, where the model predicts decoherence and entropy generation in non-equilibrium quantum states; (2) Fluid dynamics, where energy dissipation in turbulent flows is analyzed using deep learning-based turbulence modeling; and (3) Climate systems, where entropy production due to radiative and convective processes is estimated to improve predictive climate models. Experimental evaluations demonstrate that the hybrid DNN-Bayesian framework outperforms traditional numerical solvers by improving predictive accuracy while maintaining interpretability through probabilistic uncertainty estimates.
Copyright (c) 2025 Global Journal of Engineering Innovations and Interdisciplinary Research
Crime Prediction and Analysis Using Machine Learning
Crime prediction and analysis have been the backbone within which public safety is expected and effective policies can be made. Using time series forecasting techniques such as SARIMA and Prophet, this project describes crime forecasting in Andhra Pradesh, Telangana, and Maharashtra. The overall analysis will be directed towards producing actionable insights for police and policymakers by analysing historical crime data. After thorough data preparation involving cleaning and feature enrichment, Exploratory Data Analysis (EDA) will be conducted to identify important crime patterns and seasonal behaviours. The predictive model of SARIMA captures seasonal variations, while Prophet takes care of the irregularities and missing data with flexibility and accuracy. A comparative assessment of these two models would help to highlight their strength in forecasting crime trends. Crime patterns significantly differ with each region with specific traits attributed to socio-economic and geographical aspects. Some problems include data imbalance, scant historical data, and inconsistencies in records. But the project highlights the application of machine learning to handle real-life problems in crime prevention. It opens up a future direction integrating new, real-time data, socio-economic variables, and geospatial analyses to make predictions more precise and provide actionable insights dynamically. Overall, the project shows the promise of data-driven approaches in improving public safety and reducing crime rates.
Blood group detection is a crucial aspect in healthcare industry, especially during blood transfusions, organ transplantation, and prenatal care. Traditionally, blood typing has been achieved through serological tests. These are highly accurate tests but require a blood sample and are usually carried out in a laboratory. It can be invasive, take a lot of time, and relies on specialized equipment; hence, they are not easily accessible, especially to remote or resource-limited areas. This paper introduces a new approach for blood group detection through fingerprint image processing. Instead of depending on a blood sample we take, we look to utilize the uniqueness in our fingerprints — that is, ridge patterns and minutiae points — for determination of our blood type. Fingerprints are known to possess unique features that have, upon careful analysis, indicated a possible link to blood group traits. This method, using advanced image processing techniques and machine learning algorithms, particularly Convolutional Neural Networks (CNNs), can analyze fingerprint images and predict blood types with accuracy. It could transform the way blood typing is done by offering a non- invasive, quick, and affordable alternative that could be used in places where traditional blood typing is very challenging. The preliminary data look encouraging, indicating the potential of this approach for revolutionizing point-of-care diagnostics and making blood typing easier and more efficient and quick..
AI Powered Server Log Management System Using Random Forest Model
As technology continues to advance, system errors have become more frequent, and users often struggle to find the right solutions. Traditionally, users had to search across multiple servers for answers, which often resulted in a flood of responses, making it difficult to identify the correct one. This process can be time-consuming and frustrating. To address this issue, we've created a solution that collects error logs from all servers and utilizes Artificial Intelligence (AI) to build a smart system. By training the AI model on past error questions and their solutions, the system can now analyse a user’s error and predict a suitable solution. This means users no longer have to search through various sources. Instead, they can get a quick and accurate answer from a single, centralized server. This AI-powered system simplifies error resolution by providing more direct, relevant answers. Unlike Google or Bing, which may return too many irrelevant results, our AI ensures users receive the most precise solution to their specific problem. The system is built using a carefully selected dataset containing questions and answers related to operating systems (OS), programming issues, and other technical problems. Over time, with the addition of new questions and answers from users, the AI will learn and improve, solving an even wider variety of errors. A key advantage of this system is that the dataset can be continuously updated with new information from users. This ensures that the AI model remains current and is capable of addressing a broader range of errors. Whether the issue relates to OS problems, software bugs, or network errors, the AI will keep getting better, making it a valuable tool for troubleshooting.
Intelligent Tunnel Surveillance Using Deep Learning
Maintaining tunnel safety presents numerous challenges due to their restricted environments and specific conditions. This study proposes a cutting-edge system that leverages deep learning to improve tunnel surveillance efficiency and reliability. The framework incorporates advanced neural network models, particularly Region-based Convolutional Neural Networks (RCNN), to process real-time video feeds and detect risks like unauthorized entry, debris, and structural problems. Tailored with data specific to tunnel scenarios, the system achieves impressive accuracy, even under challenging circumstances such as dim lighting and reduced visibility. By streamlining anomaly detection and hazard prevention, this framework enhances safety protocols, minimizes reliance on manual oversight, and facilitates swift emergency responses. This innovative approach has the potential to transform tunnel management, ensuring greater safety and operational dependability.
Vessel Nerves Extraction and Segmentation using Frangi Filter and Multiresolution Techniques
Blood vessels of fundus images identification holds an important position for identifying many eye diseases. It suggests a simplified method, with which the detection process is accelerated, so far as the efficiency of its results is concerned. Starting from the input image, enhanced through Contrast Limited Adaptive Histogram Equalization, increases blood vessels' visibility. To isolate the optic disc, a morphologically processed version of the image is subtracted from the enhanced image. By using this Frangi algorithm and some multiresolution techniques like up sampling and down sampling. Hessian vessel Ness and gaussian filter ensures the accuracy of the system by playing a vital role in the main methodology of the system. This version maintains the technical details while presenting the information in a more natural, reader-friendly manner.Future scope can be established using many integration techniques like artificial intelligence. In conclusion the model accelerate the time and accuracy in computer aided diagnosis regarding retinal fundus images.
Detection of Cardiovascular Diseases in ECG Images Using Machine Learning and Deep Learning Methods
Cardiovascular conditions (CVDs) are a major global health challenge and the leading cause of mortality worldwide. Early opinion and effective bracket of these conditions can significantly reduce losses. Electrocardiograms (ECGs), as a readily available and cost-effective individual tool, give critical perceptivity into the electrical exertion of the heart, enabling the identification of CVDs. This exploration focuses on using deep literacy models to prognosticate four distinct cardiac conditions irregular twinkle, myocardial infarction, a history of myocardial infarction, and normal heart function, exercising a intimately accessible ECG image dataset. Originally, transfer literacy was explored using pretrained models similar as SqueezeNet and AlexNet. latterly, a custom- designed convolutional neural network(CNN) was developed to enhance the discovery of cardiac abnormalities. also, these pretrained networks, along with the proposed CNN, were employed as point birth mechanisms for machine literacy algorithms, including support vector machines(SVM), K- nearest neighbors(K- NN), decision trees(DT), arbitrary timbers (RF), and Naïve Bayes(NB). The findings reveal that the proposed CNN armature surpasses being styles, achieving an delicacy of 98.23, a recall rate of 98.22, a perfection score of 98.31, and an F1 score of 98.21. also, when used as a point extractor, the CNN model achieved an emotional delicacy of 99.79 with the Naïve Bayes algorithm. Impact Statement — The integration of artificial intelligence (AI) into healthcare has the implicit to revise complaint discovery, significantly perfecting patient issues. This study introduces a feather light and effective CNN model that achieves 98.23delicacy in classifying cardiovascular conditions using ECG image data. The model operates efficiently on standard computing tackle, making it practical for real- world operations. Likewise, its use as a point birth tool enhances traditional machine literacy ways, delivering a remarkable 99.79 delicacy with the Naïve Bayes classifier. This approach holds pledge for IoT healthcare ecosystems, paving the way for farther exploration in AI- driven cardiovascular diagnostics.
Predictive Analysis to Find Chances of Causing Stroke (Machine Learning)
Stroke is recognised as one of the most dangerous it can cause for both life time disability and cause immediate death,across the world, there is a immediate necessary of accurate predictive models. These machine learning models are used to more accurate identifying the person who are more likely to experiencing this life altering medical event. So, now there is need for prediction of stroke for present generation intervention and based on the result taking before medication. Stroke can be predicted by analysing different signs in your body like hypertension, severe headache, trouble speaking and numbness on the face arm or on legs. By giving some health parameters like age, hypertension(0,1), any heart disease, married, residence type, average glucose level are considered as the input feature which is used to training the model and testing the model this helps to predict the given inputs and predict accurate solution. In this model we used algorithms like Linear Support Vector Machine (SVM) for classification, SMOTEENN (Synthetic Minority Over-sampling Technique with Edited Nearest Neighbors) used to balance the dataset, One-Hot Encoding is used to preprocess categorical variables by converting them into a numerical format, Pipeline combines preprocessing and the classification algorithm into a single workflow. This model can give approximately 85-90% accuracy. .
Face To BMI: Predicting Body Mass Index Using Neural Networks
Body Mass Index (BMI), a vital health metric indicating weight relative to height, classifies individuals as underweight, normal weight, overweight, or obese, guiding early interventions for health risks, but traditional methods relying on height and weight measurements are labor-intensive, making automated BMI assessment a scalable and efficient alternative for health analysis and decision-making; leveraging facial features, which reveal correlations between face geometry and BMI, we employed large-scale pretrained models like EfficientNet-B7, Swin-Transfomer, and ResNeSt- 101, trained on diverse datasets such as the Illinois DOC, Height-weight-BMI dataset with Celebrity Faces, to enhance prediction accuracy, scalability, and applications in health monitoring, insurance, and policymaking, thereby enabling informed societal and individual health decisions.
Human Computer Interaction Through Eye Ball
Somepeoplehavehealth-relatedconditionsthatimpedetheir utilization of computers. In that regard, it is quite necessary to come up with redesigns for effective use of computers that meet the requirement of people living with disability. The human eye can be considered as a promising replacement of standard input devices. This paper presents a web camera as an option with which a person would control the cursor by moving the eye. For the pupil detection, we use OpenCV, so the eye frames obtained would be sequenced for the purpose of controlling the movement ofthemouseonthescreen. Also, Eye Aspect Ratio (EAR) is computed for determining the gaze movement, i.e., lateral movement of the left or right eye using the OpenCV library under Python programming. This particular method aims to optimize the computer environment in the very focus of those with physical challenges by providing them with a way to control the computer without using for instance a mouse.
Improving Bankruptcy Prediction Using Machine Learning
It is very crucial to predict bankruptcy for the decision-making process of creditors, investors, businesses, and policymakers. While it will help businesses and financial institutions in making sound judgments by accurately projecting bankruptcy, it helps to reduce the adverse impacts also for the economy and society. The methodologies like random forest regression and SMOTE along with other algorithms were cross validated to enhance accuracy. These prediction models can be developed further by including both financial and non-financial factors like market conditions and management quality. Moreover, dramatic efficiency improvements can be achieved through advanced technologies like deep learning. Technological advancement and data accessibility will increase these tactics such that banks and businesses can discover bankruptcy risk, make the right decisions, and save losses.
Traffic Sign Recognition Using YOLO And RCNN
The “Traffic Sign Recognition with YOLO-RCNN” system, investigates a novel approach to traffic sign recognition for advanced driving systems by combining the speed and efficiency of the You Only Look Once (YOLO) object detection framework with the precision and accuracy of Region-based Convolutional Neural Networks (R-CNN). YOLO excels at rapidly identifying multiple traffic signs within an image, providing an initial set of detections. However, its initial bounding box predictions can sometimes be imprecise. To address this limitation, R-CNN is integrated as a post-processing step. R-CNN refines the initial detections by adjusting the bounding box coordinates and significantly improving the accuracy of traffic sign classification. This hybrid approach leverages the strengths of both architectures, resulting in a system that achieves a superior balance between speed and accuracy. The system's performance was rigorously evaluated on a diverse dataset encompassing a wide range of traffic sign types and challenging environmental conditions, such as varying lighting and occlusions. Experimental results demonstrate a significant improvement in both detection accuracy and processing speed compared to traditional methods. This innovative approach not only enhances the performance of traffic sign recognition systems but also paves the way for more robust and reliable solutions for autonomous driving applications. Future research directions include: optimizing the model architecture through techniques such as network pruning and quantization to further improve inference speed and computational efficiency, expanding the training dataset to include more diverse and challenging scenarios, such as adverse weather conditions and complex urban environments, to enhance the model's generalization capabilities, and exploring the integration of advanced deep learning techniques, such as attention mechanisms and transformer architectures, to further improve the model's ability to focus on salient features and enhance its overall performance.
Machine Learning Techniques for Crop, Fertilizer Prediction and Disease Detection
Agriculture is an important part of a country's economy, but with the population growing, weather patterns changing, and resources dwindling, the food demands of today's world are becoming more challenging to meet. Smart farming, or precision agriculture, is a modern solution to these challenges. It utilizes technology, especially machine learning (ML), to help farmers make better decisions about their crops. ML allows machines to learn and get better with time without having to be directly programmed. This paper examines how ML is being used in farming for such important questions as: Which crops should one grow in that particular area? What fertilizer does the soil prefer? How does one detect diseases and treat them? In this regard, new farming techniques also make it easy to harvest crops, thus not requiring much human labor.ML is not technology; it's changing the way farmers work. For instance, consider a farmer who used to decide how to manage his crops with the help of years of experience. Now, they can make much more accurate data-driven decisions through ML. Farmers can use the sensors and drones to check how healthy their crops are in real-time, then problems can easily be identified, and solutions would be implemented rapidly. This means that not only time is saved but also assists the farmer in being more productive, with guesswork reduced. The use of ML in agriculture makes agriculture become more sustainable. By using resources like water, fertilizer, and pesticides more efficiently, farmers can minimize waste and lower their environmental footprint. This is important because the climate is changing and it is not easy to predict weather patterns anymore. Smart farming practices help in creating a more resilient agricultural system, one that can adapt to changing conditions and continue to produce food for future generations. In the end, ML is not just about increasing productivity but making farming more efficient, environmentally friendly, and secure for everyone.
Deep Learning Approaches For Traffic Sign Detection
Detecting traffic signs is important for many purposes, like ensuring safe driving and identifying illegal driving behavior. This study assesses specialized tools (e.g., Resnet, Inception, Mobile-net, and Dark net) and sophisticated detection models. It improves the trained prior model using the Microsoft Coco dataset for German car sign detection by using adaptive learning. evaluation of image size, computational complexity, speed, memory utilization, and accuracy. The results show that R-Fcn Resnet 101 balances precision and quick performance While Ssd Mobile-net is the fastest and best memory, Yolo Version 2 excels in performance & precision making it perfect for embedded devices, mobile devices, and gadgets.
Machine learning Ensemble Methods for Detection of Phishing in Website
In this research article, we propose to use a learning method with combinations such as the competitive random forest algorithm and the cloud gradient boosting and algorithm to efficiently and accurately identify customers who follow phishing websites. Phishing is one of the biggest cybercrimes in today's digital world. The attackers attempt to Obtain victims’ credentials, account information, and other sensitive information by impersonating existing and generally trusted individuals or organizations are visible and similar to phishing websites. On real websites. Online commerce has also grown to increase the number of phishing scams. Network Security is the most difficult task to achieve, and development. Automated systems are in place for phishing website detection. Need machine learning is one of the best solutions for this situation because it can provide the correct classification system as well as check the status of phishing strategies.
Cloud Resource Forecasting Using LSTM Neural Networks
Efficiently managing cloud resources is critical for balancing performance, availability, and cost in modern cloud environments. Allocating too many resources leads to unnecessary expenses, while insufficient resources can cause performance issues, bottlenecks, and downtime. This paper aims to solve these challenges with Long-Short-Term Memory (LSTM) neural networks to forecast cloud resource usage. LSTMs excel at processing time-series data, making it ideal for predicting trends in cloud resource consumption such as CPU usage, memory allocation. The project involves collecting historical cloud usage data and refining it through preprocessing to ensure accuracy and quality. Using this data, a robust LSTM model is designed and trained to provide reliable predictions of future resource demands. With accurate forecasts, cloud service providers can optimize resource allocation and implement proactive scaling strategies. The model helps ensure resources are neither over-provisioned nor underprovisioned, reducing waste while maintaining the capacity to handle workload demands. This approach leads to significant cost savings, improved system performance, and seamless scalability. Dynamic autoscaling is another benefit of this solution, enabling real-time adjustments to resource allocation based on predicted demand. This ensures that performance remains stable during usage spikes while avoiding unnecessary operational costs. To evaluate the model’s accuracy, metrics like Mean-Absolute-Error (MAE) and Root-Mean Squared-Error (RMSE) are analysed, with results showing that the LSTM model performs better than traditional forecasting techniques. This paper highlights the transformative potential of machine learning in cloud management. By accurately predicting resource needs, it offers an innovative, cost-efficient, and scalable solution for modern cloud operations, empowering organizations to meet demand reliably while keeping costs under control.
Gen AI Content Generation Model Using LLM Without Internet
The Gen AI content generation model uses the LLAMA-3.1-8B model where 8B implies 8 billion parameters to effectively produce customized content. It generates the content based on three inputs: topic, word count, and complexity level, which can be easy, medium, or hard. The front-end development is done by using Streamlit and CSS-based interface to make the model user-friendly. Backend functions are supported by LangChain and CTransformers modules for proper communication. It is totally an offline-working model that minimizes reliance on servers or even internet access in favor of increased security. This pre-training will have been extensive over datasets that try to uncover all patterns while the fine-tuning is toward applications in various tasks, thereby perfecting text summarization, question answering, content generation, or even classification tasks.
Road safety is a critical concern, with traffic accidents posing significant risks to human life and infrastructure. Accidents are considered one of the most serious problems for human population and cause great disruption to traffic flow worldwide. The effects especially loss of life and disruption of traffic could be addressed by timely detection of these events in real time and notifying relevant authorities. The goal of this proposal is to research on how to design and implement Automated Accident Detection and Notification System by using modern day image analysis and deep learning tools like CNN. Using cameras as input, the system detects unusual vehicle dynamics such as sudden stops and irregular vehicle movements that are precursors of an impending accident. In the case where additional learning is required to classify events, the proposed solution incorporates the use of OpenCV to preprocess and retrieve motion information from video sequences and a set of CNN models to classify the learned features of the encapsuled event video related to an accident. The system presented progresses the new studies in which, through the use of video, irregularities in traffic flows are quantified. Borrowing techniques of motion estimation like the Farneback Optical Flow and adaptive thresholding for anomaly detection, a solution to the problems is presented in the form of deep learning models. The objective is for accuracy though at low computation making it deployable in different settings like highways and urban intersections. The final goal of the project is improving road safety in general by fast-tracking responses during emergencies and limiting the aftermath of traffic accidents.
Anomaly Detection in Network Traffic Using Machine Learning Techniques
It has been nothing less than exponential growth in the last two decades, and with this growing Internet has come unprecedented connectivity, significantly increasing the number of cyberattacks. Zeroday attacks have always challenged traditional signature-based detection techniques, which is why anomaly-based detection techniques have become increasingly important for identifying any anomalies in normal network behavior. Key features were selected using the Random Forest Regressor. Seven machine learning algorithms are tested in this experiment.
Unmasking Employment Scam: An Automated System to Detect Fraudulent Job Postings
An increased fraudulent job posting continues to pose a grave threat to the job seeker who is more or less susceptible to financial and personal risks. The paper takes this issue to solve it with the help of Employment Scam Aegean Dataset (EMSCAD) using the powerful machine learning algorithm called XGBoost, for classification of jobs posted as real or fake ones, which, to a good approximation, will show scams around near 98% with robust computational efficiency. For this reason, it will certainly be of service in high volumes. With the implementation of the solution in a website for jobs, the purpose of the solution will attempt to improve security for job markets and prevent victims from being misguided while applying.
Predicting Employee Layoffs With Machine Learning : A Social Network and Data Mining Approach
Uniform Detection Using Deep Learning applies computer vision techniques to identify and classify uniforms like identification cards through CNNs. ID cards are important for the proper identification of persons in any organization, making it easier and fast to determine their association with the institution. This paper focuses on detecting and authenticating individuals along with their respective ID cards. By determining whether a person is wearing their assigned ID card, the system ensures that only authorized individuals gain access to the institution's premises. It also verifies the individual’s affiliation, enhancing security and minimizing the risk of unauthorized entry. Besides security, the system includes an attendance tracking feature, allowing educational institutions to manage and monitor attendance records efficiently. The automation of this process reduces manual intervention, eliminates errors, and improves operational efficiency. The real-time detection and authentication capabilities make the system an invaluable tool for environments where ID cards are mandatory. This solution improves security while making everyday business processes much easier. Future expansion may include sophisticated tracking features and interfaces with other security systems, which makes it a complete tool for efficient and secure management.
Implementing Automated Attendance System Using Face Recognition
This paper introduces a method for verifying individuals through facial recognition. Images are collected during the registration process and for live verification, using OpenCV to enhance data security. Live facial data is compared to stored records with the help of the DeepFace library for accurate identification. In case of mismatches, a message is sent to the administrator via Twilio API. Participation records are updated in an Excel file, marking individuals as present or absent based on verification. This method offers an efficient and automated way to manage participation records. It combines instant notifications, real-time data updates, and cloud-based communication to improve performance. By utilizing advanced tools such as deep learning and computer vision, this project highlights how modern technologies can streamline processes and ensure accurate and dependable user validation.
Garbage Detection Algorithm Based on Deep Learning
Enabling automation technologies such as garbage detection can drastically improve waste management. Understanding the importance of waste disposal management, this paper provides a detailed project on the deep learning approaches for detecting garbage, specifically the Mask R-CNN model through the Tensor Flow framework. Given that Mask R-CNN is the extension to Faster R-CNN, instead of just object detection, it offers instance segmentation too. In this case, the image can segment garbage into multiple types. This was modeled using COCO as the base model. The model will be able to reuse the information from previous learnings and apply it to the new context of garbage detection. The system has shown its potential to locate and cut rubbish even in difficult circumstances which can only be an asset in the automation of waste disposal systems.
A Comparative Analysis of Neural Architecture Search Techniques for Efficient Model Design
This study presents a comprehensive comparative analysis of four prominent Neural Architecture Search (NAS) techniques—NASNet, AmoebaNet, DARTS, and ProxylessNAS—with a focus on evaluating their effectiveness in designing efficient neural network models. By conducting experiments on the CIFAR-10 and ImageNet datasets, we assess these methods across several key metrics, including model accuracy, search time, computational cost, and energy consumption. The results reveal significant trade-offs among the techniques, with NASNet achieving the highest accuracy but at the cost of increased computational resources and energy usage. DARTS, on the other hand, demonstrates remarkable efficiency in terms of search time and resource utilization, albeit with a slight reduction in accuracy. This analysis highlights the importance of choosing a NAS method that aligns with the specific needs of the application, whether it be maximizing performance or optimizing for resource constraints.
Evaluating Deep Reinforcement Learning Models in Automated Trading Systems
This research presents a comprehensive evaluation of Deep Reinforcement Learning (DRL) models— specifically, Deep Q-Network (DQN), Deep Deterministic Policy Gradient (DDPG), and Proximal Policy Optimization (PPO)—in the context of automated trading systems. The study compares these models across critical performance metrics, including cumulative returns, Sharpe ratio, maximum drawdown, and the number of profitable trades, to assess their effectiveness in dynamic and complex financial markets. Our findings indicate that PPO outperforms DQN and DDPG in terms of both profitability and risk management, achieving the highest cumulative return and the best risk-adjusted performance. DDPG also demonstrates strong potential, particularly in handling continuous action spaces, while DQN shows effectiveness in simpler, discrete decision-making environments. These results underscore the capability of DRL models to enhance automated trading strategies by adapting to evolving market conditions and optimizing long-term returns.
Evaluating The Scalability of Distributed Neural Networks in High-Performance Computing
This study investigates the scalability of distributed neural networks (DNNs) in high-performance computing (HPC) environments, focusing on the comparative analysis of horizontal and vertical scaling methods. By distributing neural network training across multiple nodes and upgrading individual nodes, we assess key metrics such as training time, speedup, efficiency, and resource utilization. Our experimental results demonstrate that horizontal scaling significantly reduces training time but introduces challenges in efficiency due to communication overhead and synchronization costs. Conversely, vertical scaling offers improved resource utilization and maintains high efficiency, though its scalability is constrained by hardware limitations. A hybrid approach, combining both scaling strategies, is shown to optimize performance by balancing resource utilization and computational efficiency. These findings provide valuable insights into optimizing distributed neural network training, highlighting the trade-offs and potential of different scaling methods in HPC settings.
Evaluating AI Algorithms for Sentiment Analysis in Customer Feedback Systems
This research evaluates various AI algorithms for sentiment analysis in customer feedback systems, focusing on both traditional machine learning and advanced deep learning models. We analyzed the performance of Support Vector Machines (SVM), Naive Bayes, Decision Trees, Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks using a dataset of 100,000 customer feedback entries. The evaluation metrics included accuracy, precision, recall, F1 score, processing time, and scalability. Our results indicate that deep learning models, particularly CNNs and LSTMs, outperformed traditional machine learning models in terms of accuracy and recall, with LSTMs achieving the highest overall performance. However, these models also required significantly more processing time and showed varying scalability. In contrast, traditional models like Naive Bayes and Decision Trees demonstrated faster processing times and higher scalability but with lower accuracy. This study provides a comprehensive comparison of these algorithms, offering valuable insights into their effectiveness and practical applicability in real-time sentiment analysis.
Hybrid CNN-LSTM Models for Traffic Flow Prediction in Smart Cities
In this research, we explore the effectiveness of hybrid CNN-LSTM models for traffic flow prediction in smart cities, comparing their performance against traditional statistical methods, classical machine learning algorithms, and standalone deep learning models. Traffic flow prediction is critical for optimizing urban transportation systems, yet it poses significant challenges due to the complex and dynamic nature of urban traffic patterns. The proposed hybrid CNN-LSTM model leverages the spatial feature extraction capabilities of Convolutional Neural Networks (CNNs) and the temporal sequence modeling strength of Long Short-Term Memory (LSTM) networks. Experimental results demonstrate that the hybrid model outperforms all other methods, achieving the lowest Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), alongside the highest R² score. These findings highlight the potential of hybrid CNN-LSTM architectures in enhancing real-time traffic management and smart city planning
Comparison of Online Learning Vs Batch Learning in Predictive Maintenance Systems
This study presents a comparative analysis of batch learning and online learning methodologies in predictive maintenance (PdM) systems. The objective is to evaluate the performance, resource efficiency, and adaptability of these two approaches in the context of equipment failure prediction. Batch learning models, trained on comprehensive datasets, achieved an accuracy of 91.2% but were characterized by longer prediction latencies (6.0 seconds), higher memory usage (4.0 GB), and increased CPU consumption (70%). Conversely, online learning models, which continuously update with new data, demonstrated a slightly lower accuracy of 88.5%, yet they excelled in real-time performance with a latency of 2.0 seconds, reduced memory usage (1.5 GB), and lower CPU utilization (40%). Additionally, online learning models showed greater adaptability, achieving a 90.3% adaptability rate, and required significantly less training time (45 minutes) compared to the 12 hours needed for batch learning. This study highlights the trade-offs between batch and online learning approaches, offering valuable insights for optimizing predictive maintenance systems where real-time data processing and resource efficiency are crucial.