Osama AlQahtani, College of Engineering and Computer Science, University of Jazan, Jazan, Saudi Arabia
Ultra-Reliable Low Latency Communications (URLLC) applications in 5G and beyond net- works demand unprecedented performance levels with sub-millisecond latencies and 99.999% reliability. While AI-based traffic classification has emerged as a critical enabler for intelligent network management, existing approaches focus primarily on algorithmic improvements without adequately addressing the prac- tical constraints of edge deployment environments. This paper presents a novel theoretical framework for edge-native URLLC traffic classification that systematically bridges the gap between AI model capabil- ities and real-world deployment limitations. The framework comprises four interconnected components: resource constraint modeling, latency decomposition analysis, reliability-performance trade-off optimiza- tion, and edge-cloud orchestration principles. A systematic four-phase methodology guides practitioners through system characterization, model selection and optimization, deployment strategy determination, and performance validation. Theoretical case study analysis across three representative scenarios—5G base station edge computing, industrial IoT gateways, and vehicular edge computing nodes—demonstrates the framework’s effectiveness in diverse deployment environments. Framework projections indicate potential achievement of URLLC targets with 0.7ms average latency and 99.997% reliability for high-resource sce- narios, and 400μs latency with 99.9993% reliability for constrained industrial applications. The theoretical analysis shows consistent resource optimization potential of 45-78% while maintaining acceptable classifi- cation accuracy. This work provides the foundation for systematic deployment of AI traffic classification systems in edge environments, offering both theoretical rigor and practical guidance for next-generation URLLC applications.
Nikitha Merilena Jonnada, University of the Cumberlands, USA
In this paper, the author discusses about the significance of network security as it continues to grow and how digital infrastructure is becoming an integral part of our daily life. Modern networks are facing more threats like the malware, ransomware, phishing, advanced persistent threats (APTs), and vulnerabilities in the cloud and Internet of Things (IoT) environments. These threats compromise data confidentiality, integrity, and availability, posing risks to individuals, businesses, and the government. This paper presents a comprehensive review of contemporary network security principles, technologies, and practices, while proposing an Integrated Network Defense Framework (INDF) that combines technical, administrative, and policy-driven measures for holistic protection. The paper examines the foundational CIA triad (confidentiality, integrity, availability), common attack vectors, and modern defense mechanisms, such as firewalls, intrusion detection and prevention systems, endpoint detection and response, and encryption methods.
Network Security, Cybersecurity, Zero Trust, Artificial Intelligence, Threat Mitigation.
Amirmasoud Soltanzadeh and Zbigniew Dziong, Department of Electrical Engineering, Montréal, Québec, Canada
The increasing use of sensor-based wireless communication systems, such as WSN, provides numerous benefits; however, some issues exist in their utilization, most notably regarding energy efficiency. In previous times, multiple approaches existed to address the energy issue in WSNs; however, it is necessary to adapt efficient approaches to overcome the remaining issues. This study utilizes an advanced artificial intelligence algorithm, clustering, and a routing process to enhance the performance of WSN intelligently. The algorithm used for clustering is PSO-mutation, which is employed to choose CHs. Golden eagle optimization is used for route optimization within the CHs to minimize energy expenditure and enhance the WSN’s lifetime. Matlab tool to simulate and evaluate with AI techniques based on a genetic and predictive coding theory algorithm, as well as the traditional Leach-CE-based routing protocol for WSNs. Performance metrics include energy consumption, the number of dead nodes, throughput, and delay. The proposed model demonstrates significant improvements over the Leach-CR model, thereby justifying its validity.
Golden eagle optimization (GEO), Particle swarm optimization-mutation (PSO-Mutation), Energy efficiency routing protocol (EERP). Wireless sensor networks (WSNs).
Bornoma Halima, Farouk Hafsa Mu’azu, Okonta Ehijesumuan, Diadie Sow, Ignace Djitog and Ekpe Okorafor, Nigerian British University, Nigeria
Bone marrow diseases are illnesses that affect the bone marrow of an individual. The bone marrow is a mesh-like organ situated inside the bones of a human being entirely in charge of producing blood and all blood components i.e. lymphocytes, erythrocytes, platelets and plasma. Any disease affecting the bone marrow affects the production of blood which can lead to loss of blood or cancers which eventually lead to death. This paper proposes a new web-based application integrated with convolutional neural networks algorithm, a machine learning approach, to automate an early diagnosis of bone marrow diseases without much hassle contrary to common manual processing which is exceedingly labor-intensive and costly. While the dataset was thoroughly examined, features that fit in with patient characteristics living with sickle cell disease in Nigeria were extracted to carry out the analysis. The dataset contains 11 classes of different bone marrow disease cell types, and a number of performance measures such as area under the curve (AUC), precision, and recall were generated and analyzed with the following results 98.38%, 87.12%, and 77.12% respectively. In terms of diagnosing sickle cell diseases with patients in Nigeria, the proposed model surpassed all existing learning models. The resulting model was saved in a specific file format then successfully imported into the developed web portal for instant analysis and wider access by authorized personnel.
Machine Learning, Convolutional Neural Networks (CNN), Bone-Marrow Disease, Hematology, Smear, Prediction, Web Application.
Yousef Mehrdad Bibalan1, Behrouz Far1, Mohammad Moshirpour2, and Bahareh Ghiyasian3, 1 University of Calgary, Canada, 2 University of California, Irvine, USA
Work-in-Progress (WiP) prediction is critical for predictive process monitoring, enabling accurate anticipation of workload fluctuations and optimized operational planning. This paper proposes a retrieval-augmented, multi-agent framework that combines retrieval-augmented generation (RAG) and collaborative multi-agent reasoning for WiP prediction. The narrative generation component transforms structured event logs into semantically rich natural language stories, which are embedded into a semantic vector-based process memory to facilitate dynamic retrieval of historical context during inference. The framework includes predictor agents that independently leverage retrieved historical contexts and a decision-making assistant agent that extracts high-level descriptive signals from recent events. A fusion agent then synthesizes predictions using ReAct-style reasoning over agent outputs and retrieved narratives. We evaluate our framework on two real-world benchmark datasets. Results show that the proposed retrieval-augmented multi-agent approach achieves competitive prediction accuracy, obtaining a Mean Absolute Percentage Error (MAPE) of 1.50% on one dataset, and surpassing Temporal Convolutional Networks (TCN), Long Short-Term Memory (LSTM), and persistence baselines. The results highlight improved robustness, demonstrating the effectiveness of integrating retrieval mechanisms and multi-agent reasoning in WiP prediction.
Predictive Process Monitoring, Work-in-Progress, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Framework
Allen Chen and Garret Washburn, California State Polytechnic University, USA
Intoxicated operation of motor vehicles is undoubtedly one of the most prominent death causes in the United States and around the world. While purposeful operation of a vehicle under the influence is obviously part of that problem, a big part of it is the ignorance of intoxication status by the operator under the influence. The SpeechSense system aims to remedy this issue by providing a completely free and convenient way for individuals to identify their intoxication status by simply recording themselves reading a script and waiting a few seconds for their result.vThe core component of the SpeechSense system is the custom trained SVC model on sober and intoxicated samples, able to accurately classify audio of the user as sober or intoxicated [10]. The framework built around this model for the functionality of the complete system includes a mobile application built using the Flutter framework and a backend server using Flask that pairs with a Firebase database for storage [11]. The majority of challenges faced during the development of this system included the management and movement of data from user to server to database and back, as well as the processing time of the machine learning model to create a prediction on the audio sample. To ensure the quickness and consistency of the SpeechSense system, multiple experiments were performed to find the model accuracy as well as the uploading time of the audio recordings. The results of these experiments proved positive, and reassured of the quality of the SpeechSense system. Ultimately, the SpeechSense system is a positive solution to resolving the ignorance around one’s intoxication status as it provides a quick, easy, and consistent way for a user to discover if they are sober or not. With the SpeechSense system, many accidents can be avoided, as it also is completely free and a very accessible solution.
Machine Learning, Artificial Intelligence, Mobile Application, AI System, Intoxication, Drunk or Sober
Ye Li, University of Canberra, Australia
Phishing persists because current email architectures cannot guarantee individual-level sender authenticity. Domain-based defenses (SPF, DKIM, DMARC) validate organizational infrastructure yet admit execution paths where messages from compromised accounts are accepted without verifying the named individual. We introduce authenticity completeness, a global property requiring that every execution of the email transmission process terminate either in verified authenticity or explicit rejection, thereby forbidding any form of unverified acceptance. We formalize this property via a state-machine model, prove that domain-level mechanisms are structurally incapable of satisfying it, and present a document-oriented digital signature scheme that uses ephemeral per-message keys. The design satisfies four reinforcing properties—proactive defense, individual-level authenticity, transparency of operation, and localized deploy ability—and we prove that it achieves authenticity completeness. This yields the first rigorous end-to-end guarantee against individual level impersonation within email systems.
Email Authenticity, Phishing, Digital Signatures, Authenticity Completeness.
Anna Forster1, Carlo Lucheroni1 and Stefan Gürtler2, 1University of Camerino, Camerino, MC, Italy, 1University of Applied Science of North-west Switzerland, Olten, Switzerland
The Digital Out-of-Home (DOOH) advertising industry still struggles to achieve precision in getting audience attention focused, relying mainly on location-based targeting that neglects critical consumer context. This paper presents an Ontology-Enhanced Multimodal Targeting System designed to bridge this gap. The core innovation of the proposal is a domain-specific ontology that integrates anonymized mobile Global Positioning System (GPS) data and real-time contextual inputs (e.g., time, weather). This semantic framework enables the operational advertisement targeting system to move beyond data correlation, inferring audience insights that are synthesized into a composite business-centric Recommendation Score (MR). A field experiment validates the system’s performance against a traditional baseline, demonstrating a 220% relative uplift in relevant advertisement impressions. The presented findings establish a quantifiable and privacy-conscious methodology for optimizing DOOH advertising delivery, possibly positioning the proposed ontology-enhanced approach as a foundation for privacy-conscious contextual personalization.
Advertising, digital out-of-home; mobile GPS; ontology; contextual targeting; audience.
CheukLaam Liang1, Yu Sun2, Shuo Chen3, 1USA, 2California State Polytechnic University, Pomona, CA 91768, 3Rutgers University–New Brunswick, New Brunswick, NJ 08901
Small bakeries face competitive disadvantages due to expensive digital ordering systems, with 30% closing in the last decade partly due to digital adaptation challenges. This paper presents a mobile e-commerce application built with Flutter and Firebase that enables affordable digital storefronts for local bakeries [1]. The system integrates three major components: Firebase Authentication for user management, Firestore-based product browsing with intelligent search across multiple collections, and singleton-pattern cart management with real-time order processing [2]. Implementation challenges included maintaining cart consistency across navigation, implementing efficient multicollection search, and ensuring secure authentication with graceful error handling. Experimental testing demonstrated 98% search accuracy for standard queries, 267ms average response times, and 98.25% cart consistency across complex navigation scenarios. Comparison with traditional web systems, generic e-commerce platforms, and native development revealed cost reductions of 90% while maintaining superior mobile user experience. This solution democratizes e-commerce technology for small businesses, enabling local bakeries to compete effectively in the digital marketplace while preserving artisanal food culture.
E-Commerce, Flutter Development, Firebase Integration, Small Business Digitalization
Prakhar Rai, Indian Institute of Technology Guwahati, India
Natural Language Processing (NLP) for Big Data has become one of the most challenging frontiers in computer science and engineering. The exponential growth of heterogeneous, multi-modal, and noisy data has pushed NLP beyond classical statistical methods into the realms of distributed deep learning, knowledge-enhanced reasoning, and quantum-inspired architectures. This paper critically analyzes the integration of NLP with Big Data ecosystems, highlighting the interplay of scalability, semantic representation, and distributed optimization. We propose an advanced taxonomy of techniques, comparative analyses of architectures, and future research directions such as neurosymbolic fusion, federated multi-lingual embeddings, and quantum variational NLP models. Our aim is to stimulate the development of next-generation NLP systems capable of thriving in petabyte- scale, privacy-aware, and real-time environments..
NLP for Big Data, Deep Learning, Semantic Systems, Quantum NLP, Dis- tributed Architectures.
Satsuki Maeda, Bismark Kweku Asiedu Asante, and Hiroki Imamura, Soka University of Japan, Japan
Action recognition has many practical applications, but the task still faces significant challenges. A major challenge is the variation in human action poses across different viewpoints, which complicates determining the ideal pose for action recognition. To address these viewpoint-invariant issues, we propose a pose normalization approach combined with object-based action recognition to classify actions in videos. In this method, the normalized pose is compared with a reference pose to identify the action being performed. The objective of this research is to develop a three-dimensional (3D) object-associated action recognition framework that leverages the stereo camera’s ability to capture accurate distance information. This approach offers three main advantages: (1) action recognition that incorporates object context, (2) resolving occlusion problems, and (3) improving recognition accuracy through precise distance information. Experimental results show that our proposed approach achieves 70% classification accuracy across ten selected action categories, independent of viewpoint or camera angle.
Object Recognition, Behavior Recognition, AI, Stereo Camera, Active Detection.
Takaki Ohoka, Bismark Kweku Asiedu Asante and Hiroki Imamura, Soka University of Japan, Japan
In the interior and exterior industry, professionals goes through a painstaking tasks to plan and create functional and aesthetically pleasing indoor and outdoor spaces by selecting materials, colors, finishes, furnitures, and landscaping elements to achieve a harmonious and appealing environment for occupants and visitors. Recently, technological advancements has provided simulation environmemts using Virtual Reality (VR) or Augment Reality (AR) to easily perform these tasks. However the challenge of seamlessly switching between interior and exterior environments while simulating in multi user platforms is still a challenge. This is as a result of each of the spaces are simulated differently in VR space. To address this challenge, our research focus on developing a seamless simulation system for interior and exterior design in a VR space. Our system demonstrate that users can easily switch between interior and exteriors designs in the VR spaces in the multi user platforms as well.
Virtual Reality, interior design, exterior design, seamless simulations. 3D modelling, Data Visualization
Prakhar Rai, Indian Institute Of Technology Guwahati
The convergence of 3D imaging, biomedical applications, and secure data transmission presents a formidable challenge at the intersection of digital image processing and pattern recognition. This paper proposes a novel, end-to-end hybrid framework for the secure acquisition, processing, and transmission of 3D biomedical reconstructions. The core of our method lies in a unique combination of several advanced themes: we first employ a multidimensional signal processing pipeline for 3D and surface reconstruction from multi-view stereo image acquisition systems, addressing the critical issue of motion detection and illumination and reflectance modeling in clinical environments. The reconstructed 3D model then undergoes a dual-process: (i) An adaptive neuro-filtering stage, implemented on an embedded system (DSP Implementation), performs real-time array signal processing and higher-order spectral analysis to denoise and enhance the model, a task particularly crucial for low-signal medical image analysis. Concurrently, (ii) a deep learning-based watermarking algorithm, designed within a constraint processing framework, intricately embeds patient metadata and authentication signals directly into the spectral coefficients of the 3D mesh, ensuring data integrity and security without compromising visual fidelity for computer vision & VR-based visualization. This nonlinear approach to simultaneous enhancement and encryption is a significant departure from sequential methods. Our results, validated on a novel biomedical imaging dataset of anatomical phantoms, demonstrate a 23% improvement in reconstruction accuracy (PSNR) over state-of-the-art structure from motion techniques, a 40% reduction in signal noise, and unparalleled robustness of the watermarked data against common signal processing attacks, thereby establishing a new benchmark for secure, high-fidelity 3D telemedicine applications.
3D Surface Reconstruction, Adaptive Neuro-Filtering, Deep Watermarking, Multidimen sional Signal Processing, Biomedical Imaging, Embedded DSP, Secure Telemedicine
Derek Li1, Austin Amakye Ansah2, 1USA, 2California State Polytechnic University, Pomona, CA 91768
Traditional basketball training relies on feedback from coaches and video playback, which can be delayed and inconvenient for solo practice. This paper introduces HoopLab, a cross-platform mobile application designed to provide immediate, comprehensive analysis of basketball shots. The app, built with Flutter, utilizes a custom-trained YOLO object detection model to analyze user-submitted videos. It features two distinct analysis modes: a backboard view that confirms if a shot is successful by tracking the balls path through the rim, and a side-view that evaluates a shots quality by comparing its actual path to a computationally generated optimal trajectory. The underlying YOLOv11 model was trained on a dataset of over 8,200 images, achieving a mean average precision (mAP@0.5) of 94.72% and demonstrating high performance in identifying key objects like the ball, rim, and player [10]. HoopLab offers players a powerful tool for instant, data-driven feedback to accelerate skill improvement.
Computer vision, AI, mobile, basketball, Flutter
Kyle Chuang1, Jonathan Sahagun2, 1USA, 2California State Polytechnic University, Pomona, CA 91768
Drowning represents a significant global public health crisis, with approximately 300,000 deaths occurring annually worldwide, disproportionately affecting children under the age of five [1]. Despite the presence of supervising adults in 80% of child drowning incidents, the silent and rapid nature of drowning events often prevents timely intervention. This paper presents SafeSwim, an intelligent wearable drowning detection and alert system that integrates accelerometer-based motion detection, underwater acoustic signaling, and mobile application technology to provide real-time alerts to caregivers. The system comprises three primary components: a wearable device utilizing an Adafruit RP2040 Prop-Maker Feather with a LIS3DH accelerometer that detects abnormal motion patterns and generates low-frequency acoustic signals, a Raspberry Pi-based receiver equipped with an Aquarian Audio H2d hydrophone that processes underwater acoustic signals using Fast Fourier Transform analysis, and a Flutter-based mobile application connected to Firebase for device management and alert delivery. Experimental evaluation demonstrated reliable detection of simulated drowning events with minimal false positives. The system addresses limitations of existing approaches by providing non-intrusive monitoring that does not impede swimming ability while ensuring rapid alert transmission to nearby adults.
Drowning, Protection, IoT, Hydrophone, Underwater Acoustics
Ye Li, University of Canberra, Australia
Phishing persists because current email architectures cannot guarantee individual-level sender authenticity. Domain-based defenses (SPF, DKIM, DMARC) validate organizational infrastructure yet admit execution paths where messages from compromised accounts are accepted without verifying the named individual. We introduce authenticity completeness, a global property requiring that every execution of the email transmission process terminate either in verified authenticity or explicit rejection, thereby forbidding any form of unverified acceptance. We formalize this property via a state-machine model, prove that domain-level mechanisms are structurally incapable of satisfying it, and present a document-oriented digital signature scheme that uses ephemeral per-message keys. The design satisfies four reinforcing properties—proactive defense, individual-level authenticity, transparency of operation, and localized deploy ability—and we prove that it achieves authenticity completeness. This yields the first rigorous end-to-end guarantee against individual level impersonation within email systems.
Email Authenticity, Phishing, Digital Signatures, Authenticity Completeness.
Milind Jagre, Jia Huang, Dayvid V. R. Oliveira, Zhinan Cheng, Babak Seyed Aghazadeh, Puja Das, Chris Alvino, Jinda Han, Kailash Thiyagarajan, USA
In search and recommendation systems, predictive models often suffer from temporal instability when certain features introduce volatility in output scores. This instability reduces reliability and user experience - especially in multi-stage systems where consistent predictions are critical. We introduce Fortress, a general framework that enhances model stability and accuracy by identifying and pruning features that cause inconsistent scores over time. Fortress leverages temporally partitioned historical snapshots to capture score fluctuations for the same entity and follows a four-step process: (1) collect snapshots, (2) detect unstable predictions, (3) isolate instability-inducing features, and (4) retrain models with stable features. While semantic features from LLMs improve generalization, they often lack full coverage; engagement features add predictive power but introduce volatility. Fortress suppresses this instability while preserving value, yielding more stable and accurate models. Validated on a query-to-app relevance model in a large marketplace, Fortress shows notable gains in stability and PR-AUC.
recommendation system, relevance stability, feature pruning, information retrieval .
Ala Karajeh1 and Rasit Eskicioglu2, 1Independent Researcher, Winnipeg, Canada, 2Department of Data Science and Analytics, Atlas University, Turkey
Older patients typically exhibit different traits compared to younger ones and often have multiple comorbidities, such as diabetes and cardiovascular diseases, which complicate their severity assessment at emergency care facilities. This research utilizes two clinical databases from Beth Israel Deaconess Medical Center to explore the clinical characteristics of this demographic based on triage information, triage scores, and disposition outcomes. Additionally, a machine-learning model is proposed to predict likely disposition outcomes, specifically whether patients are hospitalized or discharged at the end of their emergency visit. This model could be instrumental in proactively managing this critical patient segment and improving their health outcomes.
Emergency Medicine, Hospitalization Prediction by Machine Learning, Emergency Older Patients Classification, and Emergency Older Patients Data Analytics.
Christoph Heike, Greetmate Inc., Irvine, California, United States
Voice AI applications, such as customer service chatbots, automated phone systems, and virtual receptionists, face a fundamental challenge known as “turn detection”, which means accurately identifying when a user has finished speaking and when the assistant should respond. Traditional systems typically rely on Voice Activity Detection (VAD), which interprets short pauses as signals of turn completion. However, this approach often fails in natural conversation, where users hesitate, think aloud, or provide structured information over multiple fragments. In this paper, we introduce an advanced turn detection mechanism that leverages semantic understanding, conversational context, and linguistic cues to significantly improve interaction accuracy and naturalness. Our method is based on a novel parallel prompting architecture, in which a dedicated turn detection language model runs in parallel with the assistant’s response generation process. By evaluating speech content in real time, the system can distinguish between genuine end-of-turns and mid-utterance pauses without increasing response latency. Qualitative evaluation demonstrates that our method substantially reduces false interruptions, enhances dialogue fluidity, and advances the goal of human-centered, meaning-aware conversational AI.
voice AI, artificial intelligence, parallel prompting, voice agents, turn detection
Zhansheng Huang1, Garret Washburn2, 1USA, 2California State Polytechnic University, Pomona, CA 91768
Children face dual risks during school activities: safety concerns from inadequate supervision and health risks from carrying heavy backpacks exceeding recommended weight limits. BackTracked addresses both through an integrated IoT system combining GPS location tracking with real-time weight monitoring [9]. The solution comprises Particle Boron firmware interfacing with GPS and pressure sensors, a cloud backend for data processing and secure storage, and a cross-platform Flutter mobile application for parental monitoring [10]. Key technical components include NMEA sentence parsing for location extraction, analog pressure sensing with calibrated weight mapping, and token-based authentication for data security. Experimental evaluation demonstrated 3.2 meters outdoor GPS accuracy and 2.98% weight measurement error, validating system reliability for intended use cases. Compared to existing solutions, BackTracked uniquely combines health and safety monitoring, provides cellular connectivity independent of Wi-Fi, and supports both iOS and Android platforms. The system enables parents to monitor their childrens location and backpack weight through a single, comprehensive solution.
IoT Monitoring, GPS Tracking, Weight Sensing, Child Safety Systems.
Jamilynn Mackenzie Modelo and Mervis Encelan, College of Information Systems and Technology Management, Information Technology Department, Pamantasan ng Lungsod ng Maynila, Manila, Philippines
Menstrual health plays a critical role in an individual’s overall well-being, influencing physical, emotional, and reproductive health. Tracking menstrual cycles provides valuable insights into hormonal patterns, allowing for early detection of disorders such as hormonal imbalances. However, most period tracking applications are designed for users with regular cycles, often resulting in inaccurate predictions for individuals with irregular menstruation or Polycystic Ovary Syndrome (PCOS). This study proposes the development of Evelune, a menstrual and PCOS management mobile application that integrates KMeans Clustering and a rule-based prediction system to improve menstrual phase prediction and health awareness. The K-Means algorithm organizes user input into groups based on similarities in symptom patterns, which are then analyzed through a rule-based system that predicts menstrual phases and provides personalized health insights. Evelune further implements data privacy features such as AES-256 encryption, Firebase Authentication, HTTPS communication, and Multi-Factor Authentication (MFA) to ensure user data protection. The system is evaluated using ISO/IEC 25010:2023 quality standards, emphasizing functional suitability, usability, reliability, and security. Evelune aims to empower Filipino users, particularly those managing PCOS, by promoting reproductive health literacy, accurate symptom tracking, and privacy-conscious self-care practices
Menstrual Health, Polycystic Ovary Syndrome (PCOS), K-Means Clustering, Rule-Based System, Mobile Health Application, Data Application, Data Privacy, Reproductive Health Literacy.
Nairila Ge1, Rodrigo Onate2, 1USA, 2California State Polytechnic University, Pomona, CA 91768
The e-commerce industry’s rapid growth has created opportunities for entrepreneurs, yet significant barriers remain including complex product selection, technical website development, and search engine optimization expertise requirements [8]. Traditional approaches fragment these critical functions across multiple specialized tools, creating workflow inefficiencies and demanding diverse technical skills. This research presents an integrated AI-powered platform that combines machine learning-based product recommendation, automated dropshipping website generation, and SEO-driven analytics into a unified system designed for non-technical users. The platform employs collaborative filtering algorithms to analyze market data and generate data-driven product recommendations, achieving user satisfaction scores of 4.58/5.00. Integration with Shopify’s API enables automated website generation, reducing launch time from 2-4 weeks to under 24 hours while maintaining professional standards. Natural language processing automatically generates SEO-optimized content, democratizing search optimization traditionally requiring specialist expertise. Implementation challenges addressed include handling sparse data in recommendation systems through hybrid approaches, balancing template efficiency with brand differentiation in automated generation, and providing actionable SEO guidance to non-experts through predictive analytics [9]. A user satisfaction survey (n=50) demonstrated strong reception across all dimensions (mean=4.32/5.00), with 68%-time savings compared to traditional methods and 84% of users successfully launching within 24 hours. Comparative analysis against traditional product research tools, website builders, and SEO platforms reveals that the integrated approach eliminates workflow friction and knowledge barriers, making data-driven e-commerce entrepreneurship accessible to individuals without technical or marketing expertise while maintaining professional quality standards.
AI-powered e-commerce, Automated website generation, Product recommendation systems, SEO optimization
Malcolm Qiu1, Moddwyn Andaya2, 1USA, 2California State Polytechnic University, Pomona, CA 91768
This simulations purpose overall helps complete the requirements of a proper design. With its features of imposing problems, the user must adjust to help improve the safety and usability of their ideas, for example making sure that the NPC will always reach the exit no matter what and the features and objects in a building do not prevent evacuation in case of emergencies. One disaster imposed so far is in the case of a fire,the NPC must be able to leave safely and without casualties. The user will test their ideas by copying their layout onto the 3d simulation using the build blocks/furniture’s provided by the simulation and then placing NPCs inside. Eexperiments’ involving NPCs show that they recognize and act on special limitations most of the time and will always head towards an available exit if they can reach it and stand still if there is none. However, cannot scale heights both up and down without stairs. When that is patched the user will be able to work with similar limitations of humans thus making sure that safety is ensured with realistic logic. By testing their designs through the simulation, it helps produce and revise a more ideal final draft of a concept by figuring how to fix/expand on the starting layout.
Emergency evacuation, Simulation design, NPC behavior modeling, Architectural safety
Copyright © DSA 2025
