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Articles 361 - 390 of 3697
Full-Text Articles in Computer Sciences
Holistic Correlation Measure For Enhanced Encapsulation Of Trait Heterogeneity And Discovery Of Co-Expression, Zachary Valleroy
Holistic Correlation Measure For Enhanced Encapsulation Of Trait Heterogeneity And Discovery Of Co-Expression, Zachary Valleroy
Theses
Large-scale, high-dimensional data analyses can be computationally prohibitive due to combinatorial explosion of the search space for finding complex patterns; a viable alternative is network modeling for abstraction and quantifying intrinsic data associations. Prominent network analysis methods furnish frameworks for model synthesis and validation but rely on standard correlation measures impaired by semi-supervised biases, latent heterogeneity, and uneven discretization techniques. Here we investigate a holistic measure for encapsulating data heterogeneity for enhanced efficacy of revealing complex patterns through network analysis. Our unique correlation metric, K-medoids Utility for Duo Original Similarities (Kudos), exhaustively factors real-valued analyte data to compute …
Vision-Language Integration For Enhanced Locomotion Mode Prediction, Ehsan Ahmadi
Vision-Language Integration For Enhanced Locomotion Mode Prediction, Ehsan Ahmadi
LSU Master's Theses
Wearable exoskeletons offer significant potential in enhancing human mobility in industrial environments. However, their adaptability to dynamic, task-intensive settings presents challenges, especially in accurately predicting locomotion modes such as ladder climbing, stair navigation, low-space movement, and obstacle navigation. This research proposes a multimodal framework that integrates visual data and speech commands to improve locomotion mode prediction in unpredictable environments. Multimodal data was collected using smart glasses, capturing both the user’s perspective (field-of-view, FOV) and voice during locomotion tasks. State-of-the-art models—CLIP, ImageBind, and GPT-4o—process these visual and linguistic inputs to predict locomotion activities. The models were evaluated in zero-shot and fine-tuned …
Smartphone Haptics Can Uncover Differences In Touch Interactions Between Asd And Neurotypicals, Ivonne Monarca, Franceli L. Cibrian, Isabel López Hurtado, Monica Tentori
Smartphone Haptics Can Uncover Differences In Touch Interactions Between Asd And Neurotypicals, Ivonne Monarca, Franceli L. Cibrian, Isabel López Hurtado, Monica Tentori
Engineering Faculty Articles and Research
Utilizing touch interactions from smartphones for gathering data and identifying digital markers for screening and monitoring neurological disorders, such as Autism Spectrum Disorder (ASD), is an emerging area of research. Smartphones provide multiple benefits for this kind of study, including unobtrusive data collection via built-in sensors, integrated haptic feedback systems, and the capability to create specialized applications. Acknowledging the significant yet understudied presence of tactile processing differences in individuals with ASD, we designed and developed Feel and Touch, a mobile game that leverages the haptic capabilities of smartphones. This game provides vibrotactile feedback in response to touch interactions and collects …
Medilink: A Secure Blockchain Framework For Multi-Institutional Healthcare, Jorge Castillo, Qian Chen
Medilink: A Secure Blockchain Framework For Multi-Institutional Healthcare, Jorge Castillo, Qian Chen
Informatics and Engineering Systems Faculty Publications
The use of Electronic Medical Records (EMRs) in the healthcare industry has proven to be critical for storing highly sensitive information. Disseminating and protecting healthcare data poses major challenges for the current healthcare information system. Blockchain technology provides solutions to these challenges with its inherited properties, such as decentralization, immutability, and transparency. This provides a unique opportunity to improve data sharing among stakeholders. We propose MediLink, a blockchain-based framework for secure collaborative medical storage. MediLink is designed to (1) protect EMR data from cyber attacks, (2) share healthcare information of patients with different stakeholders, and (3) enable the Internet of …
Dynamic Knowledge Elicitation: Leveraging Student Feedback For Improved Language Model Distillation, Reuven Muller
Dynamic Knowledge Elicitation: Leveraging Student Feedback For Improved Language Model Distillation, Reuven Muller
Master's Theses
Large Language Models (LLMs) have significantly advanced the field of natural language processing but remain resource-intensive and impractical for many organizations. Specialist models offer a viable alternative, often developed through Knowledge Distillation (KD) techniques. However, traditional KD methods rely on predefined static datasets to elicit knowledge from the teacher model, failing to dynamically address the weaknesses of the student model during training. This research introduces two novel methods for adaptive knowledge elicitation: Feedback-Driven Question Generation and Agent-Based Targeted Question Generation. These methods iteratively expand the training dataset based on the student model’s performance, leveraging a teacher model to generate targeted …
Participatory Ethical Regulations: Risk Challenges Of Artificial Intelligence Era And Construction Of Governance Logic, Chenggang Zhang, Lu Pan
Participatory Ethical Regulations: Risk Challenges Of Artificial Intelligence Era And Construction Of Governance Logic, Chenggang Zhang, Lu Pan
Bulletin of Chinese Academy of Sciences (Chinese Version)
Participatory ethical norms emphasize the involvement of diverse stakeholders, aiming to construct a more comprehensive and balanced ethical governance framework. The rapid development of artificial intelligence (AI) technology is leading society through unprecedented transformations, significantly impacting ethical perspectives, social governance models, and the symbiotic relationship between humans and technology. The participatory ethical norms, characterized by multi-stakeholder participation, interactivity, and openness, represent a crucial pathway for addressing the challenges posed by the rapid development of AI technology. Constructing an AI governance framework based on participatory ethical norms provides solutions for the sustainable, fair, and transparent development of AI from multiple aspects …
Ethical Risks And Challenges Of Chatgpt Applications In Education, Jingbo Fan, Hui Liang
Ethical Risks And Challenges Of Chatgpt Applications In Education, Jingbo Fan, Hui Liang
Bulletin of Chinese Academy of Sciences (Chinese Version)
ChatGPT is a typical application in the field of natural language processing, with the potential to empower and revolutionize education. It can serve not only as a digital tutor for students but also as a virtual assistant for teachers, driving the transformation of student learning methods and teaching paradigms. Additionally, ChatGPT shows a wide range of applications in the research field. However, while bringing opportunities for educational development, ChatGPT also poses ethical risks and challenges to educational equity. Firstly, ChatGPT may exacerbate the digital divide, leading to unequal educational opportunities. Secondly, it presents risks such as knowledge alienation, algorithmic black-box …
Artificial Intelligence Foundation Model Risk Identification And Governance Model From Esg Perspective, Jincheng Shi, Guoyu Wang, Yingchun Wang
Artificial Intelligence Foundation Model Risk Identification And Governance Model From Esg Perspective, Jincheng Shi, Guoyu Wang, Yingchun Wang
Bulletin of Chinese Academy of Sciences (Chinese Version)
The application ecology of artificial intelligence foundation model is rapidly expanding. The environment, society, and governance are facing new challenges and opportunities. Exploring the construction of a governance framework for the development risks of foundation model has important theoretical value and practical significance for promoting the healthy and sustainable development of artificial intelligence. Based on the theories of ESG and artificial intelligence governance, this study analyzes the development benefits and typical risks of foundation model from the perspective of ESG and then constructs a risk governance framework and implementation strategies for artificial intelligence foundation models. This study shows that a …
Enlightenment Of Us Nairr To Construction Of Artificial Intelligence Innovation Ecosystem In China, Tian Jiang, Li Qian
Enlightenment Of Us Nairr To Construction Of Artificial Intelligence Innovation Ecosystem In China, Tian Jiang, Li Qian
Bulletin of Chinese Academy of Sciences (Chinese Version)
The National Artificial Intelligence Research Resource (NAIRR) of the United States is of great significance for addressing the new challenges faced by the innovation of artificial intelligence technology. For China, the experience of NAIRR provides valuable references in resource optimization allocation and efficient utilization, which helps us to overcome resource bottlenecks and promote the rapid development of artificial intelligence technology. This study analyzes the enlightenment of NAIRR to the innovation and development of artificial intelligence in China from two key aspects. The first is the innovation-driven elements, where the integration strategies of NAIRR in computing and storage power, data resource, …
Overview On Autonomous Machine Computing, Shaoshan Liu, Yiming Gan, Yinhe Han
Overview On Autonomous Machine Computing, Shaoshan Liu, Yiming Gan, Yinhe Han
Bulletin of Chinese Academy of Sciences (Chinese Version)
Autonomous machine computing, an innovative blend of algorithms, software, and cutting-edge computing hardware, is poised to be the next major paradigm shift in the global economy, following personal, mobile, and cloud computing. This study delves into the research and commercialization of the robotics industry, underscoring the critical importance of establishing a comprehensive autonomous machine computing ecosystem. This study argues that autonomous machine computing necessitates a complete ecosystem that encompasses applications, programming languages, and the foundational hardware architectures, and presents a comprehensive review of significant research contributions across these areas. Moreover, the study explores the synergy between autonomous machine computing and …
Phr-Nft: Decentralized Blockchain Framework With Hyperledger And Nfts For Secure And Transparent Patient Health Records, Huwida E. Said, Nedaa B. Al Barghuthi, Sulafa M. Badi, Faiza Hashim, Shini Girija
Phr-Nft: Decentralized Blockchain Framework With Hyperledger And Nfts For Secure And Transparent Patient Health Records, Huwida E. Said, Nedaa B. Al Barghuthi, Sulafa M. Badi, Faiza Hashim, Shini Girija
All Works
Blockchain technology holds significant promise for healthcare by enhancing the security and integrity of patient health records (PHRs) through decentralized storage and transparent access. However, it has substantial limitations, including problems with scalability, high transaction costs, privacy concerns, and intricate stakeholder access management. This study presents PHR-NFT, a novel framework that strengthens PHR privacy by utilizing Hyperledger Fabric and non-fungible tokens (NFTs) to address these issues. PHR-NFT improves privacy and communication by letting patients keep control of their medical records while permitting temporary, permission-based access by medical professionals. PHR-NFT offers a transparent solution that increases trust among healthcare stakeholders through …
Gmc-137 Iot Security Vulnerabilities And How To Improve Them, Austin D Klein, Ryeon N Naderi, Tyler J Hood, Keren Bassourou
Gmc-137 Iot Security Vulnerabilities And How To Improve Them, Austin D Klein, Ryeon N Naderi, Tyler J Hood, Keren Bassourou
C-Day Computing Showcase
With the increased usage of IoT devices in homes as well as different industries, vulnerabilities have also increased significantly. The IoT devices are small in size, and it is hard to incorporate security in the software because security has high demand for computation. We have been conducting this research in order to find more suitable security methods that are lightweight as well as efficient. We have decided to move away from key hiding algorithms, which have increased time and space consumption, in favor of smaller and quicker block cipher algorithms.
Gmc-157 Text-To-Digital Person Video Generator: Digitalavatargen, Akansha Kesharwani, Nisha Bagdwal, Md E Hossain, Drashti Patel, Nikhil Adigoppula
Gmc-157 Text-To-Digital Person Video Generator: Digitalavatargen, Akansha Kesharwani, Nisha Bagdwal, Md E Hossain, Drashti Patel, Nikhil Adigoppula
C-Day Computing Showcase
The Text-to-digital person video generator: DigitalAvatarGen project uses AI to create lifelike videos of 2D digital avatars from user text input. Users enter text, select a voice and select or upload an avatar, and generate a video using DigitalAvatarGen web application which uses Google TTS and SadTalker, to synchronize voice, expressions, and lip movements. Key contributions include a customizable user interface, personalized voice and avatar options, and an optimized backend for efficient video generation. This tool provides an engaging, realistic solution for applications in education, media, and customer interaction.
Gmc-2162 Prompt Engineering And Its Effects On Ai And Human Relationships: A Contemporary Approach, Francis Madu, Naga Janaki Madhav Kadiyala, Nivesh Thallapally
Gmc-2162 Prompt Engineering And Its Effects On Ai And Human Relationships: A Contemporary Approach, Francis Madu, Naga Janaki Madhav Kadiyala, Nivesh Thallapally
C-Day Computing Showcase
A. Background: Prompt engineering refers to the process of designing and refining input prompts for AI models (especially language models like GPT) to improve their outputs. It has become a critical tool in maximizing the performance and utility of AI models in diverse applications, from customer service to content creation. Beyond technical aspects, the interaction between humans and AI is increasingly shaped by the effectiveness of these prompts. B. Motivation: As AI becomes more integrated into daily life, the way humans interact with AI models is profoundly influenced by prompt engineering. Misaligned prompts can lead to misunderstanding, confusion, or unintended …
Gmr-229 Semantic Search Using Sentence Transformers, Roshni Satish, Arpana Challa
Gmr-229 Semantic Search Using Sentence Transformers, Roshni Satish, Arpana Challa
C-Day Computing Showcase
Traditional keyword-based search engines struggle to accurately capture the semantics of user queries in today's enormous digital resources. Our research study focuses on creating a semantic search engine that uses Sentence Transformers to improve information retrieval by understanding the context of queries and documents. Our method creates sentence embeddings for documents and user queries, allowing retrieval based on semantic similarity rather than keyword matching. The project involves data collection and preprocessing, feature extraction with Sentence Transformers, and implementation of a search engine that ranks documents based on cosine similarity to query embeddings. According to preliminary testing, this method greatly improves …
Gpr-1194 Computer Vision-Enhanced Spectroscopy For Glucose Prediction: An In Vitro Validation Study, El Arbi Belfarsi
Gpr-1194 Computer Vision-Enhanced Spectroscopy For Glucose Prediction: An In Vitro Validation Study, El Arbi Belfarsi
C-Day Computing Showcase
This study introduces a novel computer vision-based spectral approach for non-invasive glucose detection using synthetic blood samples. We developed an experimental setup with glucose concentrations from 70 to 120 mg/dL, using two dye methods. Light sources tested included an 850 nm LED, 850 nm laser, 808 nm laser, and 650 nm laser, with image capture via a 1080p IR camera. Data augmentation, including Gaussian noise, contrast and brightness adjustments, rotations, and zooming, produced seven variants per image. Three machine learning models—CNN, AdaBoost, and ResNet—were evaluated, with the 850 nm light source yielding the best results: 87.5% of predictions fell within …
Gpr-6126 Utilizing Ml Techniques For A Quantum Augmented Http Protocol, Nitin Jha
Gpr-6126 Utilizing Ml Techniques For A Quantum Augmented Http Protocol, Nitin Jha
C-Day Computing Showcase
Over the past decade, several small-scale quantum key distribution (QKD) networks have been implemented worldwide. However, achieving scalable, large-scale quantum networks relies on advancements in quantum repeaters, channels, memories, and network protocols. To enhance the security of current networks while utilizing available quantum technologies, integrating classical networks with quantum elements appears to be the next logical step. In this study, we propose modifications to the HTTP protocol's data packet structure, adjustments to end-to-end encryption methods, and optimized bandwidth distribution between quantum and classical channels for high-traffic network routes.
Uc-181 Prison Minecraft Game Mode Plug-In, Ryan S Venable, Carson R Hunter, David Do
Uc-181 Prison Minecraft Game Mode Plug-In, Ryan S Venable, Carson R Hunter, David Do
C-Day Computing Showcase
A project designed for Kennesaw State University's owned Minecraft server. The project centers around creating a Minecraft plug-in, a software product that is easy to activate in any Minecraft server. This plug-in changes the standard rules of Minecraft to become a classic game mode called Prison where players are taken to a special map and tasked with collecting resources in specialized mines or by fighting each other for them to earn in game currency for the purpose of buying their way to more privileged positions in the prison, gaining access to new areas and features. Prison was designed to work …
Ur-172 A Comparative Study Of Llm Effectiveness In Mental Health Assistance, Kris Prasad
Ur-172 A Comparative Study Of Llm Effectiveness In Mental Health Assistance, Kris Prasad
C-Day Computing Showcase
This study evaluates the effectiveness of LLMs in supporting mental health applications by analyzing their performance in understanding and categorizing user (mental health-related) inputs. We collected data from various mental health apps on the Google Play Store, including user reviews and app descriptions, and filtered content using a targeted mental health keyword bank. Sentiment analysis and keyword similarity scores were generated for reviews using RoBERTa-based models, this showed us how each review aligned with the mental health keywords advertised by the app and how users felt about the app. We prompted four modern LLMs: GPT-4o, Claude 3.5 Sonnet, Gemma 2, …
Gmc-219 Athlete-Agent Connect Mobile App, Ayokunle Ijagbemi, Esther N Uzoka, Foluke Omoniyi
Gmc-219 Athlete-Agent Connect Mobile App, Ayokunle Ijagbemi, Esther N Uzoka, Foluke Omoniyi
C-Day Computing Showcase
The Athlete-Agent Connect app aims to bridge the gap between athletes and agents, simplifying the process of professional engagement. By providing a digital space for talent acquisition and event coordination, the app fosters networking, recruitment, and collaboration within the sports industry. The platform’s features are tailored to meet the needs of athletes looking for representation and agents seeking clients, with tools for direct communication, event planning, and a calendar of relevant sports gatherings. This mobile app serves as a dedicated platform for athletes and sports agents to connect, collaborate, and enhance professional opportunities. The app enables athletes to hire agents …
Gmc-246 Enhancing Workforce Management Through Advanced Hr Analytics, Pradeep Rekapalli, Ruthvik Reddy Gurram
Gmc-246 Enhancing Workforce Management Through Advanced Hr Analytics, Pradeep Rekapalli, Ruthvik Reddy Gurram
C-Day Computing Showcase
The business analytics of employee data is a concern that human resource departments worldwide deal with. Some big organizations have entire teams working on analyzing these metrics. To obtain insights about employee turnover rates, performance trends, and compensation patterns from data, the Data warehousing techniques—OLAP and ETL—can be used to handle data. This paper aims to develop an OLAP model for multi-dimensional analysis using data warehousing techniques that help extract valuable insights from the data. Popular datasets will be used, and the model will be evaluated according to standards.
Gmc-218 Pet Rescue Ai-Based Support Application, Mariah Akintayo, Sibgha Ajmal, Kazi Nafis Ishtiaque, Krut Patel, Chelsi Alexander
Gmc-218 Pet Rescue Ai-Based Support Application, Mariah Akintayo, Sibgha Ajmal, Kazi Nafis Ishtiaque, Krut Patel, Chelsi Alexander
C-Day Computing Showcase
This project focuses on the development of an AI-driven support application for foster caregivers at Angels Among Us Pet Rescue. The application provides foster caregivers with real time assistance through an interactive chatbot, task reminders and resource management capabilities, streamlining the caregiving process. By leveraging automation and AI, the application enhances both the foster experience and operational efficiency aligning with the organizations mission of improving animal care.
Gmr-159 Llm Enabled Synthetic Dataset Generation For Human-Ai Teaming Algorithm, Sai Sanjay Potluri
Gmr-159 Llm Enabled Synthetic Dataset Generation For Human-Ai Teaming Algorithm, Sai Sanjay Potluri
C-Day Computing Showcase
This research explores using Large Language Models (LLMs) to generate synthetic datasets for Human-AI teaming algorithms, focusing on mental health assessments. We create a diverse dataset simulating human-AI collaboration scenarios in diagnostic processes. The synthetic data is labeled through an innovative approach involving two human annotators and three LLMs, using majority voting for consensus-based annotations. This dataset serves as a resource for training and evaluating Human- AI teaming algorithms, enabling exploration of collaboration dynamics between human expertise and AI in complex decision-making. Our approach addresses the scarcity of real-world data in Human-AI teaming scenarios and provides a controlled environment for …
Gmc-4190 Cellnucleirag - Smart Search Tool For Cell Nuclei Research, Sai Chandana Koganti
Gmc-4190 Cellnucleirag - Smart Search Tool For Cell Nuclei Research, Sai Chandana Koganti
C-Day Computing Showcase
CellNucleiRAG is a specialized tool developed to address a significant challenge in medical research: the rapid retrieval and synthesis of detailed information on cell nuclei. Understanding cell nuclei characteristics is crucial in fields like pathology, oncology, and diagnostics, where detailed cell analysis can guide disease identification and treatment planning. However, accessing relevant, organized information on specific cell nuclei types, datasets, models, and methods is often time-consuming, requiring manual searches through multiple, disparate sources. CellNucleiRAG solves this problem by acting as a smart search engine, designed specifically for cell nuclei research, combining traditional retrieval methods with advanced AI capabilities. Built with …
Gmr-208 Automatic Categorization Of Behavioral Health Issues In Police Reports, Mason V Pederson, Abm Adnan Azmee, Francis E Nweke
Gmr-208 Automatic Categorization Of Behavioral Health Issues In Police Reports, Mason V Pederson, Abm Adnan Azmee, Francis E Nweke
C-Day Computing Showcase
911 is often the first place contacted for dealing with behavioral health related (BHR) issues. Its estimated at least a fifth of all calls are related to behavioral health, and with BHR affected convicts having a recidivism rate of around 30%, its not hard to see how straining these issues can become on systems already stretched thin, where chronic understaffing is often a reality. A great solution would be if we could intervene as soon as possible to get people the treatment they need, police reports would be excellent for identifying and treating these individuals, but annotation is a long …
Gmr-210 Cogni-Resource: Ai-Driven Reflective Feedback Analysis For Enhanced Learning Insights And Resource Discovery, Ashrith Kumar Devara
Gmr-210 Cogni-Resource: Ai-Driven Reflective Feedback Analysis For Enhanced Learning Insights And Resource Discovery, Ashrith Kumar Devara
C-Day Computing Showcase
Cogni-Resource is a unified platform enhanced by AI that merges the introspective analysis of Cogni-Reflect with the precise resource exploration functions of the Learning Resource Finder, providing a holistic tool to improve educational environments. The Cogni-Reflect component uses advanced Large Language Models (LLMs) to examine student reflections, giving educators instant insights into learning results, difficulties, and areas where students may require extra assistance. Cogni-Reflect allows instructors to adjust their teaching by analyzing key themes and topics in reflective narratives, leading to a more adaptive and successful learning atmosphere. Using both web scraping and OpenAI API integration, the Learning Resource Finder …
Gmr-215 Efficient Sentiment Analysis Using Encoder-Only Transformer, Rohan Jonnalagadda, Srinidhi Kandimalla, Siri Yellu
Gmr-215 Efficient Sentiment Analysis Using Encoder-Only Transformer, Rohan Jonnalagadda, Srinidhi Kandimalla, Siri Yellu
C-Day Computing Showcase
In the era of social media, sentiment analysis has emerged as a vital instrument for comprehending public opinion, especially on sites like LinkedIn and Twitter. Because user-generated content is informal and noisy, traditional sentiment classification techniques like Naive Bayes and Support Vector Machines sometimes find it difficult to capture context, sarcasm, and long-term interdependence. In order to improve sentiment analysis accuracy for social media datasets with a specific focus on sentiments related to corporate layoffs, this study suggests an encoder-only transformer model. Our method successfully captures intricate phrase patterns and contextual subtleties in textual data by leveraging the self-attention mechanism …
Gmr-7179 Improving Alzheimer’S Detection Via Synthetic Data Generation Using Gpt-4 And Multi-Level Embeddings, Venkata Sai Bhargav Mutala, Imaan Shahid
Gmr-7179 Improving Alzheimer’S Detection Via Synthetic Data Generation Using Gpt-4 And Multi-Level Embeddings, Venkata Sai Bhargav Mutala, Imaan Shahid
C-Day Computing Showcase
This study leverages large language models (LLMs), particularly GPT-4, to overcome the data limitations often encountered in Alzheimer’s detection. We utilize GPT-4 for data augmentation, generating synthetic speech transcripts to enhance machine learning model training. Our approach combines fine-tuned BERT embeddings with CLAN-derived linguistic features, as well as sentence-level embeddings, to improve classification performance on the ADReSS2020 dataset. BERT and CLAN features capture detailed linguistic variants, while sentence embeddings offer robust semantic representations, collectively enhancing the accuracy and generalization of the models. Among the classifiers tested, the Random Forest model shows the best performance, achieving an accuracy of 88% with …
Gmr-8193 Harnessing Ml-Powered Hpcc Systems For Advanced Cybersecurity Analytics, Zularbine Kamal
Gmr-8193 Harnessing Ml-Powered Hpcc Systems For Advanced Cybersecurity Analytics, Zularbine Kamal
C-Day Computing Showcase
Information security in the era of AI and automation is the biggest challenge for cybersecurity professionals. Traditional information security protection has limitations in detecting zero-day attacks, which can be overcome with machine learning-based information security. An ML-powered intrusion detection system uses statistical analysis to spot deviations from normal behavior and helps to detect new and unknown threats. This poster will demonstrate how an open-source platform can be used for cybersecurity by leveraging various machine-learning algorithms.
Gpr-132 Hyperparameter Optimization In Neural Network Using Binary Search Algorithm, Faysal Chowdhoury, Yinning Zhang, Sait Suer
Gpr-132 Hyperparameter Optimization In Neural Network Using Binary Search Algorithm, Faysal Chowdhoury, Yinning Zhang, Sait Suer
C-Day Computing Showcase
Hyperparameter searching is a crucial process for every neural network training. However, this process is notably time-consuming due to the vast number of possible combinations and the influence these hyperparameters have on each other. The common approach is using grid search to exhaust all the options, which is computationally very expensive. In this research, we propose a new algorithm for this problem that is inspired by binary search and returns a significant improvement in time efficiency.