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arXiv 2025年

作者： Averly, Reza Baker, Frazier N. Ning, Xia Department of Computer Science and Engineering The Ohio State University United States Department of Biomedical Informatics The Ohio State University United States Translational Data Analytics Institute The Ohio State University United States College of Pharmacy The Ohio State University United States

Drug discovery is a long, expensive, and complex process, relying heavily on human medicinal chemists, who can spend years searching the vast space of potential therapies. Recent advances in artificial intelligence for chemistry have sought to expedite individual drug discovery tasks;however, there remains a critical need for an intelligent agent that can navigate the drug discovery process. Towards this end, we introduce LIDDIA, an autonomous agent capable of intelligently navigating the drug discovery process in silico. By leveraging the reasoning capabilities of large language models, LIDDIA serves as a low-cost and highly-adaptable tool for autonomous drug discovery. We comprehensively examine LIDDIA, demonstrating that (1) it can generate molecules meeting key pharmaceutical criteria on over 70% of 30 clinically relevant targets, (2) it intelligently balances exploration and exploitation in the chemical space, and (3) it can identify promising novel drug candidates on EGFR, a critical target for cancers. © 2025, CC BY-NC-SA.

关键词： Drug discovery

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A Smart Air Pollutants Monitoring System Using IOT Technologies

A Smart Air Pollutants Monitoring System Using IOT Technolog...

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Intelligent Systems for Communication, IoT and Security (ICISCoIS), International Conference on

作者： M Pyingkodi N R Wilfred Blessing K Thenmozhi K Kanimozhi L Pavithira P K Sandhya Balakrishnan Department of Computer Applications Kongu Engineering College Erode India IT Department College of Computing and Information Sciences University of Technology and Applied Sciences-Ibri(Government) Oman Department of Computer Science Kristu Jayanti College Bengaluru India Department of Information Technology Sri Krishna Adithya College of Arts and Science Coimbatore Department of Computer Science with Data Analytics PSG college of Arts & Science College Coimbatore Department of Computer Science CMS College of Science and Commerce Coimbatore

On international basis, one in all our century's essential problems is air pollutants. Every year, the range of individuals killed via way of means of pollutants climbs, with air pollutants being the main cause. The Internet of Things (IoT) is used worldwide community of “inventive gadgets” which could sense, and join to, and have interaction with different humans and systems. The solution is furnished via way of means of a sensor-primarily based totally hardware module that can be deployed anywhere. These gadgets, which can be hooked up on mild poles, homes, and different structures, wirelessly speak air pleasant facts to a far flung server. The microprocessor collects facts from IoT sensing gadgets and sends it to a cloud server for processing. These facts can be used to expecting while the vicinity is maximum polluted. The machine additionally gives air pleasant statistics through a cell application, permitting clients to choose alternatives wherein the air pleasant is great. It is likewise a totally less expensive device.

关键词： Cloud computing Microprocessors Air pollution Hardware Sensors Servers Internet of Things

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Novel Framework for Alzheimer Early Diagnosis using Inductive Transfer Learning Techniques 2

Novel Framework for Alzheimer Early Diagnosis using Inductiv...

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2nd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2022

作者： Sahu, Himanshu Kumar Kumar, Santosh Alsamhi, Saeed Hamood Chaube, Mithilesh Kumar Curry, Edward Shri Shankaracharya Technical Campus Bhilai India IIIT-Naya Raipur Department of Computer Science and Engineering Chhattisgarh India University of Galway Insight Centre for Data Analytics Galway Ireland IBB Univeristy Faculty of Engineering Ibb Yemen IIIT-Naya Raipur Department of Mathematical Sciences Chhattisgarh India

ISBN: (数字)9781665461818

ISBN: (纸本)9781665461818

The early diagnosis of Alzheimer's disease (AD) is gaining popularity due to massive applications in the medical field. The estimated 50 million people affected by AD worldwide are increasing rapidly, and the condition has an approximately 1 dollar trillion economic cost. This necessitates developing scalable, economical, and reliable methods for detecting Alzheimer's disease. We describe a unique architecture that uses auditory, cognitive, and linguistic aspects to create a multimodal ensemble system. Scores from the Mini-Mental State Examination are used to assess AD severity, which is detected using specialized artificial neural networks with temporal properties. We first test it using the ADReSS challenge dataset, a balanced dataset available through Dementia Bank that contains no biases against any patient. Our system achieves state-of-the-art test accuracy, precision, recall, and F1-score of 93.30% each, as well as a state-of-the-art test Root Mean Squared Error (RMSE) of 4.60, when it comes to the classification of AD. To our knowledge, the algorithm achieves cutting-edge AD classification accuracy of 88.0 % when tested against the benchmark Dementia Bank Pitt database. In order to build a robust inductive transfer learning model, our study highlights the applicability and transferability of spontaneous speech and demonstrates generalizability by utilising a task-agnostic feature space. © 2022 IEEE.

关键词： Classification (of information)

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data driven VRP: A neural network model to learn hidden preferences for VRP 27

Data driven VRP: A neural network model to learn hidden pref...

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27th International Conference on Principles and Practice of Constraint Programming, CP 2021

作者： Mandi, Jayanta Canoy, Rocsildes Bucarey, Víctor Guns, Tias Data Analytics Laboratory Vrije Universiteit Brussel Belgium Institute of Engineering Sciences Universidad de o'Higgins Rancagua Chile Department of Computer Science KU Leuven Belgium

ISBN: (纸本)9783959772112

The traditional Capacitated Vehicle Routing Problem (CVRP) minimizes the total distance of the routes under the capacity constraints of the vehicles. But more often, the objective involves multiple criteria including not only the total distance of the tour but also other factors such as travel costs, travel time, and fuel consumption. Moreover, in reality, there are numerous implicit preferences ingrained in the minds of the route planners and the drivers. Drivers, for instance, have familiarity with certain neighborhoods and knowledge of the state of roads, and often consider the best places for rest and lunch breaks. This knowledge is difficult to formulate and balance when operational routing decisions have to be made. This motivates us to learn the implicit preferences from past solutions and to incorporate these learned preferences in the optimization process. These preferences are in the form of arc probabilities, i.e., the more preferred a route is, the higher is the joint probability. The novelty of this work is the use of a neural network model to estimate the arc probabilities, which allows for additional features and automatic parameter estimation. This first requires identifying suitable features, neural architectures and loss functions, taking into account that there is typically few data available. We investigate the difference with a prior weighted Markov counting approach, and study the applicability of neural networks in this setting. © Jayanta Mandi, Rocsildes Canoy, Víctor Bucarey, and Tias Guns.

关键词： Neural network models

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An Industrial IoT Driven Approach for Sewage Gas Identification and Reporting System using Intelligent Sensors with Logical Controller 1

An Industrial IoT Driven Approach for Sewage Gas Identificat...

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1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart Systems, ISTEMS 2024

作者： Rajasubha, J. Ramya, R. Juliet, A. Hency Janakiraman, V. Devi, S. Rukmani Karuppaiah, S. Dhanalakshmi College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Nano Electronics Materials and Sensors Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Data Analytics Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai India Saveetha College of Liberal Arts and Sciences Simats Deemed to Be University Department of Computer Science Tamil Nadu Chennai India R.M.D Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350384246

Based on a real-time sewage gas level detection and monitoring system, this system seeks to offer intelligent solutions for the monitoring of toxic sewage gases. An alarm is issued to the observer who is looking at the circumstances from a distance whenever a specific threshold is crossed. Following this, the data is transmitted together with different gas ppm levels, indicating if the worker can clean or operate securely in that region. The suggested solution incorporates both the IoT platform and the remotely located IoT monitoring devices. This necessitates finding the appropriate threshold values for septic plants and facilities, as well as calibrating gas sensors for use in industry. The gear is meant to provide the sewage worker advance notification whenever the concentration of toxic gaseous substances grows over time, ensuring their safety. The presence of gas, temperature, and other factors in sewage may be detected using a variety of sensors. By notifying sewage workers as well as cleaners via SMS whenever the threshold value drops below the observed levels, this technology helps save people from harmful infections, which is a social good. A number of harmful gas concentrations are graphed out for real-time monitoring, and this is how it accomplishes its job. In order to assess the efficacy of the suggested model, this paper cross-validates it with the Conventional Sewage Gas Detection (CSGD) based on GPRS functioning. The goal of the cross-validation is to create an IoT based Sewage Gas Detection (IoTSGD) system that can monitor and analyze toxic gas levels in real-time and notify workers. Firebase receives the measured gas levels. If the levels rise over a certain point, it will notify the authorized individuals working remotely through their linked mobile devices. An SMS message can alert the authorities if the sewage system is likely to overflow. For the purpose of monitoring, an android app was created and connected with the planned system. © 2024 IEE

关键词： Internet of things

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GeLLM3O: Generalizing Large Language Models for Multi-property Molecule Optimization

arXiv

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arXiv 2025年

作者： Dey, Vishal Hu, Xiao Ning, Xia Department of Computer Science and Engineering The Ohio State University United States Translational Data Analytics Institute The Ohio State University United States Department of Biomedical Informatics The Ohio State University United States College of Pharmacy The Ohio State University United States

Despite recent advancements, most computational methods for molecule optimization are constrained to single- or double-property optimization tasks and suffer from poor scalability and generalizability to novel optimization tasks. Meanwhile, Large Language Models (LLMs) demonstrate remarkable out-of-domain generalizability to novel tasks. To demonstrate LLMs’ potential for molecule optimization, we introduce MuMOInstruct, the first high-quality instruction-tuning dataset specifically focused on complex multi-property molecule optimization tasks. Leveraging MuMOInstruct, we develop GeLLM3Os, a series of instruction-tuned LLMs for molecule optimization. Extensive evaluations across 5 in-domain and 5 out-of-domain tasks demonstrate that GeLLM3Os consistently outperform state-of-the-art baselines. GeLLM3Os also exhibit outstanding zero-shot generalization to unseen tasks, significantly outperforming powerful closed-source LLMs. Such strong generalizability demonstrates the tremendous potential of GeLLM3Os as foundational models for molecule optimization, thereby tackling novel optimization tasks without resource-intensive retraining. MuMOInstruct, models, and code are accessible through https://***/ninglab/ GeLLMO. © 2025, CC BY.

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An Effective Classification Algorithm for Heart Disease Prediction with Genetic Algorithm for Feature Selection 1

An Effective Classification Algorithm for Heart Disease Pred...

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1st Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2021

作者： Kanwal, Samina Rashid, Junaid Nisar, Muhammad Wasif Kim, Jungeun Hussain, Amir COMSATS University Islamabad Department of Computer Science Wah Campus Pakistan Kongju National University Department of Computer Science and Engineering Korea Republic of Edinburgh Napier University Data Science and Cyber Analytics Research Group EdinburghEHI 4DY United Kingdom

ISBN: (纸本)9781665424134

Heart disease is the world's leading cause of increasing death rates. Although there is a lot of research in the medical sector, an efficient and reliable model to predict this disease at an early stage is still required. So, early diagnosis of heart disease is the most promising strategy for effective treatment. In this paper, we utilize the Genetic algorithm (GA) to select attributes, which are used as input for the machine learning algorithms Deep Learning(DL), Support Vector Machine (SVM), Neural network (NN), Naive Bayes (NB), and Logistic regression (LR). The two datasets of heart disease are used for model implementation. The results evaluation is measured using accuracy, precision, and f-measure. The proposed model achieves the 92% result in the term of accuracy. In terms of precision, 96% result is achieved. © 2021 IEEE.

关键词： Genetic algorithms

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Reconstructing Optical Imagery from Microwave data: A Neural Regression-Based Framework

Reconstructing Optical Imagery from Microwave Data: A Neural...

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Remote Sensing Symposium (InGARSS), 2020 IEEE India Geoscience and

作者： Arpan Dawn Kounik De Sarkar Abhiroop Chatterjee Susmita Ghosh Surajit Ghosh Department of Civil Engineering National Institute of Technology Durgapur Durgapur India Environmental Science and Engineering Department Indian Institute of Technology Bombay Mumbai India Department of Computer Science and Engineering Jadavpur University Kolkata India Water Futures Data and Analytics International Water Management Institute Colombo Sri Lanka

ISBN: (数字)9798350390346

ISBN: (纸本)9798350390353

To monitor the vegetation condition during extreme weather events, this article suggests an approach for mimicking vegetation indices, notably the Normalized Difference Vegetation Index (NDVI), using microwave data. The main goal is to generate simulated NDVI images from microwave data to improve temporal availability and fill the data gaps in the time series when optical data is unavailable. A neural network regression model was developed, using SAR data as input features to predict optical data. The network architecture included an input layer, followed by a dense layer with 32 ReLU-activated nodes, and an output layer with a single node. The model was trained using the Adam optimizer and mean squared error (MSE) as the loss function. Initial results show an $R^{2}$ of 0.62. Pearson’s correlation coefficient is $\mathbf{- 0. 7 9}$ and 0.019 for red vs VV and NIR vs VH bands, respectively.

关键词： Normalized difference vegetation index Clouds Ultraviolet sources Vegetation mapping Optical imaging Cyclones Adaptive optics Optical sensors Monitoring Microwave imaging

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A Random Forest Students' Performance Prediction (RFSPP) Model Based on Students' Demographic Features 1

A Random Forest Students' Performance Prediction (RFSPP) Mod...

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1st Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2021

作者： Batool, Saba Rashid, Junaid Nisar, Muhammad Wasif Kim, Jungeun Mahmood, Toqeer Hussain, Amir COMSATS University Islamabad Department of Computer Science Wah Campus Pakistan Kongju National University Department of Computer Science and Engineering Korea Republic of National Textile University Faisalabad Department of Computer Science Pakistan Edinburgh Napier University Data Science and Cyber Analytics Research Group EdinburghEHI 4DY United Kingdom

ISBN: (纸本)9781665424134

Education plays a crucial role in individual life as well as for the whole nation. Many students are dropped out yearly in different academic courses. This study investigates the contribution of students' demographic attributes to their academic achievements. The Random Forest classification model is used to predict students' final exam performance. Three publically available datasets with a different set of demographic features are used to evaluate these attributes and their impact on students' results. Hold-out and cross-validation methods are used to evaluate experimental results. Random Forest with three different datasets gave F-measure of 81.20%, 95.10%, and 84.16%. The presented study is significant for educational authorities to predict students' performance before they drop out. © 2021 IEEE.

关键词： Students

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Reducing False Positives in Intrusion Detection System Alerts: A Novel Aggregation and Correlation Model 15th

Reducing False Positives in Intrusion Detection System Alert...

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15th EAI International Conference on Digital Forensics and Cyber Crime, ICDF2C 2024

作者： Hadi, Hassan Jalil Cao, Yue Hussain, Faisal Bashir Ahamad, Naveed Alshara, Mohammed Ali Ullah, Insaf Javed, Yasir He, Yinglong Jamil, Abdul Majid School of Cyber Science and Engineering Wuhan University Wuhan China Department of Computer Science Bahria University Islamabad Pakistan College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Institute for Analytics and Data Science University of Essex ColchesterCO4 3SQ United Kingdom University of Surrey Guildford United Kingdom

ISBN: (纸本)9783031893629

Network security involves safeguarding and preventing unauthorized access to networks through various methodologies, including access control, firewalls, and intrusion detection and prevention systems (IDS/IPS). Despite the escalating frequency of network attacks, traditional tools like antivirus software and firewalls often fall short. In contrast, IDS/IPS solutions are increasingly favored for robust network protection due to their effectiveness. However, the integration of machine learning and signature-based approaches has led to a significant increase in false positives, making the manual analysis and identification of genuine alerts labor-intensive. Additionally, conventional processes may impede IDS performance, even with the use of alert aggregation and correlation techniques that aim to efficiently eliminate redundant or duplicate alerts. This work introduces a model for alert aggregation and correlation based on similarity-based correlation to mitigate anomaly-based alerts, employing a Security Event Correlator (SEC). Initially, an Anomaly Detection System (ADS), akin to IDS, is deployed. The alerts generated by the ADS serve as inputs for the proposed SEC, which preprocesses the alerts to systematically remove redundancies and duplicates. Aggregation is then executed based on extracted features, followed by correlation to prioritize the remaining alerts by considering their frequency and features. The proposed model has successfully achieved a remarkable 99.0% accuracy rate in removing redundant and duplicate alerts. © ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Network intrusion

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