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arXiv 2023年

作者： Mehra, Ashman Saha, Snehanshu Raychoudhury, Vaskar Mathur, Archana Dept. of CSIS BITS Pilani K K Birla Goa Campus Goa India Dept. of CSIS and APPCAIR BITS Pilani Goa India HappyMonk AI Bangalore India Department of Computer Science and Software Engineering Miami University Miami United States Dept. of Information Science and Engineering Nitte Meenakshi Institute of Technology India

Delivery of items from the producer to the consumer has experienced significant growth over the past decade and has been greatly fueled by the recent pandemic. Amazon Fresh, Shopify, UberEats, InstaCart, and DoorDash are rapidly growing and are sharing the same business model of consumer items or food delivery. Existing food delivery methods are sub-optimal because each delivery is individually optimized to go directly from the producer to the consumer via the shortest time path. We observe a significant scope for reducing the costs associated with completing deliveries under the current model. We model our food delivery problem as a multi-objective optimization, where consumer satisfaction and delivery costs, both, need to be optimized. Taking inspiration from the success of ride-sharing in the taxi industry, we propose DeliverAI - a reinforcement learning-based path-sharing algorithm. Unlike previous attempts for path-sharing, DeliverAI can provide real-time, time-efficient decision-making using a Reinforcement learning-enabled agent system. Our novel agent interaction scheme leverages path-sharing among deliveries to reduce the total distance traveled while keeping the delivery completion time under check. We generate and test our methodology vigorously on a simulation setup using real data from the city of Chicago. Our results show that DeliverAI can reduce the delivery fleet size by 12%, the distance traveled by 13%, and achieve 50% higher fleet utilization compared to the baselines. © 2023, CC BY.

关键词： Reinforcement learning

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Advancing Speech Therapy for Sinhala-Speaking Children with Autism Spectrum Disorder Through an Intelligent Dialog System

Advancing Speech Therapy for Sinhala-Speaking Children with ...

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International Research Conference on Smart Computing and Systems engineering (SCSE)

作者： Aparna Jayawardena Koliya Pulasinghe Samantha Rajapakshe Dept. of Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Dept. of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331523268

ISBN: (纸本)9798331523275

This paper presents a dialog system integrated with a NAO socially assistive robot, designed to support Sinhala-speaking children with Autism Spectrum Disorder (ASD). The system leverages a pipeline-based architecture implemented using the RASA framework, consisting of Natural Language Understanding (NLU), Dialog Management (DMU), and Natural Language Generation (NLG) units. The NLU unit processes user input by identifying intents, entities, and dialogue acts, incorporating custom tools like the SpokenSinhalaVerbTokenizer for handling spoken Sinhala. The DMU includes a Dialog State Tracker (DST) to maintain conversation context and a Dialog Policy Generator, which employs rule-based, TED, and UnexpecTED policies to adapt conversation flows dynamically. The NLG unit generates natural responses to foster interactive and goal-oriented conversations. Integrated with the NAO robot, the system engages children through meaningful dialogues, such as discussing toy preferences, aiming to enhance social interaction and communication skills. This work highlights the potential of conversational AI and robotics in therapeutic interventions for ASD in low-resource languages.

关键词： Autism Accuracy Natural language generation Toy manufacturing industry Medical treatment Oral communication Systems engineering and theory Solids Reliability Robots

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Rain Predictive Model using Machine learning Techniques

Rain Predictive Model using Machine learning Techniques

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2023 International Multi-Disciplinary Conference in Emerging Research Trends, IMCERT 2023

作者： Muneer, Muhammad Shahbaz Nabeel Mustafa, Syed Muhammad Zehra, Syeda Sundus Maqsood, Haniya NED University of Engineering and Technology Dpertment of Computational Finance Karachi Pakistan Sir Syed Uuniversity of Engineering and Technology Dept. of Software Engineering Karachi Pakistan NED University of Engineering and Technology Department of Computer and Information Systems Engineering Karachi Pakistan

ISBN: (纸本)9798350347708

Climate is rapidly changing around the world. Over time, there have been significant changes in the weather. Rainfall is now erratic due to climate change. The frequency of extreme weather events like droughts and floods has increased due to climate change, necessitating the need for more precise and timely rainfall forecasts. For strategic reasons including agriculture, water resource management, and architectural design, rain forecasting is crucial. The naturally occurring non-stationary component in the rainfall time series impairs model performance for practical hydrologists and drought risk assessors. We present a rain predicting model based on machine learning to address the forecasting issue. In our work, we predict the possibility of rain the next day on the basis of last 10 years' data. The variables that were calculated during the experiments were humidity, pressure, evaporation, sunshine, rainfall, and so on. Random Forest gave the 90% accuracy with 0.904 Area under Curve, highest out of all the algorithms. The model's performance will significantly aid in the rain forecast. © 2023 IEEE.

关键词： Rain

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Classification of Subtypes of Acute Lymphoblastic Leukemia Using Feature Fusion from Blood Smear Images

Classification of Subtypes of Acute Lymphoblastic Leukemia U...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Ghazala Alhadi Eljadi Ahmed Lawgali Salwa Elakeili dept. Computer Sciences Faculty of Information Technology University of Benghazi Beghazi Libya dept. Software Engineering Faculty of Information Technology University of Benghazi Beghazi Libya

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Early detection of Acute Lymphoblastic Leukemia (ALL) is vital for providing appropriate treatment, which requires accurate diagnosis to identify the types of the disease. Therefore, this study focuses on evaluating the performance of deep learning models in classifying different types of leukemia using three pre-trained CNN models: MobileNet V2, DenseNet201, and EfficientNet, to extract deep features. These features are then combined into a single set. An information exchange technique was used to select the best features after merging, and SMOTEENN was employed to address feature imbalance. The results showed that combining features extracted by the pre-trained CNN models achieved high accuracy and reliability, reaching 99.66%, enhancing the performance in accurately and quickly classifying patients. This approach not only improves diagnostic accuracy but also contributes to accelerating the decision-making process for treatment, enabling doctors to better tailor therapies and thereby improve patient outcomes. The findings underscore the importance of ongoing research in this field and provide future recommendations for enhancing performance and increasing classification accuracy in similar medical applications.

关键词： Support vector machines Accuracy Merging Leukemia Medical treatment Feature extraction Convolutional neural networks Reliability Blood Medical diagnostic imaging

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Blockchain-based Reputation System For IoT Data Ecosystem: A Utility Maximization Approach 19

Blockchain-based Reputation System For IoT Data Ecosystem: A...

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19th IEEE International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI, HONET 2022

作者： Khezr, Seyednima Yassine, Abdulsalam Benlamri, Rachid Lakehead University Dept. of Electrical and Computer Engineering Thunder BayON Canada Lakehead University Dept. of Software Engineering Thunder BayON Canada University of Doha for Science and Technology Dept. of Information Technology Doha Qatar

ISBN: (纸本)9781665461979

Recently, blockchain technology has been used to build private and secure internet of things (IoT) data markets and trading systems. Reputation of the trading parties is an important attribute that affect their profitability and trading prosperity. However, current reputation systems are prone to malicious manipulation. Such an issue can be avoided by registering all reputation scores in the blockchain which guarantees trans-parency, encryption, privacy, and security. To this end, this paper proposes a blockchain-based reputation system where market players can maximize their utility while engaged in the IoT data ecosystem. We also propose an adaptive learning mechanism that allows the data providers and consumers to enhance their reputation and review credibility scores. We provide extensive experiments to ensure our system satisfies the feasibility, usability, and availability properties. These properties are essential for functional deployment in real IoT data markets. © 2022 IEEE.

关键词： Blockchain

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Impact of Artificial Intelligence on Decision-Making in information Systems

Impact of Artificial Intelligence on Decision-Making in Info...

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Advanced Research in Computing (ICARC), International Conference on

作者： RWMR Dharani Rajasinghe Dept. of Software Engineering & Information Systems Faculty of Computing NSBM Green University Homagama Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

The study examines how artificial intelligence (AI) affects information systems (IS) decision-making, emphasizing how AI improves precision, speed, and flexibility in a range of industries. AI presents potential and problems, including ethical, legal, and societal ramifications, as it revolutionizes organizational decision-making. In order to demonstrate AI's function in intricate IS systems, this paper examines a variety of AI strategies used in decision-making, assesses their efficacy using accepted metrics, and examines real-world case studies. Key findings address issues including data bias, lack of transparency, and job displacement while highlighting the advantages of AI in processing massive information and producing useful insights. The recommendations highlight the necessity of worker reskilling, sector-specific adaptability, explainable AI (XAI) models, and regulatory frameworks. In order to facilitate sustainable AI integration in IS for improved organizational and social results, this research emphasizes the significance of striking a balance between AI-driven innovation and ethical issues.

关键词： Industries Surveys Ethics Adaptation models Technological innovation Explainable AI Law Decision making Data models information systems

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Impact of Artificial Intelligence on Decision-Making in information Systems 5

Impact of Artificial Intelligence on Decision-Making in Info...

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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Rajasinghe, Rwmr Dharani Faculty of Computing Nsbm Green University Dept. of Software Engineering & Information Systems Homagama Sri Lanka

ISBN: (纸本)9798331530983

关键词： Risk perception

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Flexibility and Privacy: A Multi-Head Federated Continual Learning Framework for Dynamic Edge Environments 11

Flexibility and Privacy: A Multi-Head Federated Continual Le...

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11th International Symposium on Computing and Networking, CANDAR 2023

作者： Chen, Chunlu Wang, Kevin I-Kai Li, Peng Sakurai, Kouichi Kyushu University Graduate School of Information Science and Electrical Engineering Fukuoka Japan The University of Auckland Dept. of Electrical Computer and Software Engineering Auckland New Zealand The University of Aizu School of Computer Science and Engineering Aizuwakamatsu Japan Kyushu University Faculty of Information Science and Electrical Engineering Fukuoka Japan

ISBN: (纸本)9798350306705

Federated Learning (FL) and the Internet of Things (IoT) have revolutionized data processing and analysis, overcoming the traditional limitations of cloud computing. However, traditional machine learning strategies lead to increased costs and catastrophic forgetting due to model retraining with new datasets. Continual learning has been proposed to counter this, enabling models to adapt to new data while preserving previous knowledge, which is beneficial for dynamic edge environments. Despite these advantages, the retention of previous knowledge during the continual learning process may lead to the information leakage. To address the inherent challenges of multitask scenarios, we present a Federated Continual Learning (FCL) framework that integrates the privacy-preserving benefits of Federated Learning (FL) into a continual learning system, ensuring both continual learning and privacy preservation in edge computing data processing and analysis. Specifically, our architecture introduces dedicated fully-connected layers for each task. This architecture ensures that distinctive features pertinent to each task are not only captured but also preserved throughout the model's lifespan. Within our framework, data is processed via task-specific layers. Subsequently, the final label is determined by associating it with the paramount prediction value, thus capitalizing on the model's comprehensive knowledge reservoir to bolster prediction accuracy. We subjected our FCL framework to rigorous validation using two benchmark datasets: MNIST and CFAR-10. Experimental outcomes unequivocally substantiate the efficacy of our proposed methodology. © 2023 IEEE.

关键词： Learning systems

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Formal Verification for Security Technologies in the Blockchain with Artificial Intelligence: A survey

Formal Verification for Security Technologies in the Blockch...

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2022 Innovations in Intelligent Systems and Applications Conference, ASYU 2022

作者： Ilgi, Galip Savas Kayali, Devrim Olawale, Priscilla Demir Erdem, Binnur Dimililer, Kamil Kirsal-Ever, Yoney Near East University Information Sys. Engineering Dept. Nicosia Turkey Near East University Electrical & Electronic Engineering Dept. Nicosia Turkey Near East University Software Engineering Dept. Nicosia Turkey Near East University Automotive Engineering Dept. Nicosia Turkey

ISBN: (数字)9781665488945

ISBN: (纸本)9781665488945

Last two decades enhancements of the communication technologies have been interpreting the daily lives as well as the academia and multidisciplinary research fields. One of these enhancements is the blockchain technology which has become one of the most popular and attractive topics over the last five years. Blockchain technology has attracted more and more attention from academia and industry recently. Blockchain is defined as a list of data blocks as a publicly distributed ledger, which are linked together using cryptography. In this matter, cryptographic security issues have increased. In recent years, new, advanced intrusion detection and authentication/privacy protection techniques have been designed and developed. Diversity of computer security requirements results in introducing of different kinds of security models. In fact, each security model is an abstraction of a security policy. Formal Methods have a mathematical foundation, and thus, employs techniques and tools based on mathematics and mathematical logic that support the modelling, specification and verification of the systems. This paper provides a critical and detailed review of the recent researches published in recent years in formal verification methods for communication security in blockchain with artificial intelligence applications. © 2022 IEEE.

关键词： Smart contract

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Generative AI in FinTech: Generating Images Based on Predefined Lists of Stock Keeping Units Using Product Descriptions 2

Generative AI in FinTech: Generating Images Based on Predefi...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rohella, Piyush Mimani, Sushant Ramakrishnan, Rakesh Jiwani, Nasmin Logeshwaran, J. Payments & Commerce GoDaddy EdmondsWA United States Senior Software Engineer GoDaddy SnohomishWA United States University of the Cumberlands Dept. of Information Technology Union CityCA United States University of the Cumberlands Dept. of Information Technology WilliamsburgKY United States Sri Eshwar College of Engineering Department of Ece Tamil Nadu Coimbatore India

ISBN: (纸本)9798350371055

Generative AI in FinTech is a new technology that uses AI to generate images based on predefined lists of Stock Keeping Units (SKUs) using product descriptions provided by users. It leverages an AI-based tool to produce custom product images to accurately sync with individual SKUs. This technology allows FinTech companies to create product images to help promote their ecommerce sites or offline stores, while still adhering to product description specifics that define user intent. With more accurate product images, customers are able to more easily find the product they are looking for. Generative AI in FinTech also helps to reduce the manual effort it takes for businesses to define and recreate product images for customers, saving cost and time. Overall, this technology is a key breakthrough in FinTech's efforts to create seamless customer experiences. © 2024 IEEE.

关键词： FinTech Generative AI Generative Imaging Product Descriptions Stock Keeping Units

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