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2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Gupta, Gaurav Menaka, C. Padmanabhan, P. Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science and Information Technology Bangalore India Noida Institute of Engineering and Technology Department of Artificial Intelligence & Machine Learning Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350344387

The Internet of Drones (IoD) has garnered significant interest in recent years due to its portable and automated nature and is being utilized across various domains such as the military, search and rescue operations, and entertainment. However, due to the inherently unsecured radio transmission paths in the Internet of Drones (IoD), there are numerous security concerns associated with the data collected, generated, or processed by drones. This paper aims to address the prominent challenges of security and privacy in the IoD. It undertakes a thorough examination of security concerns and corresponding solutions for IoD security. The discussion encompasses the security requirements associated with IoD and highlights the most recent advancements in research pertaining to IoD security. Authentication methods and Blockchain-based schemes are highlighted in this overview of numerous significant security technologies. Through an extensive analysis, we hereby present an examination of the challenges encountered by existing methodologies and propose potential avenues for future research in the field of IoT security. This review highlights the necessity of implementing suitable security measures to tackle IoT security concerns. Furthermore, it emphasizes the importance of developing security solutions that carefully consider the trade-off among the level of security provided and the cost efficiency of implementation. © 2023 IEEE.

关键词： Authentication Blockchain Internet of Drones (IoD) Privacy Security

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Securing Digital Governance: A Deep learning and Blockchain Framework for Malware Detection in IoT Networks 2

Securing Digital Governance: A Deep Learning and Blockchain ...

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2nd IEEE International Conference on Integrated intelligence and Communication Systems, ICIICS 2024

作者： Pawar, Priyanka Pramod Kumar, Deepak Meesala, Mohan Kumar Pareek, Piyush Kumar Addula, Santosh Reddy Shwetha, K.S. Department of Information Technology University of the Cumberlands WilliamsburgKY United States Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Dayananda Sagar College of Engineering Bangalore India

ISBN: (纸本)9798331504960

This research uses deep learning and blockchain frameworks to provide a safe platform that promotes digital governance data exchange and interoperability. Use the bonobo optimization algorithm to start a blockchain-based smart city data authentication approach. This paper presents a Blockchain-based malware detection method and framework that uses AI to account for multiple distributed conditions. An upgraded greedy search algorithm and the XGBoost decision tree construct a two-layer extreme gradient boosting (XGBoost) classification model that detects attacks. Three pre-existing XGBoost significance indices were split and merged based on the model's leaf nodes' tree traversal structural features. The augmented greedy search technique retrieved and imported spectral band variables into the XGBoost model's second layer. Bat method was used to optimize XGBoost modeling parameters. The deployed model increased power consumption per device by 13.5%, while Raspberry Pi devices used 0.2 GB and NVIDIA Jetson devices used 0.42 GB. ML models had 93% f1-scores and 95% detection accuracy on both datasets. Our technology detects malware and attacks in Smart Environments efficiently and accurately, as shown by the models. © 2024 IEEE.

关键词： Decision trees

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Building a Data Lake for Power BI in the Cloud: A Review on Utilizing Cloud Storage Services for Large Datasets 2

Building a Data Lake for Power BI in the Cloud: A Review on ...

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2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Gurbade, Viraj Vijay Verma, Prateek Gundewar, Swapnil Bramhe, Vijendra Singh Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Vit Bhopal University School of Computing Science and Engineering Kothrikalan Madhya Pradesh Sehore466114 India

ISBN: (纸本)9798331528713

A data lake is a centralized repository where you may store both structured and unstructured data at any scale. Unlike typical data warehouses, which need data structure for storage, data lakes allow you to store raw data in its natural state. This adaptability makes data lakes suitable for storing large volumes of various data kinds, including photographs, videos, log files, sensor data, and others. When integrating a data lake with Power Business intelligence (BI), organizations can harness the power of both technologies to gain valuable insights from their data. In the realm of data-driven decision-making, organizations face the challenge of managing and extracting insights from vast and diverse datasets, a task conventional on-premises storage solutions often struggle with due to the exponential growth of data. Thus, the adoption of more scalable and flexible alternatives becomes imperative. Cloud-based data lakes emerge as transformative solutions, offering unparalleled scalability, accessibility, cost-effectiveness, and advanced security protocols. This paper discuss the concept of constructing a cloud-based data lake optimized for Power BI, leveraging leading cloud storage services like Azure Data Lake Storage (ADLS) and Amazon S3. By seamlessly integrating Power BI with the cloud data lake, organizations can overcome traditional storage limitations and reap numerous benefits. The practical applications in advanced analytics, customer insights, IoT data management, and data discovery underscore the real-world utility of cloud data lakes. The integration of Power BI with a cloud data lake augments efficient data analysis and self-service analytics potential, offering organizations a comprehensive solution for deriving actionable insights from their data assets. © 2024 IEEE.

关键词： Decision making

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Improved top-down attention phasmatodea population spiking neural network based multi crop recommendation framework

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Multimedia Tools and Applications 2025年第10期84卷 8071-8105页

作者： R, Chetan Ashoka, D.V. B, Ajay Prakash Department of Computer Science and Engineering JSS Academy of Technical Education Visvesvaraya Technological University Karnataka India Department of Information Science JSS Academy of Technical Education Visvesvaraya Technological University Karnataka India Department of Information Science and Engineering JSS Academy of Technical Education Visvesvaraya Technological University Karnataka India Department of Artificial Intelligence and Machine Learning Dr. Ambedkar Institute of Technology Visvesvaraya Technological University Karnataka India

Modern agriculture refers to the contemporary practices, technologies and methods used in food production to meet the growing global demand for agricultural products. With increasing concerns about environmental degradation, soil health and resource depletion, there is an insistent need for agricultural practices that are both productive and sustainable. Multicropping mitigate the negative impacts of monoculture and reduce the reliance on synthetic inputs. This research introduces an innovative approach for multi-crop recommendation through the utilization of an integrated framework that combines advanced optimization techniques and neural networks like Enhanced top-down attention Spiking Neural Network (ETSNN). The proposed framework involves two main phases: the first phase focuses on classifying soils suitable for specific agro-ecological zones using an enhanced top-down attention spiking neural network. The second phase predicts multi-crop yields across different districts based on various environmental parameters using an Improved Phasmatodea population Evolutionary Algorithm (IPEA). Before classifying the crops feature selection takes place for the dataset by Enhanced Sea horse Optimization (ESO) method. The outputs from both phases are harmonized using a stacking-based fusion method. The developed user interface accepts real-time input for location-specific soil properties and provides crop recommendations that take into account both the input data and climate parameters. The proposed method attains 99.7% accuracy, 99.2% precision, 99% recall and 99.9% F1-score respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Crops

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Impacts of Fake Reviews on Dietary Supplements and Healthcare Products in Social Media 15

Impacts of Fake Reviews on Dietary Supplements and Healthcar...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kannal, Nithin M Asmathunnisa, N. Kallimani, Jagadish S M S Ramaiah Institute of Technology Department of Artificial Intelligence and Machine Learning Karnataka Bangalore India M S Ramaiah Institute of Technology Department of Computer Science and Engineering Bangalore India Visvesvaraya Technological University Karnataka Belagavi India

ISBN: (纸本)9798350370249

In today's digital landscape, the prevalence of misinformation is a pressing concern, particularly on social media platforms where approximately $42.8 \%$ of shared content is identified as inaccurate and fake, as highlighted by a 2019 study from Loughborough University's Online Civic Culture Centre. This phenomenon extends beyond mere inconvenience, with false information on political issues and product reviews posing significant threats to societal trust, business integrity, and political discourse. Furthermore, 'The State of Fake Reviews - Statistics and Trends' by InvespCRO (2023) underscores a concerning trend: the steady rise in fake reviews from 2019 to 2023, indicative of a persistent challenge in maintaining online authenticity. This article delves into the intriguing world of online reviews, focusing specifically on the impact of falsified reviews within the realm of supplements and health products. With the power to sway consumer opinions and shape company reputations, false reviews wield considerable influence, often distorting market perceptions and consumer choices. By immersing ourselves in a hypothetical scenario where a product garners an alluring yet deceitful review, we unravel the ripple effects across various sectors, illuminating the far-reaching consequences of deceptive practices. © 2024 IEEE.

关键词： Dietary Supplements Fake Reviews Global Forecast on Online Products Healthcare Products Social Media

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A Novel Privacy-Centric Training Routine for Maintaining Accuracy in Traditional machine learning Systems 7th

A Novel Privacy-Centric Training Routine for Maintaining Acc...

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7th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2023

作者： Haritas, Hrishikesh K. Haritas, Chinmay K. Kallimani, Jagadish S. Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bengaluru India Undergraduate Programme Indian Institute of Science Bengaluru India

ISBN: (纸本)9789819940394

The increasingly prevalent concerns on user privacy and user’s abstinence on data sharing assuredly spells a decline in novel model development for machine learning. On-Device AI is an exciting paradigm with local training and no requirements of externalizing personal data. Hybridizing these techniques of traditional data upload and server-side training to maximize accuracy and an On-Device approach, protecting privacy standards yields us a novel training routine, which can find itself extensively invoked with increasing privacy concerns. We enumerate multiple use cases in addition to examining feasibility and an abstract implementation of this concept. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data acquisition

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SigStyle: Signature Style Transfer via Personalized Text-to-Image Models 39

SigStyle: Signature Style Transfer via Personalized Text-to-...

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39th Annual AAAI Conference on artificial intelligence, AAAI 2025

作者： Wang, Ye Bai, Tongyuan Xie, Xuping Yi, Zili Wang, Yilin Ma, Rui School of Artificial Intelligence Jilin University China College of Computer Science and Technology Jilin University China School of Intelligence Science and Technology Nanjing University China Adobe United States Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MOE China

ISBN: (纸本)157735897X

Style transfer enables the seamless integration of artistic styles from a style image into a content image, resulting in visually striking and aesthetically enriched outputs. Despite numerous advances in this field, existing methods did not explicitly focus on the signature style, which represents the distinct and recognizable visual traits of the image such as geometric and structural patterns, color palettes and brush strokes etc. In this paper, we introduce SigStyle, a framework that leverages the semantic priors that embedded in a personalized text-to-image diffusion model to capture the signature style representation. This style capture process is powered by a hypernetwork that efficiently fine-tunes the diffusion model for any given single style image. Style transfer then is conceptualized as the reconstruction process of content image through learned style tokens from the personalized diffusion model. Additionally, to ensure the content consistency throughout the style transfer process, we introduce a time-aware attention swapping technique that incorporates content information from the original image into the early denoising steps of target image generation. Beyond enabling high-quality signature style transfer across a wide range of styles, SigStyle supports multiple interesting applications, such as local style transfer, texture transfer, style fusion and style-guided text-to-image generation. Quantitative and qualitative evaluations demonstrate our approach outperforms existing style transfer methods for recognizing and transferring the signature styles. Copyright © 2025, Association for the Advancement of artificial intelligence (***). All rights reserved.

关键词： Hypertext systems

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Using Petri Nets as an Integrated Constraint Mechanism for Reinforcement learning Tasks

Using Petri Nets as an Integrated Constraint Mechanism for R...

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IEEE Symposium on Intelligent Vehicle

作者： Timon Sachweh Pierre Haritz Thomas Liebig Faculty of Computer Science Chair of Artificial Intelligence TU Dortmund University Dortmund Germany Lamarr Institute for Machine Learning and Artificial Intelligence Dortmund Germany

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

The lack of trust in algorithms is usually an issue when using Reinforcement learning (RL) agents for control in real-world domains such as production plants, autonomous vehicles, or traffic-related infrastructure, partly due to the lack of verifiability of the model itself. In such scenarios, Petri nets (PNs) are often available for flowcharts or process steps, as they are versatile and standardized. In order to facilitate integration of RL models and as a step towards increasing AI trustworthiness, we propose an approach that uses PNs with three main advantages over typical RL approaches: Firstly, the agent can now easily be modeled with a combined state including both external environmental observations and agent-specific state information from a given PN. Secondly, we can enforce constraints for state-dependent actions through the inherent PN model. And lastly, we can increase trustworthiness by verifying PN properties through techniques such as model checking. We test our approach on a typical four-way intersection traffic light control setting and present our results, beating cycle-based baselines.

关键词： Privacy Process modeling Petri nets Process control Reinforcement learning Communication channels Production

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Estimation of Response Time with Neural Networks in Cloud Computing System 1

Estimation of Response Time with Neural Networks in Cloud Co...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： More, Rajesh Suresh, Sanjna Veerappampalayam Easwaramoorthy, Sathish Kumar Rizvi, Haider Ali Kunnathully, Kannan Gupta, Sahil K. Cloudwerx Wordwide Departmen of AIML Hyderabad India Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India School of Engineering and Technology Sunway University Selangor Darul Ehsan Malaysia Yogananda School of Artificial Intelligence Shoolini University Department of Artificial Intelligence and Machine Learning India Veltech University Department of Research Chennai India Tula's Institute Department of Computer Science and Engineering Dehradun248197 India

ISBN: (纸本)9798331518578

Resource allotment, responsiveness, connection, research of unexplored assets, related resource modeling, and asset scheduling are just a few of the numerous factors and concerns that need to be considered to enhance cloud computing efficiency. The functionality of the cloud has a significant impact on the propagation of cloud architecture. Consequently, it is important to determine the components that influence cloud function to analyze it. Response duration is the primary aspect that significantly affects cloud computation functioning, according to the research. The response duration assistance is among the numerous different variables that may be used to organize resource applications. artificial neural networks (ANNs) were employed as a technique for evaluating the parameter of pursuit, and a fork-join framework or an architecture with demand partitioning was selected as the queuing paradigm. As a result, this study suggests a load-managing method depending on cloud demand response duration using the ARIMA Estimation of Response Duration Method. The fundamental concept is to employ ARIMA methods to estimate the future response duration, providing a more efficient method of allocating resources with limited values. The results of the investigation demonstrated that the estimations were more precise compared to those that had been recently established. The results of the study indicate that estimation is an outstanding path for load balance and has possibility and value for load balance with displayed response duration. Additionally, the suggested method makes it feasible to acquire estimations for a wider range of cloud structure functioning signs compared to earlier feasible and also to extend the evaluation of the cloud platform to the instance of a structure with a non-Poisson data flow and non-exponential assistance duration. © 2025 IEEE.

关键词： Cloud platforms

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SPEAR: Design and Implementation of an Advanced Virtual Assistant 4

SPEAR: Design and Implementation of an Advanced Virtual Assi...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Jain, Garima Shukla, Amita Bairwa, Nitesh Kumar Chaudhary, Anamika Patel, Ashish Jain, Ankush Noida Institute of Engineering and Technology Department of Computer Science and Buisness Systems Greater Noida India Noida Institute of Engineering and Technology Department of Artificial Intelligence Greater Noida India Noida Institute of Engineering and Technology Department of Data Science Greater Noida India Dronacharya Group of Institutions Department of Artificial Intelligence and Machine Learning Greater Noida India Netaji Subhash University of Technology Department of Computer Science and Enginnering Delhi India

ISBN: (纸本)9798331540364

This research presents the development and evaluation of SPEAR, an advanced voice-activated personal desktop assistant designed to address challenges in existing virtual assistant technology, such as limited language support, low recognition accuracy, and inadequate task automation. Despite recent advancements in Natural Language Processing (NLP) and machine learning, implementation in current systems remains subpar. SPEAR achieves an 88% overall command execution success rate, with a speech recognition accuracy of 92% and an average response time of 1.8 seconds, demonstrating efficiency in real-time interactions. Developed using Python and machine learning libraries, SPEAR supports 15 voice commands across multiple platforms, excelling in note creation (95% success), web searching (94%), and information retrieval (92%). Its natural language understanding score of 0.86 and context retention rate of 75% highlight its conversational capabilities. While the assistant performs well in messaging functionality (85% success) and currently supports three languages, expanding its multilingual capabilities is essential for broader user engagement. Metrics indicate a satisfaction score of 8.2/10 and an average of 12 daily interactions per user, showcasing its practical utility. Additionally, the system's text-to-speech quality (8.5/10) and error recovery rate (82 %) affirm its robustness. These findings contribute valuable insights for future research, focusing on enhancing multilingual support, broadening task categories, and improving context retention, ultimately positioning SPEAR to rival established industry leaders by offering a comprehensive suite of services. © 2024 IEEE.

关键词： Speech recognition

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