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1st IEEE International Conference on Smart and Sustainable Developments in Electrical engineering, SSDEE 2025

作者： Hole, Shreyas Rajendra Kolluru, Vinothkumar Salotagi, Shreekant Challagundla, Yagnesh Mungara, Sudeep Naik, Mude Nagarjuna Bangalore India Stevens Institute of Technology Department of Data Science HobokenNJ07030 United States Dayananda Sagar University Computer Science & Engineering Bangalore India Herbert Wertheim College of Engineering University of Florida Department of Computer Science GainesvilleFL32611 United States Stevens Institute of Technology Masters in Information Systems HobokenNJ07030 United States

ISBN: (纸本)9798331542108

A smart city's transportation system can be en-hanced if there is an accurate prediction of the traffic flow which develops in an area as time goes on. This article investigates the relative accuracy of predicting traffic flow at target intersections in a city, taking advantage of two models: Tab Net and a hybrid Transformer-XGBoost. Several metrics including Mean Squared Error (MSE) and Coefficient of determination (R2 score) among others were employed to evaluate the models employed. A sequential deep learning model known as Tab Net triumphing in the competition with MSE, RMSE, and MAE being 34.48, 5.87, and 3.73 respectively, and R2 score as high as 0.917, although performing well while enhancing identification of features such as year and junction. On the other hand, while reporting similar R2 score as low as 0.184, the Transformer-XGBoost hybrid model had structuring limitations where complex interdependencies and time variations were involved recording MSE, RMSE and MAE as 338.52, 18.40 and 13.24 respectively. The graphs of residuals and QQ plots served to uncover that the hybrid model failed making a right prediction during high traffic volume days, as general tabnet results shown in the final graphs are more stable. Following investigation of the models in this study, the researchers suggest implementing TabNet as a better option for traffic management explaining its high accuracy and providing valuable guidance for cities. This would result in improvement of the traffic flow prediction, which is highly needed for effective urban mobility solutions. © 2025 IEEE.

关键词： Smart city

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Real-Time Traffic Flow Optimization using Adaptive IoT and data Analytics: A Novel DeepStreamNet Model

Real-Time Traffic Flow Optimization using Adaptive IoT and D...

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Sustainable Expert systems (ICSES), International Conference on

作者： V.S. Saranya Garimidi Subbarao Dega Balakotaiah Maloth Bhavsingh K. Suresh Babu Srinivasa Rao Dhanikonda Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai Department o Computer Science and Engineering (AIDS) Pace Institute of Technology and Sciences Department of Computer Science and Engineering Vignan's Founadation for Science Technology & Research (Deemed to be University) Department of Computer Science and Engineering Ashok: Womens Engineering College Kurnool Andhra Pradesh Department of Computer Science and Engineering Narasaraopeta Engineering College(Autonomous) Narasaraopet A.P Department of Artificial Intelligence and Data Science Faculty of Science and Technology (IcfaiTech) ICFAI Foundation for Higher Education (Deemed to Be University) Hyderabad Telangana India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Thisstudy introduces the DeepStreamNet model, an advanced framework for enhancing real-time traffic management in urban environments using adaptive IoT and sophisticated big data analytics. Central to our approach is the integration of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) units, which synergize with IoT devices to facilitate dynamic adaptation to fluctuating traffic conditions. The model excels in operational performance, achieving a notable accuracy of 94.7% in congestion prediction with an exceptionally low latency of only 45 ms per decision cycle and the capability to process up to 50,000 data points per second. These technical achievements have marked significant improvements over traditional traffic management systems, enhancing traffic flow efficiency and reducing congestion effectively. The results underscore the transformative potential of the DeepStreamNet model in urban traffic management and public safety, offering actionable insights for intelligent transportation systems and setting the stage for more informed urban planning. Looking forward, we suggest extending the application of our model to areas such as logistics and broader smart city initiatives, highlighting its adaptability and broad utility in various urban contexts

关键词： Adaptation models Analytical models Accuracy Smart cities Urban planning data models Real-time systems Internet of Things Optimization Context modeling

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Trans-Bifurcation Prediction of Dynamics in terms of Extreme Learning Machines with Control Inputs

arXiv

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arXiv 2024年

作者： Tadokoro, Satoru Yamaguchi, Akihiro Namiki, Takao Tsuda, Ichiro Education and Research Center for Mathematical and Data Science Hokkaido University Sapporo Japan Department of Information and Systems Engineering Fukuoka Institute of Technology Fukuoka Japan Department of Mathematics Faculty of Science Hokkaido University Sapporo Japan AIT Center Sapporo City University Sapporo Japan

By extending the extreme learning machine by additional control inputs, we achieved almost complete reproduction of bifurcation structures of dynamical systems. The learning ability of the proposed neural network system is striking in that the entire structure of the bifurcations of a target one-parameter family of dynamical systems can be nearly reproduced by training on transient dynamics using only a few parameter values. Moreover, we propose a mechanism to explain this remarkable learning ability and discuss the relationship between the present results and similar results obtained by Kim et al. Predicting the behaviors of dynamical systems is critical in various scientific fields. The recent advancements in machine learning have enabled the prediction of dynamical system trajectories for fixed parameter values. However, the accuracy of predicting behavioral changes due to changes in parameter values remains insufficient. In this study, we extend the extreme learning machine, a neural network-based technique, to enhance its capabilities for predicting behavioral changes in response to changes in parameter values. We subsequently evaluate the predictive performance of this extended model for a variety of dynamical systems, thereby elucidating its underlying mechanisms. © 2024, CC BY.

关键词： Adversarial machine learning

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Multi-behavior Learning for Socially Compatible Autonomous Driving

Multi-behavior Learning for Socially Compatible Autonomous D...

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International Conference on Intelligent Transportation

作者： Sanjula Jayawardana Vindula Jayawardana Kaneeka Vidanage Cathy Wu University of Westminster UK Informatics Institute of Technology Colombo Sri Lanka Department of Electrical Engineering and Computer Science MIT Laboratory for Information & Decision Systems Cambridge MA USA Department of Civil and Environmental Engineering MIT Laboratory for Information Decision Systems Institute of Data Systems and Society Cambridge MA USA

Autonomous vehicles (AVs) have the potential to revolutionize transportation, but their effective integration into the real world requires addressing the challenge of interacting with human drivers. Real-world driving involves negotiating and cooperating with fellow drivers through social cues, necessitating AVs to also demonstrate such social compatibilities. However, despite the popularity, current learning-based control methods for AV policy synthesis often overlook this crucial aspect. In this work, we look at the problem of enabling socially compatible driving when AV control policies are learned. We leverage human driving data to learn a social preference model of human driving and then integrate it with reinforcement learning-based AV policy synthesis using Social Value Orientation theory. In particular, we propose to use multi-task reinforcement learning to learn diverse social compatibility levels in driving (ex: altruistic, prosocial, individualistic, and competitive), focusing on the requirement of having diverse behaviors in real-world driving. Using highway driving scenarios, we demonstrate through experiments that socially compatible AV driving not only enables naturalistic driving behaviors but also reduces collision rates from the baseline. Our findings reveal that without social compatibility, AV policies tend to adopt dangerously competitive driving behaviors, while the incorporation of social compatibility fosters smoother vehicle maneuvers.

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Bridging Linguistics and Machine Learning: A New Human-Machine Communication

Bridging Linguistics and Machine Learning: A New Human-Machi...

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Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI), International Conference on

作者： Kawaljit Kaur Suman Chahar Manika Manwal Kuldeep Singh Kaswan Jagjit Singh Dhatterwal University Institute of Computing Chandigarh University Mohali India Department of Computer Science and Engineering Graphic Era Hill University Research Scholar Graphic Era Deemed to be University Dehradun India Department of Computer Science and Engineering Galgotias University Greater Noida India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Andhra Pradesh India

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

This research paper explores the evolution of Natural Language Processing (NLP) and Computational Linguistics, tracing the transition from grammar-based approaches to the adoption of machine learning methods. Initially rooted in linguistic theories, early NLP systems relied on rule-based methods for language parsing and processing, albeit with limitations in handling natural language complexities. The advent of machine learning brought about a paradigm shift, empowering NLP systems to learn patterns directly from data, leading to more adaptable and robust language processing capabilities. Hybrid approaches, blending rule-based techniques with machine learning, have emerged as a synergistic solution, combining the interpretability of rules with the learning capacity of algorithms. Recent advancements in NLP, including transformer models and multimodal techniques, have further accelerated progress, enabling state-of-the-art systems capable of human-level performance across diverse applications. Looking forward, future directions in NLP encompass neural symbolic approaches, self-supervised learning, multimodal NLP, continual learning, and ethical considerations, promising further innovation in the field. Through interdisciplinary collaboration and a commitment to responsible AI, the evolution of NLP continues to bridge the gap between human language and machine understanding, shaping a future where intelligent communication between humans and machines is seamless and ubiquitous.

关键词： Ethics Technological innovation Adaptation models Collaboration Machine learning Self-supervised learning Linguistics Transformers Natural language processing Complexity theory

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Heuristic Solution to Joint Deployment and Beamforming Design for STAR-RIS Aided Networks

arXiv

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arXiv 2024年

作者： Yan, Bai Zhao, Qi Zhang, Jin Zhang, J. Andrew Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Global Big Data Technologies Centre University of Technology SydneyNSW2007 Australia

This paper tackles the deployment challenges of Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS) in communication systems. Unlike existing works that use fixed deployment setups or solely optimize the location, this paper emphasizes the joint optimization of the location and orientation of STAR-RIS. This enables searching across all user grouping possibilities and fully boosting the system’s performance. We consider a sum rate maximization problem with joint optimization and hybrid beamforming design. An offline heuristic solution is proposed for the problem, developed based on differential evolution and semi-definite programming methods. In particular, a point-point representation is proposed for characterizing and exploiting the user-grouping. A balanced grouping method is designed to achieve a desired user grouping with low complexity. Numerical results demonstrate the substantial performance gains achievable through optimal deployment design. Copyright © 2024, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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Hybrid Approach of TabNet and Transformer-XGBoost for Predicting Traffic Flow in Smart Cities

Hybrid Approach of TabNet and Transformer-XGBoost for Predic...

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Smart and Sustainable Developments in Electrical engineering (SSDEE), IEEE International Conference on

作者： Shreyas Rajendra Hole Vinothkumar Kolluru Shreekant Salotagi Yagnesh Challagundla Sudeep Mungara Mude Nagarjuna Naik Computer Science & Engineering (AI&ML) Dayananda Sagar University Bangalore India Department of data science Stevens Institute of technology Hoboken NJ U.S.A. Computer Science & Engineering Dayananda Sagar University Bangalore India Department of Computer science Herbert Wertheim College of Engineering University of Florida Gainesville Florida USA Masters in Information Systems Stevens Institute of Technology Hoboken NJ U.S.A.

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

关键词： Measurement Hands Adaptation models Accuracy Smart cities Transportation Tail Predictive models Transformers Junctions

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A Design of Hybrid Model and Bayesian Neural Networks for Smart Grid Stability Prediction

A Design of Hybrid Model and Bayesian Neural Networks for Sm...

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Smart and Sustainable Developments in Electrical engineering (SSDEE), IEEE International Conference on

作者： Shreyas Rajendra Hole Vinothkumar Kolluru Shreekant Salotagi Yagnesh Challagundla Sudeep Mungara Sriramkumar R Computer Science & Engineering AI & ML Dayananda Sagar University Bangalore India Department of data science Stevens Institute of technology Hoboken NJ U.S.A. Computer Science & Engineering Dayananda Sagar University Bagalore India Department of Computer science Herbert Wertheim College of Engineering University of Florida Gainesville Florida USA Masters in Information Systems Stevens Institute of Technology Hoboken NJ U.S.A.

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

The latest evolution in power systems, is ‘smart grids'that offers real time monitoring, control features as well as effective management of renewable energy sources. Nonetheless, with the increase in the system complexity, the chances for insta- bility also increase and thus it is crucial to forecast the stability accurately for the safety of the grid. Weather it is in terms of Gradient Boosting Tree Algorithm or any other traditional algorithm, these machine learning models are successful in achieving the objectives of the task but they lack a forthcoming metric to assist in high stake situations, uncertainty quantification. The paper is devoted to the analysis of the reliability of smart grid systems based on Bayesian Neural Networks (BNN) taking into account both the reliability of predictive modeling and forecasting uncertainty. The smart grid stability augmented dataset was used to benchmark the efficacy of using BNNs or BNNs combined hybrid models, one with XGBoost, for feature extraction and the other with LightGBM. The present study shows that the BNN used with the above mentioned smart grid system achieved an accuracy of 92% with precision of 0.89, recall of 0.91 and F1 of 0.90. These metrics are appending with the help of the additional features of BNN where they are capable of addressing the key questions around uncertainty and thus the metrics confirming the predictions are quite strong in terms of real-time grid stability evaluation. This article encapsulates a detailed study of BNN architecture, the role of uncertainty in machine learning models and the natural application of such models in the design of dependable and fault-tolerant smart grids.

关键词： Measurement Renewable energy sources Uncertainty Neural networks Power system stability Predictive models Stability analysis Real-time systems Smart grids Bayes methods

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Deepfakes: A Review on Technologies, Applications and Strategies

Deepfakes: A Review on Technologies, Applications and Strate...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Kuldeep Singh Kaswan Kiran Malik Jagjit Singh Dhatterwal Mahaveer Singh Naruka D Govardhan School of Computing Science and Engineering Galgotias University Greater Noida UP India Department of Computer Science and Engineering MRIEM Rohtak India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Andhra Pradesh India G L Bajaj Institute of Technology and Management Greater Noida India Department of Aeronautical Engineering Institute of Aeronautical Engineering Hyderabad

Deepfakes are artificial intelligence-generated multimedia, primarily videos or audio, that effectively edit or invent information by superimposing one person's likeness or voice onto that of another. They use deep learning techniques such as GAN s to generate hyper-realistic simulations that blur the line between reality and fiction. Deepfakes encompass everything from political satire to possible concerns in the fields of disinformation, privacy invasion, and cybersecurity. Understanding deepfakes is critical in an age when manipulated media may influence public opinion and undermine confidence. The primary objective of this paper is to provide a holistic overview of deepfake technology, shedding light on both its creative potential and inherent risks. Through a systematic exploration of the evolution, applications, techniques, and detection strategies, we aim to equip readers with an informed perspective on this ever-evolving field. By synthesizing existing knowledge and insights, this paper intends to contribute to the ongoing discourse surrounding deepfakes, enabling stakeholders to make informed decisions, develop effective countermeasures, and anticipate the future trajectory of this technology. Ultimately, our work strives to foster awareness and preparedness in an age where discerning the truth has never been more vital.

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An Exploratory Investigation of Log Anomalies in Unmanned Aerial Vehicles

An Exploratory Investigation of Log Anomalies in Unmanned Ae...

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International Conference on Software engineering (ICSE)

作者： Dinghua Wang Shuqing Li Guanping Xiao Yepang Liu Yulei Sui Pinjia He Michael R. Lyu University of Technology Sydney Australia Southern University of Science and Technology China The Chinese University of Hong Kong China Nanjing University of Aeronautics and Astronautics China Department of Computer Science and Engineering Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology China The University of New South Wales Australia School of Data Science The Chinese University of Hong Kong Shenzhen (CUHK-Shenzhen) China Shenzhen Research Institute of Big Data China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Unmanned aerial vehicles (UAVs) are becoming increasingly ubiqui-tous in our daily lives. However, like many other complex systems, UAVs are susceptible to software bugs that can lead to abnormal system behaviors and undesirable consequences. It is crucial to study such software bug-induced UAV anomalies, which are often mani-fested in flight logs, to help assure the quality and safety of UAV systems. However, there has been limited research on investigating the code-level patterns of software bug-induced UAV anomalies. This impedes the development of effective tools for diagnosing and localizing bugs within UAV system code. To bridge the research gap and deepen our understanding of UAV anomalies, we carried out an empirical study on this subject. We first collected 178 real-world abnormal logs induced by soft-ware bugs in two popular open-source UAV platforms, i.e., PX4 and Ardupilot. We then examined each of these abnormal logs and com-piled their common patterns. In particular, we investigated the most severe anomalies that led to UAV crashes, and identified their features. Based on our empirical findings, we further summarized the challenges of localizing bugs in system code by analyzing anoma-lous UAV flight data, which can offer insights for future research in this field.

关键词： Bridges Codes Computer bugs Autonomous aerial vehicles Software Safety Complex systems

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