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Smart automated highway lighting system using IoT: a survey

Energy Informatics 2024年第1期7卷 1-28页

作者： Achar, Tejaswini Eshwar Rekha, C. Shreyas, J. Department of Computer Science and Engineering Government Engineering College Koppal Talakal India Department of Information Science and Engineering Bapuji Institute of Engineering and Technology Davanagere India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

Efficient highway lighting is crucial for ensuring road safety and reducing energy consumption and costs. Traditional highway lighting systems rely on timers or simple photosensors, leading to inefficient operation by illuminating lights when not needed or failing to adjust to changing conditions. The emergence of the Internet of Things (IoT) and related technologies has enabled the development of smart automated highway lighting systems that can dynamically control illumination levels based on real-time data. This paper provides a comprehensive review of the current state-of-the-art in smart automated highway lighting systems employing IoT technologies. Key components, communication protocols, data processing techniques, and lighting control strategies are discussed. The integration of renewable energy sources and energy storage systems is explored for environmentally sustainable operations. Practical implementation case studies are analyzed to highlight benefits and challenges. Open research issues and future directions for further enhancements are identified. © The Author(s) 2024.

关键词： Street lighting

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Forecasting Directional Movement of Stock Prices using Deep Learning

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Annals of Data science 2023年第5期10卷 1361-1378页

作者： Chandola, Deeksha Mehta, Akshit Singh, Shikha Tikkiwal, Vinay Anand Agrawal, Himanshu Department of Electrical Engineering and Computer Science York University Toronto Canada Hebert Werthim College of Engineering University of Florida Gainesville Gainesville United States Department of Electronics and Communication Engineering Jaypee Institute of Information Technology Noida India Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Stock market’s volatile and complex nature makes it difficult to predict the market situation. Deep Learning is capable of simulating and analyzing complex patterns in unstructured data. Deep learning models have applications in image recognition, speech recognition, natural language processing (NLP), and many more. Its application in stock market prediction is gaining attention because of its capacity to handle large datasets and data mapping with accurate prediction. However, most methods ignore the impact of mass media on the company’s stock and investors’ behaviours. This work proposes a hybrid deep learning model combining Word2Vec and long short-term memory (LSTM) algorithms. The main objective is to design an intelligent tool to forecast the directional movement of stock market prices based on financial time series and news headlines as inputs. The binary predicted output obtained using the proposed model would aid investors in making better decisions. The effectiveness of the proposed model is assessed in terms of accuracy of the prediction of directional movement of stock prices of five companies from different sectors of operation. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Large dataset

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An effective object detection and tracking using automated image annotation with inception based faster R-CNN model

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 343-356页

作者： Vijiyakumar, K. Govindasamy, V. Akila, V. Department of Computing Technologies School of computing SRM institute of Science and Technology Kattankulathur campus Tamilnadu Chengalpattu district India Department of Information Technology Pondicherry Engineering College Pondicherry India Department of Computer Science and Engineering Pondicherry Engineering College Pondicherry India

The present study advances object detection and tracking techniques by proposing a novel model combining Automated Image Annotation with Inception v2-based Faster RCNN (AIA-IFRCNN). The research methodology utilizes the DCF-CSRT model for image annotation, Faster RCNN for object detection, and the inception v2 model for feature extraction, followed by a softmax layer for image classification. The proposed AIA-IFRCNN model is evaluated on three benchmark datasets: Bird (Dataset 1), UCSDped2 (Dataset 2), and Under Water (Dataset 3), to determine prediction accuracy, annotation time, Center Location Error (CLE), and Overlap Rate (OR). The experimental results indicate that the AIA-IFRCNN model outperformed existing models regarding detection accuracy and tracking performance. Notably, it achieved a maximum detection accuracy of 95.62 % on Dataset 1, outperforming other models. Additionally, it achieved minimum average CLE values of 4.16, 5.78, and 3.54, and higher overlap rates of 0.92, 0.90, and 0.94 on the respective datasets (1, 2 and 3). Hence, this research work on object detection and tracking using the AIA-IFRCNN model is essential for improving system efficiency and fostering innovation in the field of computer vision and object tracking. © 2024

关键词： Image annotation

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Machine Learning Based Alzheimer’s Disease Detection: A Comprehensive Approach 7th

Machine Learning Based Alzheimer’s Disease Detection: A Com...

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7th IFIP TC 12 International Conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Periya Nayaki, A. Vidyabharathi, A.K. Krishnaveni, S. Thanabal, M.S. Department of Information Technology Velammal College of Engineering and Technology Madurai India Computer Science and Engineering PSNA College of Engineering and Technology Madurai India

ISBN: (纸本)9783031699818

Alzheimer’s disease typically leads in the death of neurons and the connections between them in memory-related parts of the brain, such as the entorhinal cortex and hippocampus. Following that, it affects the areas of the cerebral cortex responsible for language, cognition, and social interaction. Several other regions of the brain get harmed at some time. Patients with Alzheimer’s disease gradually lose their capacity to function normally and go about their daily lives. Currently, age is regarded as one of the most important risk factors;the majority of patients with dementia are 65 or older. Symptoms of the condition include increased disorientation, memory loss, including the inability to recall recent events or personal history, and learning difficulties. As of yet, there is no treatment for Alzheimer’s. A vast quantity of data will be acquired from the previously affected person, and the user input data will be compared to past data using machine learning techniques to determine whether they are still impacted. Finding the best categorization approach is difficult because it necessitates adapting to regional datasets. The methods LightGBM, AdaBoost, Random Forest Classifier, Support-Vector-Machine, Decision-Tree, and XGBoost provided accuracy rates of 78%, 86%, 81%, 73%, and 84%, respectively. In this work, the Voting Classifier algorithm, which may be more accurate than other methods, will be employed. © IFIP International Federation for information Processing 2024.

关键词： Decision trees

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Speech Emotion Recognition based on Multi COATTENTION Acoustic Feature Fusion 2

Speech Emotion Recognition based on Multi COATTENTION Acoust...

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2nd International Conference on Data science and information System, ICDSIS 2024

作者： Song, Hongchen Kang, Kuo Wei, Yuhua Zhang, Hengyuan Zhang, Long Tianjin Normal University College Of Computer And Information Engineering Tianjin China Guangzhou Huali Science And Technology Vocational College College Of Computer Information Engineering Guangdong Guangzhou China

ISBN: (纸本)9798350381665

Speech Emotion Recognition (SER) is a technology aimed at extracting emotional information from speech signals, thereby discerning the emotional state of the speaker. Its objective is to equip machines with empathetic capabilities akin to those of humans. The ability to extract high-quality, deep, and comprehensive speech representations from audio often determines the robustness of emotion analysis. In this paper, we focus on modeling speech using spectrograms, MFCCs (Mel-frequency cepstral coefficients), and raw waveforms. We employ CNNs (Convolutional Neural Networks), BiGRUs (Bidirectional Gated Recurrent Units), and self-supervised Wav2Vec-based models [1] to extract deep acoustic features, each applied separately. Taking into account the complementarity among different acoustic features, we introduce a Multi CO-ATTENTION Mechanism to enhance the acoustic characteristics. Additionally, feature fusion is performed at the final stage of the model. We evaluated our proposed method on the IEMOCAP dataset and conducted ablation studies, achieving promising results. © 2024 IEEE.

关键词： Quality control

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Data Analytics in Farming: Crop Yield Projection in Telangana State Employing Machine Learning Methods 5th

Data Analytics in Farming: Crop Yield Projection in Telangan...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Himaanshu, V. Suryanarayana, G. Prakash, L.N.C.K. Vignesh, Vamshi Srikar, B. Sai Computer Science and Engineering Vardhaman College of Engineering Hyderabad India Information Technology Vardhaman College of Engineering Hyderabad India CVR College of Engineering Hyderabad India

ISBN: (纸本)9789819780426

Telangana state is in the southern part of India where the Godavari and Krishna rivers flow providing important water resources for agriculture. Agriculture is the primary occupation of the major population in the state, and it is also one of the essential sectors that majorly contributes to the state’s economy. Accurate estimation of crop yield production is essential for sustainable agricultural development, food security, and poverty reduction. Therefore, various supervised machine learning procedures have been used to forecast the crop yield production in Telangana State. In this work, various algorithms are applied to the Telangana crop yield dataset. The results generated by these algorithms have been analysed and compared. With the help of crop yield analysis, farmers know their yield before their farming practice so that they can reveal and resolve their crop yield challenges. This article suggests a machine learning algorithm for the farmers to detect and solve their crop yield problems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Sentiment Analysis of Amazon Alexa Product Reviews: A Comprehensive Comparative Study of Learning Algorithms 5th

Sentiment Analysis of Amazon Alexa Product Reviews: A Compre...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Rao, Gouravelli Akshith Prakash, L.N.C.K. Suryanarayana, G. Joshua, Pathi Varun Reddy, Katta Nithin Kumar Karnati, Ramesh Computer Science and Engineering Vardhaman College of Engineering Hyderabad India CVR College of Engineering Hyderabad India Information Technology Vardhaman College of Engineering Hyderabad India

ISBN: (纸本)9789819780426

Sentiment analysis is part of text mining, which means extracting data from comments on specific products and people’s opinions or sentiments. It involves considering opinions, analysis, and suggestions to customers about Amazon Alexa products by classifying a review as positive, negative, or neutral. This work is to explore different learning algorithms to solve sentiment analysis on Amazon products. The proposed work is a comparative analysis of existing learning algorithms that can help businesses in real-time decision-making by providing insights into customer preferences, needs, and choices which in turn results in the growth of the company and enhancement of customer satisfaction. The existing works provided the accuracy of classification to a level of 80%. Algorithms used in previous projects are SVM, Naïve Bayes, Random Forest, and other learning algorithms. In this work, logistic regression, Gradient boosting, Multinominal NB, and Gaussian NB are used along with previously implemented algorithms for sentiment analysis. Deep learning algorithms such as Long Short-Term Memory (LSTM) and ANN with dropout and without dropout layers are also implemented and compared with machine learning algorithms. Word2Vec is employed with LSTM to increase the accuracy of the representation. Based on the evaluation of their metrics, accuracies are compared. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Logistic regression

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Artificial intelligence-inspired comprehensive framework for desertification prediction

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International Journal of Cognitive Computing in engineering 2025年 6卷 462-476页

作者： Alsubai, Shtwai Mosleh, Mogeeb A.A. Al-Hadhrami, Suheer A. Bhatia, Munish College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Alkharj Saudi Arabia Faculty of Engineering & Computing University of Science & Technology Aden Yemen Faculty of Engineering and Information Technology Taiz Univeristy Taiz Yemen Department of Computer Engineering College of Engineering Al Mukalla Hadhramout Yemen Department of Computer Applications National Institute of Technology Kurukshetra India

Desertification greatly affects land deterioration, farming efficiency, economic growth, and health, especially in Gulf nations. Climate change has worsened desertification, making developmental issues in the area even more difficult. This research presents an enhanced framework utilizing the Internet of Things (IoT) for ongoing monitoring, data gathering, and analysis to evaluate desertification patterns. The framework utilizes Bayesian Belief Networks (BBN) to categorize IoT data, while a low-latency processing method on edge computing platforms enables effective detection of desertification trends. The classified data is subsequently analyzed using an Artificial Neural Network (ANN) optimized with a Genetic Algorithm (GA) for forecasting decisions. Using cloud computing infrastructure, the ANN-GA model examines intricate data connections to forecast desertification risk elements. Moreover, the Autoregressive Integrated Moving Average (ARIMA) model is employed to predict desertification over varied time intervals. Experimental simulations illustrate the effectiveness of the suggested framework, attaining enhanced performance in essential metrics: Temporal Delay (103.68 s), Classification Efficacy—Sensitivity (96.44 %), Precision (95.56 %), Specificity (96.97 %), and F-Measure (96.69 %)—Predictive Efficiency—Accuracy (97.76 %) and Root Mean Square Error (RMSE) (1.95 %)—along with Reliability (93.73 %) and Stability (75 %). The results of classification effectiveness and prediction performance emphasize the framework's ability to detect high-risk zones and predict the severity of desertification. This innovative method improves the comprehension of desertification processes and encourages sustainable land management practices, reducing the socio-economic impacts of desertification and bolstering at-risk ecosystems. The results of the study hold considerable importance for enhancing regional efforts in combating desertification, ensuring food security, and formulatin

关键词： Life on land Climate action Desertification Internet of things Artificial neural network

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Context-Aware Practice Problem Recommendation Using Learners’ Skill Level Navigation Patterns

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Intelligent Automation & Soft Computing 2023年第3期35卷 3845-3860页

作者： P.N.Ramesh S.Kannimuthu Computer Science and Engineering Karpagam Institute of TechnologyCoimbatore641105India Information Technology Karpagam College of EngineeringCoimbatore641032India

The use of programming online judges(POJs)has risen dramatically in recent years,owing to the fact that the auto-evaluation of codes during practice motivates students to learn *** POJs have greater number of pro-gramming problems in their repository,learners experience information *** systems are a common solution to information *** recommender systems used in e-learning platforms are inadequate for POJ since recommendations should consider learners’current context,like learning goals and current skill level(topic knowledge and difﬁculty level).To overcome the issue,we propose a context-aware practice problem recommender system based on learners’skill level navigation *** system initially performs skill level navigation pattern mining to discover frequent skill level navigations in the POJ and toﬁnd learners’learning ***ﬁltering(CF)and con-tent-basedﬁltering approaches are employed to recommend problems in the cur-rent and next skill levels based on frequent skill level navigation *** sequence similarity measure is used toﬁnd the top k neighbors based on the sequence of problems solved by the *** experiment results based on the real-world POJ dataset show that our approach considering the learners’cur-rent skill level and learning goals outperforms the other approaches in practice problem recommender systems.

关键词： Recommender systems skill level navigation pattern programming online judge collaborativeﬁltering content-basedﬁltering

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Resource Allocation in Non-Cooperative Game Models with Reinforcement Learning

Resource Allocation in Non-Cooperative Game Models with Rein...

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2024 China Automation Congress, CAC 2024

作者： Wang, Linyao He, Qiyong Chen, Kai Li, Xiuxian Liang, Li Wang, Youqing College of Information Science and Technology Beijing University of Chemical Technology Beijing China College of Electronics and Information Engineering Tongji University Shanghai China College of Electronics and Information Engineering SRIAS Tongji University Shanghai China

ISBN: (纸本)9798350368604

This study proposes a non-cooperative game model that includes a two-layer supply and demand relationship and employs reinforcement learning for its *** study takes the water resource allocation in Pujiang County, Jinhua City, Zhejiang Province, as a case study, constructing a non-cooperative game model with reservoirs, water plants, and factories as the main entities. It simulates the strategic interactions among various water users to optimize the water resource allocation plan. This model takes into account the scarcity of water resources, the dynamic changes in demand, and the fairness of distribution. By introducing the CommFormer reinforcement learning algorithm, the model achieves policy optimization in complex and dynamic environments, reduces computational complexity, and speeds up decision-making response time. © 2024 IEEE.

关键词： Reinforcement learning

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