The success and safety of block cipher systems heavily depend on how efficient and secure their Key Schedule Algorithms (KSAs) are, especially when fighting against cryptanalytic attacks. This paper proposes a novel K...
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Collection of waste is one of the important goals of Waste Management Unit (WMU), where collecting waste decreases the amount of time, expenses, and the impact of waste collectors on the environment. The work is to cr...
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ISBN:
(数字)9798331538538
ISBN:
(纸本)9798331538545
Collection of waste is one of the important goals of Waste Management Unit (WMU), where collecting waste decreases the amount of time, expenses, and the impact of waste collectors on the environment. The work is to create a set of routes for waste collection that will take minimal time to get through several collection centers given the capabilities of the collection vehicles, and road systems and traffic conditions. For waste collection points, ten major stations in Bengaluru were selected along with the disposal point. For this purpose, several algorithms such as Dijkstra's Algorithm, Bellman-Ford Algorithm, Floyd-Warshall Algorithm, Johnson's Algorithm, Nearest Neighbour Algorithm, and Ant Colony Optimization (ACO) were designed and evaluated. Algorithms were measured based on parameters such as the time taken, the fuel consumed, the CO2 produced, the effectiveness of the trip that was made. The theoretical and applied objectives of the project correlate with the goals of the integration of improved optimization possibilities with operational issues with the purpose of defining such effective methods in relation to the containment of fuel consumption, the decrease in emissions and the waste management optimization in the urban environment.
IoT devices, constrained by limited resources and weak security measures, are highly vulnerable to malware at- tacks. This review examines malware detection methods using textual, visual, and network traffic features,...
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ISBN:
(数字)9798331510299
ISBN:
(纸本)9798331510305
IoT devices, constrained by limited resources and weak security measures, are highly vulnerable to malware at- tacks. This review examines malware detection methods using textual, visual, and network traffic features, with experiments fo- cused on network traffic data. Feature extraction techniques such as correlation analysis, eXtreme Gradient Boosting(XGBoost), and the Firefly algorithm were applied to machine learning models, including logistic regression, random forest, Adaptive Boosting(AdaBoost), and perceptron. Results highlight the poten- tial of these approaches for resource-constrained environments.
Recognizing architectural styles is essential for preserving & understanding cultural heritage, as it helps categorize & document diverse structures, highlighting their historical, cultural & artistic valu...
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ISBN:
(数字)9798331523893
ISBN:
(纸本)9798331523909
Recognizing architectural styles is essential for preserving & understanding cultural heritage, as it helps categorize & document diverse structures, highlighting their historical, cultural & artistic value. The study addresses the gap between technology & heritage by developing a framework for classifying 8 architectural styles: Buddhist, Indo Islamic, Rajput, Dravidian, Hindu, Sikh, British & Modern. Using the HOG method for feature extraction, the models capture intricate architectural details. Classification is done using machine learning models like Logistic Regression, Random Forest & XG-Boost, as well as advanced deep learning models such as DenseNet & InceptionV3. DenseNet achieves the highest accuracy of 86%, followed by InceptionV3 at 79%, showing the capability of deep learning in handling complex visual data. The study not only compares model performance but also provides a scalable method for architectural style classification that contributes to the heritage preservation.
Cervical cytology image segmentation is a crucial component in the automated analysis of cervical cytology screening. This research investigates the efficacy of federated learning against traditional learning methods ...
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The integration of IoT technology provides realtime, data-driven monitoring that reduces waste and maintains the quality of the food, changing the way food is preserved and managed. This research focuses on the develo...
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ISBN:
(数字)9798331520762
ISBN:
(纸本)9798331520779
The integration of IoT technology provides realtime, data-driven monitoring that reduces waste and maintains the quality of the food, changing the way food is preserved and managed. This research focuses on the development and implementation of an Arduino-based IoT device for monitoring freshness, temperature, and humidity in storage food environments. Equipped with an MQ-4 methane sensor and DHT11 sensor, the system detects spoilage indicators and environmental conditions and opens an exhaust fan if the moisture has been in excess. The BLYNK IoT app provides instant information to the user about storage conditions, thus ensuring efficient and proactive food management. This product finds answers to some of the main food-related storage challenges by offering scalable and effective solutions for enhanced freshness and reduced spoilage.
This research focuses on generating image captions using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models. As deep learning advances, the availability of large datasets and increased comput...
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ISBN:
(数字)9798331509828
ISBN:
(纸本)9798331509835
This research focuses on generating image captions using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models. As deep learning advances, the availability of large datasets and increased computing power make it more feasible to build models capable of creating captions for images. This paper uses CNN and RNN models in Python to achieve this. Image captioning combines image recognition and Natural Language Processing (NLP) to interpret image context and express it in English, drawing on core computer vision principles. This study reviews important concepts in image captioning, including the applications of Keras, NumPy, and Jupyter notebook for development. This paper also explores the use of the Flickr dataset and CNN for image classification. The BLEU score of the proposed models is found to be close to 60%. On further enhancement to the model, it could be achieved to get a better score.
Human action recognition is one of the most challenging and attractive areas in the field of computer vision. Conventional research on human action recognition has mainly focused on data modality of video or optical f...
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This paper focuses on the fault detection and diagnosis of terminal units (TUs) in a building located in London, utilizing real operational historical data to assess their performance and optimal placement across mult...
This paper focuses on the fault detection and diagnosis of terminal units (TUs) in a building located in London, utilizing real operational historical data to assess their performance and optimal placement across multiple floors. While precise locations of the TUs are unavailable, our method analyzes their operational behaviour for one month, applying popular machine learning models to detect and analyze faults effectively. By examining each TU individually and in the aggregate, we identify behavioural patterns that inform decisions regarding their positioning within the building. The dataset comprises over 2 million data points collected from 730 TUs, enabling a comprehensive analysis of their functionality and the impact of suboptimal thermostat placements. Our study employs three machine learning models-traditional multi-class Support Vector Machines and two ensemble methods: Random Forest (RF), and Adaptive Boosting (AdaBoost)-to classify TU behaviors into normal operation, heating faults, and cooling faults. Results indicate that RF outperforms the other models with an accuracy of 99.89%, while AdaBoost achieves an accuracy of 85% and SVM shows 47% accuracy. The findings underscore the potential of a data-driven approach to inform retrofitting decisions and enhance the reliability of HVAC systems. This research contributes valuable knowledge toward optimizing TU placement, ultimately leading to improved energy efficiency and indoor environmental quality.
Vehicular Ad-hoc Networks (VANETs) are central to Intelligent Transportation Systems (ITS), facilitating vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. Energy consumption, latency, and rel...
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ISBN:
(数字)9798331535193
ISBN:
(纸本)9798331535209
Vehicular Ad-hoc Networks (VANETs) are central to Intelligent Transportation Systems (ITS), facilitating vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. Energy consumption, latency, and reliability remain challenges. This paper presents a power-aware VANET prototype that combines optimized routing protocols (OLSR, DSR, AODV), adaptive beaconing, and energy-saving methods such as low packet transmission rates (0.2s), minimized safety message sizes (100 bytes), and low-power idle states (0.009A). Simulations with NS-3 and SUMO exhibit a 22% decrease in energy usage, 15% reduced latency, and 12% better packet delivery ratio over baseline protocols. These developments lead to scalable, dependable, and energy-efficient ITS solutions.
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