Administration recommender frameworks have been demonstrated as important apparatuses for giving fitting suggestions to clients. In the most recent decade, the measure of clients, administrations and online data has developed quickly, yielding the huge information examination issue for administration recommender frameworks. Subsequently, conventional administration recommender frameworks frequently experience the ill effects of adaptability and wastefulness issues when transforming or dissecting such expansive scale information. In addition, the majority of existing administration recommender frameworks show the same evaluations and rankings of administrations to diverse clients without considering various clients' inclination, and thusly neglects to meet clients' customized prerequisites. In this paper, we propose a Keyword-Aware Service Recommendation system, named KASR, to address the above difficulties. It goes for showing a customized administration suggestion rundown and prescribing the most fitting administrations to the clients adequately. In particular, catchphrases are utilized to show clients' inclination, and a client based Collaborative Filtering calculation is received to create proper suggestions. To enhance its versatility and effectiveness in enormous information environment, KASR is actualized on Hadoop, a broadly received dispersed processing stage utilizing the MapReduce parallel handling ideal model. At long last, broad tests are directed on genuine information sets, and results exhibit that KASR fundamentally enhances the exactness and adaptability of administration recommender frameworks over existing methodologies.
Abstract Wireless sensor networks (WSNs) play a critical role in applications such as wildlife monitoring, disaster recovery, and precision agriculture, where continuous coverage and longevity are paramount amidst dynamic environmental challenges. To address these demands, the cellular adaptive energy forecasting and coverage optimization (CAEFCO) framework integrates localized neuro-symbolic energy forecasting (LNS-EF), a novel concept that combines symbolic reasoning with neural network learning directly on sensor nodes. LNS-EF enables nodes to not only predict energy depletion based on past consumption patterns and environmental factors but also incorporate rule-based contextual reasoning for enhanced decision-making. Alongside this, CAEFCO employs an anomaly detection module that identifies disruptions, such as sensor damage or environmental interference, allowing real-time task redistribution. This dual approach ensures seamless task reallocation while extending network lifetime. CAEFCO’s proactive methodology demonstrates a 97% reduction in data loss and an 85% improvement in network longevity, offering a breakthrough in the resilience and sustainability of WSNs in mission-critical and harsh environments.
Intrinsic images aim is separating an image into its reflectance and illumination components to facilitate further analysis or manipulation. This paper presents the system that is able to estimate shading and reflectance intrinsic images from a single real image, given the direction of the dominant illumination of the scene. Although some properties of real-world scenes are not modeled directly, such as occlusion edges, the system produces satisfying image decompositions. The basic strategy of our system is to gather local evidence from color and intensity patterns in the image. This evidence is then propagated to other areas of the image. The most computationally intense steps for recovering the shading and reflectance images are computing the local evidence, and running the Generalized Belief Propagation algorithm. One of the primary limitations of this work was the use of synthetic training data. This limited both the performance of the system and the range of algorithm pseudo inverse process is available for designing the classifiers. Then introduce an optimization method to estimate sun visibility over the point cloud. This algorithm compensates for the lack of accurate geometry and allows the extraction of precise shadows in the final image. Finally propagate the information computed over the sparse point cloud to every pixel in the photograph using image-guided propagation. Our propagation not only separates reflectance from illumination, but also decomposes the illumination into a sun, sky, and indirect layer they expect that performance would be improved by training from a set of intrinsic images gathered from real data.
Nowadays, signal processing in the highly encrypted domain has attracted considerable research in interest.Practical cancelable biometrics (CB) schemes must satisfy the requirements of non-invertibility, revocability, and non-linkability without deteriorating the matching accuracy of underlying biometric recognition system.To bridge gap between theory and practice, it is so important to verify that new CB schemes can achieve a balance between conflicting goals of security and matching accuracy.This project investigates security and accuracy trade-off of the newly proposed local rankingbased cancelable biometrics (LRCB) scheme to protect iris-codes.Biometric technologies are being increased and used in the wide variety of applications like border control, authentication systems and health-care applications due to their efficiency, usability, and reliability.As an effective and popular means to protect privacy of image data, encryption thus converts ordinary signal into unintelligible data, so that traditional signal processing usually happens before encryption or after decryption.This project develops secured information transmission using biomatric system.Here, the content owner encrypts original uncompressed image using an encryption key.Then, data-hider updates least significant bits of encrypted image using the data-hiding key for creating a sparse space to accommodate some additional data.So Iris image of person cannot duplicated for other.With an encrypted image containing additional data, if a receiver has datahiding key, he extracts additional data though he don't know the image content.If the receiver has encryption key, he can decrypt received data to obtain an image matches to the original one, but cannot extract additional data.If the receiver has both data-hiding key as well as encryption key, he can extract additional data and recover original content without any error by exploiting spatial correlation in the natural image when amount of additional data is more than 50 words.
The use of Wireless Sensor Networks (WSNs) is growing as a flexible and affordable solution for many uses, but one of the biggest problems in WSNs is energy efficiency. An energy-efficient cluster-based routing algorithm reduces the transmission distance between sensor nodes and the base station (BS) by grouping nodes into clusters and avoiding nodes with lower energy. This work implemented an energy-efficient Ultra-Scalable Ensemble Clustering technique for large data handling. Because of its low computing complexity and great stability, the Flamingo Search Algorithm is utilized for the selection of cluster heads. In complex network conditions, the Q-learning method is utilized to find the shortest path among BS and CHs. This method generates reward points based on an objective function that requires along the distance among the BS and CHs, the energy usage and area coverage. The implemented method outperformed better performance than existing methods in terms of attained less delay of 2s, high packet delivery ratio of 95%, less energy consumption of 0.09J, and high throughput of 97% for 100 nodes.
“Advanced Machine Learning Techniques” is a comprehensive and cutting-edge book that provides an in-depth exploration of the latest machine learning methods and algorithms. The book covers a wide range of topics, including deep learning, natural language processing, computer vision, and recommender systems. It also delves into specialized areas such as transfer learning, attention mechanisms, and generative models.
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