Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning Model for 3D LiDAR Point Cloud Codec Based on Tensor

In recent years, deep learning techniques, especially autoencoders, have shown remarkable ability in learning compact and meaningful representations from raw point cloud data. In this paper, new approach is proposed to enhance the performance of autoencoders for 3D point cloud data by integrating tensor-based methods. By exploiting the inherent multidimensional nature of point cloud data, the proposed method uses tensor to capture complex spatial relationships and feature interactions. A special architecture is designed to effectively use tensor-based operations and allow networks to learn hierarchical representations of point cloud data. The approach includes two techniques: tensor compression and deep feature learning. The tensor compression phase uses state-of-the-art tensor decomposition algorithm to exploit the inherent redundancies and patterns within the 3D point cloud data. By converting the raw point cloud data into a compressed tensor format, it significantly reduces 39.94% which is 5:3 compressed form of 3D point cloud into its dimensionality while preserving important spatial relationships between points. This compression not only speeds up subsequent processing steps, but also makes the data easier to store and transfer. Compressed tensor representations serve as inputs to deep learning models specifically designed to extract salient features and meaningful representations from compressed data. It uses a multimodal neural network architecture with convolutional and iterative layers to capture both local and global patterns in point cloud data. The findings of the experiment show the superiority of the methodology in terms of both reconstruction accuracy and feature extraction compared with existing techniques. A proposed deep learning model based on Tensor (DLBT) is a Lossless compression method provides a solution for efficient data storage, transmission, and processing in 3D LiDAR applications.
A Machine Learning Approach to Compare Causal Inference Modelling Strategies in the Digital Advertising Industry

Cause and effect relationships are not extensively explored in machine learning but prior to creating a system that understands causal relationships, there needs to be a general ability to grasp and inculcate cause-and-effect statistically. The way of choice for the proposed model is causal inference where the causes are inferred from the data. The study of causality, unlike correlation, falls in the domain of prescriptive analytics, that is, the likelihood of an occurrence given that another occurrence has happened. As a result, the study of causality and predictions based on causal inference are highly important when it comes to the domain of marketing and sales, largely because many practices in the domain of advertising try to fundamentally mimic causal inference such as A/B testing, primarily used in e-commerce and the digital advertising industry. For the purposes of the study, the treatment will be exposure to multiple advertisements and the uplift modelling will be used to direct marketing efforts to the most agreeable customers. Uplift modelling is a causal learning approach that estimates the dataset's individual treatment effect and using the same, calculates the incremental impact of a direct treatment on the individual behavior of the customer, which is why the advertising use-case felt particularly suitable. The usual way causal inference is inculcated in machine learning models is that one attribute is taken as a treatment, that is, the intervention or policy that has been done, while the target label is the desired outcome while accounting for the confounders which are the rest of the attributes. However, for the crux of this study, the proposed model will be taking two treatment attributes so as to see how the causality of two outcomes improves or worsens the impact of a rudimentary machine learning model. At the same time, this comparative study will be in tandem with two other models featuring one causal relationship as well as no causal relationship to truly understand the use of causality in this particular use-case. Furthermore, the modelling strategies will also include basic machine learning algorithms along with Base Meta Learners that are standard use in causal machine learning and compare results of those as well.
Dynamic Modeling and Strategic Prioritization of Sustainable Development Goals

This paper introduces a novel dynamic modeling framework that integrates nonlinear programming and state transition graph algorithms to analyze the temporal progress and prioritization of Sustainable Development Goals (SDGs). The framework takes into account the complex interdependencies among SDGs to minimize the overall completion time while max-imizing policy balance. The state transition graph algorithm enhances the widely used Analytic Hierarchy Process (AHP) model by simulating network dynamics and optimizing the activation or introduction of goals. Empirical findings demonstrate a notable shift in prioritization, with SDG3 (Good Health and Well-being) emerging as the foremost priority by the year 2027. Additionally, the study proposes the incorporation of biodiversity conservation as an objective in the year 2031 to further bolster the achievement of SDGs. By providing valuable insights, this research offers a robust decision-making framework that facilitates informed policy formulation for policymakers and stakeholders engaged in sustainable development initiatives. Moreover, it contributes to a deeper understanding of the progress of SDGs, enabling more effective interventions and fostering sustainable development.
Semantic-Guided Voting with Segmentation for Visual Localization

Scene coordinate regression has significantly enhanced visual localization by segmenting landmark patches and aggregating votes for landmark points within these patches. However, this approach neglects dynamic semantic objects and patch-level semantic inconsistencies, leading to inaccuracies in the voting process. In this paper, we present a novel visual localization framework guided by semantics. Our framework introduces two critical modules: the Pruning Semantically Dynamic Object module, which leverages semantic segmentation to reduce interference from dynamic semantic objects, and the Eliminating Semantic Label Inconsistencies module, aimed at mitigating patch-level semantic inconsistencies. Comprehensive tests on 7-Scenes and Cambridge datasets exhibit the superior performance of our approach. Across both datasets, our method outperforms the scene coordinate regression technique, validating the effectiveness of our proposed framework.
Comparative Analysis of Deep Learning, SVM, Random Forest, and XGBoost for Email Spam Detection: A Socio- Network Analysis Approach

The surge in email usage has led to an upsurge in spam emails, posing threats to both security and user experience. In this era marked by ever-evolving cyber threats, the task of effectively detecting email spam remains critical. This research presents a comprehensive comparative analysis offour well-known machine learning techniques: Deep Learning, Support Vector Machine (SVM), Random Forest, and XGBoost, specifically aimed at detecting email spam. The approach goes beyond analyzing email content alone; it also considers the social network characteristics of emails to enhance overall detection accuracy. This involves examining factors such as sender-receiver relationships, temporal patterns, and the community structure within email networks. To ensure the robustness and real-world relevance of our analysis, the research employs a diverse and extensive dataset containing both legitimate and spam emails for training and evaluation. By bridging the gap between cutting-edge machine learning and email security, this study contributes to the ongoing efforts to combat the growing menace of email spam.
On Real-Time Object Recognition by Single Image Dehazing Method Using Deep Learning Approach

In the past decade, various haze removal techniques have been widely reported for object recognition. But hitherto little has been identified on the use of single image dehazing using transfer learning approach for object detection. Single image dehazing is an emerging computer vision technology which offers some of the extreme benefits over the existing techniques such as consumes less processing time, requires less space for real time dehaze purpose, etc. In this study, we combine both the object detection and image dehazing methods for real time applications such as-remote sensing, video surveillances, driverless automatic vehicles, etc. This paper presents an effective and efficient image dehazing method using transfer learning which helps to recognize objects in real time with more clarity and that can automatically detect objects with a high recognition rate and lesser probability of error. Our tests show that object detection becomes less accurate as the haze intensity increases; yet, under all haze circumstances (low, medium, or heavy), our jointly trained model AOD-net +YOLO v3 consistently outperforms non-joint and naïve YOLO v3 techniques.
Classification of lung cancer from histopathology Images using a Deep Ensemble Classifier

Lung cancer continues to be the leading disease of patient death and disability all over the world. Many metabolic abnormalities and genetic illnesses, including cancer, can be fatal. Histological diagnosis one of the important part to determine form of malignancy. Thus, one of the most significant research challenges is explore the classification of lung cancer based on histopathology images. The proposed method encompasses the ensemble learning for classification of lung cancer and its subtype which employing pre-train deep learning models (EfficientNetB3, InceptionNetV2, ResNet50, and VGG16). The ensemble model has been created utilizing VotingClassifier in soft voting mode. The ensemble model is fit using the extracted features (features_train) and training labels (y_train). The LC25000 database's images of lung tissues are utilized to train and evaluate the ensemble classifiers. Our proposed method has an average F_I score of 99.33%, recall of 99.33%, precision of 99.33%, and accuracy of 99.00% for lung cancer detection. The findings of the analysis demonstrate that our proposed approach performs noticeably better compared to existing models. This technology is more suited to handle a wide range of classification challenges than using a single classifier alone and could improve the accuracy of predictions.
Monte Carlo and Optimization Hybrid Methodology for Improving Workspace Modeling of Planar Manipulators

Manipulator workspaces serving as an important measure for the synthesis of robot manipulators have attracted a lot of research work in recent years. There have been many research works focusing on the shape and size modeling of manipulator workspaces. In them, numerical approaches based on Monte Carlo methods are prominent ones because of their simplicity on calculation. In this paper, taking a common three revolute-joint (3R) planar robot manipulator as an example, a comprehensive methodology combining a Monte Carlo and an optimization algorithm is presented to further improve the modeling accuracy of the workspace. After reviewing the previous procedure based on the Beta-distribution Monte Carlo method, the principles and steps of the novel methodology are presented in detail. Furthermore, two indexes consisting of percent error of the workspace area and the runtime of the algorithm program are applied to illustrate the modeling improvements and the computing efficiency respectively, which show distinct advantages in contrast with the previous one.
A Design of 2-D Wide-Angle Scanning GRIN Lens Antenna with Flat Focal Surface

In this paper, a design method of two-dimensional (2-D) wide-angle beam scanning gradient index lens antenna is proposed. The proposed lens is based on partial maxwell fish-eye lens with a plat focal surface. 2-D wide-angle beam steering is realized by moving the feed on the focal plane. And the edge part in the vertical direction is cut off to improve aperture efficiency. A demonstration of the proposed lens at 10 GHz fed by the circular waveguide is provided in this work. The simulated result shows a 2-D beam scanning range covers pitch angle of 0°-51° with a scan loss of 2.3 dB. The peak gain is 21.8 dBi and the maximum aperture efficiency reaches 51.2%. The sidelobe levels is below -12.6dB within the entire scanning range for both E-plane and H-plane.
Deep Learning Classifiers used in Speech Processing: A Literature Review

Speech is the most convenient way for people to express their emotions. It follows that this communication medium should be extended to computer programmes. This fresh research makes use of advancements in all domains of computers and technology, necessitating an update on the present approaches and techniques that enable Speech processing. Numerous strategies have been used in the literature of analysis of speech and classification techniques. Deep Learning approaches have been presented as a substitute to standard Speech processing techniques. Deep Learning approaches are discussed in this paper and covers recent research in which these techniques are used for speech-based identification.
Study and Controlling of Distillation Column Via Various Schemes

This paper compares controllers results to achieve desired output products compositions. Wood and berry mathematical model used to control, which has a methanol-water (CH30H - H20) mixture as a feed. The major problem in this model is interactions between the loops. For eliminate these interactions we are using decoupling and MPC. We have used Simulation to shows the result of controller. The various schemes are conventional decoupling, fuzzy forward decoupling and MPC. The conventional decoupling and fuzzy forward decoupling used PI controller whereas other one is uses MPC.
Online Business through Social Media and Customer Retention Management: A Bibliometric Analysis of Cutting-Edge Studies

Customer retention research and online business through social media have seen remarkable growth over the past 10 years, and academic scholars' interest in these areas has increased. This study is outlining research on the impact of social media on online business in the context of customer retention. The study uses bibliometric approaches to uncover significant omissions from the literature and focuses on two modern fields: online business through social media and customer retention. With the use of keyword exploration, the study retrieved 335 studies in the Scopus database. Here, the analytical methodologies have been used to identify well-known journals, a significant number of authors, nations, and associations, adding to the body of knowledge on online business via social media and customer retention. It has been used to better grasp the conceptual and intellectual foundation to use tools like bibliographic coupling. The guidelines of the literature review study will also serve as a roadmap for future investigations.
Unveiling Patterns and Enhancing Recommendations: A Novel Regression Analysis Approach for Yelp Dataset

This research paper presents a novel application of regression analysis on the vast and intricate Yelp dataset, a task that has not been previously explored. With its repository of over 8 million reviews, and 1 million users from 11 metropolitan areas, the dataset poses significant challenges for data analysis. Our study focuses on developing a powerful recommendation system for restaurants by harnessing the potential of regression algorithms. The uniqueness of our research lies in successfully navigating the complexities of the Yelp dataset and effectively applying regression analysis to extract valuable insights. This approach unlocks new possibilities in the realm of restaurant recommendations, enabling users to make informed dining choices based on reliable predictions. Our findings not only contribute to the field of machine learning but also hold substantial practical implications for the restaurant industry and beyond. As data-driven solutions gain prominence in modern society, our work showcases the transformative impact of regression analysis in uncovering patterns and driving innovation in a data-rich world.
Development and Validation of an Early Warning System for Septic Shock in Intensive Care Unit

In the Department of Critical Care Medicine, septic shock is a typical critical illness, with the characteristics of difficult discovery and high mortality. What’s more, doctors cannot conduct real-time observation and diagnosis for each patient due to high work pressure and heavy work burden. In order to solve this problem, we developed a septic shock prediction model and implemented it in a septic shock early warning system for the intensive care unit. The system has been applied and evaluated in clinical practice. The results of the evaluation show that the specificity of the early warning model at the optimal threshold reaches 0.95, which effectively helps doctors identify septic shock, intervene for the treatment of patients in advance, and reduce the damage caused by septic shock.
An Innovative Approach: Typicality-Based Collaborative Filtering for Recommender Systems

There are two types of collaborative filtering (CF) methods: user-based and item-based. CF is a frequently used technique for recommender systems. Item-based CF concentrates on making suggestions based on strong correlations, whereas user-based CF concentrates on discovering comparable user preferences and doing so. Today, the majority of CF approaches employ corated objects to determine how similar users are. This study suggests a typicality-based method called TyCo that identifies users who are “neighbors” of one another by looking at user group typicality. TyCo represents a user through a user typicality vector, which identifies their preferences for various goods. In order to get around the drawbacks of conventional collaborative filtering techniques, it chooses “neighbors” by comparing users' similarity based on their typicality levels. This is the first piece of work to use typicality to collaborative filtering, and it runs experiments to assess and show off its benefits. The cost of preprocessing steps, which include creating user prototypes and assessing user typicality in user groups, varies based on the clustering approach selected.
Annual comparison of temporal and spatial distribution of fishing ground of Illex argentinusin the waters of exclusive economic zone of Argentina

Short-fined squid,Illex argentinus,is an important economic cephalopod.To better understand the spatial and temporal variation in distribution of this squid is helpful in rational utilization of the resource.The fishing ground in the EEZ of Argentina is an important habitat for I.argentinus. Based on the production data from Chinese squid jigging vessels in the waters of exclusive economic zone of Argentina from 2008 to 2011,the spatial and temporal distribution of fishing ground in four years were analyzed by spatial distance and aggregative analysis.The results showed that the production of 2008 in the waters of exclusive economic zone of Argentina was dramatically higher than that of the other three years,and the production of fishing ground distributed southward to 46°S was also different from other years.The difference of gravity was lowest in February and highest in March every year.Spatial distance was smallest between 2008 and 2010,biggest between 2009 and 2011 by calculating the Euclidean distance.Aggregative analysis showed that 2008 and 2010 belonged to the same group,2009 and 2011 belonged to another group.The monthly analysis presented that February in different years had no significant difference every year and also showed no difference among adjacent months in the same year,the other months demonstrated unconspicuous regular change.It is suggested that we should combine biological characters and ocean environment factors to comprehensively analyze the tremendous annual changes of fishing ground.
Plant functional traits—concepts, applications and future directions

Plant functional traits are plant core attributes closely related to plant colonization, survival, growth and mortality. These core attributes could significantly affect ecosystem functions and reflect the response of vegetation to environmental change. There is growing recognition that many ecological questions at various levels can be addressed effectively with key functional traits. In this paper, we review key research progress over the last 10 years addressing important ecological issues based on functional traits, including global distribution patterns of functional traits and their associations, variation of traits distribution along environmental gradients, functional diversity and its application, mechanisms of community assembly and dynamics regarding functional traits, the relationship between traits and phylogeny, the effect and response of functional traits to ecosystem functions and disturbance. In addition, we also provide a future outlook, including research directions, methods and applications based on functional traits in plant science. Specially, we propose that functional traits can also be used as indicators or reference for biodiversity conservation and management of ecosystems under the current global climate change. China has a huge territory, which brings great natural recourses of geography and plants, providing a huge natural database for research on functional traits.
Determination of PAH and BTEX in Water Samples of Adriatic Sea using GC/FID

Albania is a coastal country which has a 440 km long coastline. The Port of Durres is the main and the largest Port of the country and the Albania’s Corridor VIII gateway, a European Network Corridor extremely important for manufacturing goods, cargo movement and passenger traffic between Albania and other countries. Albania currently has six commercial ports, the most important of which is the Port of Durres. Despite recent investments made to improve the rehabilitation of the port’s infrastructure, more investments are needed in order to achieve European standards.