bluuebunny/crossref_metadata_2025 huggingface dataset:
Utilizing BiLSTM For Fine-Grained Aspect-Based Travel Recommendations Using Travel Reviews In Low Resourced Language
Authors: Mohamed Basheer K. P., Rizwana Kallooravi Thandil Muneer V. K.,
Published: April 2024

Recommender systems have become an essential tool for enhancing user experiences by providing personalized recommendations. In this study, we present a novel approach to constructing a recommender system specifically tailored for Malayalam travel reviews. Our objective was to extract relevant features from these reviews and employ a bidirectional Long Short-Term Memory (BiLSTM) architecture to construct a robust and accurate recommendation model. We focused on four key features extracted from the travel reviews: travel mode, travel type, location climate, and location type. The travel mode feature encompassed the mode of transport opted for the travel such as bus, car, train, etc., while the travel type captured the nature of the trip, including family, friends, or solo travel. Additionally, we considered the climate of the location, including rainy, snowy, hot, and dry, among others, and the location type, such as beach, hilly, or forest destinations. To construct our recommender system, we implemented a BiLSTM architecture, a powerful deep-learning model known for effectively capturing temporal dependencies in sequential data. This architecture allowed us to process the extracted features and learn the underlying patterns within the Malayalam travel reviews. Our experiments were conducted on a comprehensive dataset of Malayalam travel reviews, carefully curated for this study. The dataset encompassed a diverse range of travel experiences, enabling our model to learn from a wide variety of user preferences and recommendations. The performance evaluation of our recommender system yielded promising results. With an accuracy of 83.65 percent, our model showcased its ability to accurately predict and recommend travel options based on the extracted features from the reviews. The high accuracy achieved by our model underscores the effectiveness of the BiLSTM architecture in capturing the nuances of the Malayalam language and understanding the subtle preferences expressed in travel reviews. The practical implications of our work are significant, as it offers a valuable tool for travelers seeking personalized recommendations based on their travel preferences. The use of the Malayalam language in this context expands the reach of recommender systems to a wider audience, catering specifically to individuals who prefer to consume content and make decisions in their native language.
“Hybrid MAC Methodology for Improving the QOS in Fiber Wireless Network”
Authors: Hardeep Singh Saini Prabhjot Kaur,
Published: April 2024

The Fi-Wi (Fiber-Wireless) approach stands out as a crucial element in the realm of networks, demonstrating superiority over various technologies. With the exponential growth in Internet users, significant strides have been made in the evolution of Fi-Wi networking systems in recent years. This mechanism offers broader bandwidth and network stability, ensuring high-speed connectivity with "Anytime Anywhere" availability for end users. However, the escalating energy demand in networking systems poses a constraint on the network's lifespan, impacting transmission.

Over the years, researchers have proposed and tested various Media Access Control (MAC) protocols to address transmission and energy consumption issues. Despite these efforts, existing protocols have encountered challenges such as overheating, delays, throughput issues, and collisions. This research paper introduces a combination of Time Division Multiple Access (TDMA) and Carrier Sense Multiple Access (CSMA) to tackle associated challenges. The primary objective is to enhance throughput and reduce delays in Fi-Wi networks.

To achieve this goal, the study employs techniques that involve and Utilizing an Orthogonal Frequency Division Multiplexing (OFDM) modulator, a free-space optical (FSO) communication channel, an OFDM demodulator, and Opti-system for the analysis and enhancement of received signals, our study demonstrates that the proposed MAC protocol surpasses conventional MAC protocols in terms of delay, data throughput, and transmission efficiency.
Predictive Modeling of Infection from Chest X-ray Images with ResNet34 and Deep learning CNN approach
Authors: Manish Sharma Yogendra Narayan Prajapati,
Published: April 2024

The timely and precise detection of infected persons is still a major issue in the ongoing fight against the COVID-19 epidemic. With its ability to provide information on the severity and development of the illness, chest X-ray imaging has become a useful diagnostic and surveillance technique for COVID-19 pneumonia. This paper suggests a unique method for predictive modeling of COVID-19 infection from chest X-ray pictures by using the capabilities of deep learning, namely ResetNet architecture. In image classification tasks, ResNet34, a convolutional neural network (CNN) variation, has shown impressive performance, particularly in situations with little training data. Here, we report a comprehensive training and validation protocol for the ResetNet model using a large dataset of chest X-ray images that includes both positive and negative COVID-19 instances. To improve feature extraction, the dataset is preprocessed. All data size: 5856,Train size: 4684, Val size: 585, Test size: 587. To reduce overfitting and boost generalization, data augmentation methods are used. Our suggested method uses chest X-ray pictures to reliably distinguish COVID-19 patients from non-COVID-19 cases by using deep learning's discriminative capabilities. The result of model with TRAINING: Epoch: 19, Loss: 0.08099747663349896, Accuracy: 97.11and TEST: Epoch: 19, Loss: 0.08553645759820938, Accuracy: 96.76.
Advanced Privacy-Preserving Framework for Enhancing Fog Computing to Secure IoT Data Stream
Authors: Prabhat Kumar Aditya Kaushal Ranjan,
Published: April 2024

The proposed privacy-preserving framework based on fog computing for securing IoT data was examined through 10 experiment trials, with each trial dissecting a number of performance related metrics. Specifically, across the trials, latency values varied between 45 and 55 milliseconds, which signified that communication overhead was minute and that data were processed efficiently. Throughput values also varied considerably, yet only between 95 and 110 megabits per second, which signalled that the framework would allow processing data at high speeds. The rates of resource utilization measured in terms of MHR from CPU and Mused in the memory of specific fog nodes, varied between 58% and 76% . Regarding the scalability of the proposed framework, it was assessed based on the data collected and divided into the corresponding categories. From the energy consumption analysis, the values varied between 470 and 530 joules , which was recognized as the change caused by the shifting performance of the proposed IoT solution. Finally, communication overhead values varied from 970 to 1050 bytes, showing the differences in the effects which privacy-preserving frameworks have on data transmission. In conclusion, the results indicate that the proposed complex is immensely efficient in terms of protecting sensitive IoT data, ensuring a high level of security, preserving privacy, maintaining the current performance, and being adjusted to the new threats and security challenges.
Application of Wireless Network Remote Sensing Image and Video Processing Technology in Smart City Design and planning
Authors: Liya Liu
Published: April 2024

With the rapid development of society, regional urban construction has become the main guarantee of economic development, and smart city construction has become the main trend of regional development. Smart cities consist of information images and urban development, which can meet the needs of the modern generation for material life, knowledge and scientific life. Therefore, wireless remote sensing (RS) and video image processing have become key tools for the development of smart cities, which can solve the problems encountered by many professionals before, and become an integral part of urban construction. On this basis, this paper summarized the video processing image and wireless network RS image, and analyzed their development and application in urban design and layout. Based on this, this paper expounded the application of wireless network RS image in smart city design and layout, and analyzed the application prospects of RS technology in building smart city space-time information framework, RS+urban management, RS+smart environmental protection, RS+smart city agriculture and video processing technology in smart city design and planning. It analyzed the application in intelligent security, intelligent transportation and management visualization, and then used the two-dimensional discrete Fourier transform (2D-DFT) definition to strengthen the design and layout of smart cities. According to experiments and surveys, introducing 2D-DFT into the design and layout of smart cities can build a more intelligent city and improve residents’ satisfaction by 30%.
Hybrid Artificial Bee Colony Algorithm with Variable Neighborhood Search for Capacitated Vehicle Routing Problem
Authors: Xinyu Zhang, Kaiwen Geng, Yang Li
Published: April 2024

Aiming at the capacitated vehicle routing problem, a hybrid integer programming model with goal of lowest path cost is constructed, and a hybrid artificial bee colony algorithm with variable neighborhood search based on the model and the characteristics of the CVRP problem is proposed to solve the problem. The hybrid algorithm integrates the artificial bee colony algorithm and the variable neighborhood search algorithm, embeds a multi-variable neighborhood operator in the local search link of the artificial bee colony to carry out iteration. And the operator contains targeted transformation operations on path nodes, strings, and sub paths to ensure the diversity of the bee population. In addition, a variable neighborhood perturbation strategy is used to strengthen the algorithm's ability to escape from local optima. The comparative analysis of the literature study set and its algorithm solution shows that the designed hybrid variable neighborhood artificial bee colony algorithm has strong global search ability and high solution accuracy, especially in solution stability. HABC-VNS can obtain 47 optimal solutions in 74 examples. The average minimum deviation of the optimal solution is 0.34%, and the average deviation of the average CVRP set is 0.57%. The overall performance is better than the algorithm in the comparative literature.
Media Buzzword Analysis Integrated with Phrase Vectors and Topic Model
Authors: Dengyun Zhu, Hailong Gai, Hongzhi Yu, Rong Jing, Fucheng Wan
Published: April 2024

Buzzword analysis is one of the important research contents of natural language processing, and the research results can provide technical support for public opinion analysis. The purpose of extracting media buzzwords is to analyze the rules and changes of language change within a range. The traditional word feature-based buzzword extraction had some problems, such as low accuracy and low coverage, and this paper proposes a media buzzword analysis based on the combination of phrase vector and topic model, the core idea is to integrate the semantic similarity features, and use visualization technology to more intuitively show the overall language change rules. Visualization analyses uses a large number of corpus statistics, calculate the distance between words, and then convert into similarity, through word similarity calculation to show the distribution relationship between different words, and finally quantitative perspective to analyze. Our model is better than the traditional system, and the research results can provide corpus and model support for subsequent research directions.
Computer Bibliometric Analysis of Hakka Dialect Phonetics Based on Citespace and VOSviewer
Authors: Fei Li
Published: April 2024

The findings of research on Hakka dialect phonetics has mainly been published in the form of journal papers, dissertations and monographs. With "China National Knowledge Infrastructure (CNKI)" and "Duxiu Academic Search" as data sources, in this work, relevant research reports on the phonetics of Hakka dialect in the 30-year period from 1993 to 2023 were collected, which included 186 journal papers, 79 dissertations and 36 academic monograph. Firstly, bibliometric analyses were used by Citespace and VOSviewer on computer; then, EXCEL was employed for collecting and sorting out the general situation of the thesis and evaluating the combination of qualitative and quantitative methods. The goal was to investigate the general research on Hakka dialect phonetics in the past 30 years as comprehensively as possible, reveal the development trend of Hakka dialect phonetics objectively and scientifically to provide theoretical basis for the development of new research directions in the future, and provide data support for the development and utilization of Hakka dialect language conservation project resources
Research on Simulation Robots Based on the Needs of Peaceful Community Construction
Authors: Wanghu Sun, Yuning Sun, Chao Wu, Jian Chen, Haibin Zhang
Published: April 2024

The visual simulation level of the simulation robot is not high, and its sense of social presence is also insufficient at present. It is difficult to meet the social needs of closed community management, safe community construction and other aspects in public health events such as the COVID-19 epidemic. For this purpose, this project adopts research methods such as literature, simulation, and empirical research to develop a simulation robot, which is responsible for community security, publicity, consultation, communication, receiving and receiving, and charging. Empirical research has shown that the size of the robot is consistent with that of a real person, with a highly humanoid appearance. The movements of its upper limbs and head basically conform to human behavioral norms, and it can undertake security and other tasks in community services, providing people with a better immersive experience and social presence.
“Comprehensive Integration” Method Based on the Bayesian Network and the Big Data Using in the Construction of New Energy System
Authors: Yunfei Chu, Daye Wang Zuowei Chi, Bicheng Tan,
Published: April 2024

With the continuous application of the big data technology, “Comprehensive Integration” theory is an approach to management based on Rooted theory, which has been successfully applied to the management of large enterprises and research organizations of all kinds. State Grid Corporation of China in the management process of the construction of a new energy system, with a strong innovative, large-scale investment, many participating units, urgent needs and other characteristics of its management capabilities put forward higher requirements. In this paper, a system integration principle based on the Bayesian probabilistic approach and the big data is proposed to realize the engineering application of the theory of “Comprehensive Integration”. By splitting the management system of the new energy system construction, the unit composition of the system can be interpreted to form a complete system and node interaction Bayesian network structure. The algorithm proposed in this paper is implemented in Netica, a commercial statistical analysis software, and its effectiveness is verified, which provides an important theoretical basis for the engineering practice of the theory of “Comprehensive Integration”. Finally, the effectiveness of the Dynamic probabilistic network method was quantitatively verified in a lithium battery pack energy storage management example.
Recognition of Energy Harvesting Techniques for Enhanced Operation in WSN Using IoT with Deep Learning
Authors: D. Hemanand, P. Shobha Rani, Rajalakshmi S D. Venkatesh,
Published: April 2024

The recognition of energy harvesting techniques for enhanced operation in Wireless Sensor Networks (WSN) using IoT and deep learning entails a novel approach to improving the energy efficiency and operational durability of sensor nodes deployed in various environments. Energy harvesting refers to the process of collecting energy from diverse sources such as solar power, thermal energy, wind energy, and mechanical vibrations, and converting it into electrical energy to power electronic devices, including sensor nodes in a WSN. The integration of the Internet of Things with WSNs allows for interconnectivity, enabling enhanced communication, data exchange, and remote management capabilities. This integration facilitates the deployment of more intelligent and adaptive sensor networks, capable of making decisions based on collected information. Deep learning, a subset of machine learning, plays a vital role in enhancing the operation of WSNs by providing advanced data analysis and predictive modeling capabilities. Through the utilization of deep learning algorithms, the proposed system can forecast energy consumption patterns, identify optimal periods for energy harvesting, and optimize the energy efficiency of sensor nodes. This includes adjusting operational parameters such as data communication rates or sleep cycles based on the predicted availability of harvested energy. Deep learning also aids in identifying and prioritizing the most viable energy sources in the deployment environment of the WSN. By employing appropriate energy harvesting techniques based on environmental conditions, such as solar energy harvesting in summer or mechanical vibration harvesting in industrial settings, the proposed method ensures more efficient operation and sustainability of sensor networks across various applications.
A Data Mining Method for Constitutive Model of Hard Brittle Coal
Authors: Qiang Zhang Cong Wang,
Published: April 2024

Data mining is an effective way to solve various types of complex nonlinear problems. And the anisotropy of the mechanical characteristics of coal is a typical nonlinear problem. Hard coal seam are usually characterized by brittleness, which makes them difficult to be crushed efficiently. It is important to improve the efficiency of hard coal crushing by the brittleness characterization method of hard coal is clarified. So the constitutive model and damage mechanism of hard brittle coal was studied in this paper. Damage model of hard brittle coal based on Weibull distribution was established. The damage transition point concept was proposed based on the deformation stage characteristics of hard brittle coal. And effective damage intervals for hard brittle coals can be determined based on the damage transition point. Uniaxial compression experiments of hard brittle coal were simulated using EDEM. The characterization of the effect of discrete elemental eigenstructural parameters on the brittleness of coal was analyzed. And quantitative relationships between shear modulus and brittleness, brittleness and damage transition point were fitted by Matlab. A discrete elemental constitutive model for hard brittle coals was obtained. Finally, the correctness of discrete elemental constitutive model was verified by comparing uniaxial compression experiments with other specimens. The results of this paper were expected to provide a theoretical basis for the crushing technology of hard brittle coal. And the numerical theoretical basis for data mining of the intrinsic model of hard and brittle coal was provided.
Research on Life Cycle Quality Evaluation of Urban and Rural Distribution Intelligent Logistics Park Development
Authors: Yangyang Liu Jie Chang, Zhihao Zhang,
Published: April 2024

In urban and rural distribution, intelligent logistics parks are crucial, and the life cycle is the key to the long-term development of intelligent logistics parks. This paper combines the actual situation of urban and rural distribution intelligent logistics parks, through literature analysis, questionnaire research, etc., using the AHP and gray correlation method to systematically construct a life cycle development quality evaluation system and evaluation scheme for urban and rural distribution intelligent logistics parks that is operable, quantifiable and comparable, and innovatively starts from the digitization, intelligence and greening of urban and rural distribution intelligent logistics parks, and divides the evaluation system is divided into five major aspects (infrastructure and planning, operation management and service, intelligentization level, environmental impact and sustainable development, economic and social benefits). After analyzing the advantages and shortcomings of various single evaluation methods, through the combination and improvement of hierarchical analysis method and gray correlation method, it provides certain theoretical basis and methods with practicality and universality for the evaluation of the development quality of urban and rural distribution intelligent logistics parks, so that urban and rural distribution intelligent logistics parks can carry out the evaluation of the development life in an operable, quantitative and comparative way through this evaluation method, to understand their own This will enable the urban and rural distribution intelligent logistics parks to carry out development life evaluation in an operable, quantifiable and comparable way through this evaluation method, so that they can understand their own level and find out the deficiencies, so that they can clearly define the direction of improvement of the quality of logistics and improve their own level of development
The Ramification of Broadband Infrastructure Construction on Urban Development Efficiency Based on Computer Simulation
Authors: Wang Jianzhi
Published: April 2024

Computer-simulated broadband infrastructure construction is crucial for enhancing urban development efficiency. In this study, we examine the effects of the Broadband China pilot policy as a quasi-natural experiment on urban development efficiency. Employing a multi-period difference-in-difference (DID) model, we analyze panel data from 284 cities in China from 2009 to 2019 for insights on the impact of computer-simulated broadband infrastructure construction on urban development efficiency. Results indicate that post-Broadband China policy implementation, representing computer-simulated broadband infrastructure construction, the urban development efficiency of the treatment group is approximately 6.77% higher than the control group. Computer-simulated broadband infrastructure construction enhances urban development efficiency by improving factor resource allocation, expanding technology spillovers, and upgrading industrial structure. Temporally, computer-simulated broadband infrastructure construction significantly drives efficiency for up to 4 years, while spatially, it positively impacts surrounding urban development within 50-300 km. Continued Broadband China policy implementation is recommended for fostering high-quality urban development
Localization and Detection of Sinkhole Attacks in Wireless Sensor Networks Based on Denial of Service (DoS) Attacks
Authors: Manoranjitham R Mona R,
Published: April 2024

A wireless sensor network (WSN) uses uniformly deployed and positioned sensors to regularly send sensed data to a centralized station. Sinkhole attacks, in which a malicious node draws packets from other legitimate sensor devices and drops them, are the main danger to the WSN network layer and continue to be a difficult problem on wireless sensor networks. Security becomes a major issue since these networks have limited assets including less memory, less energy, and less transmission capacity. Additionally, given the dynamic context in which they are deployed, they are vulnerable to a variety of Denial of Service (DoS) assaults, including wormhole, sinkhole, and black hole attacks. The most dangerous routing attack at the network layer is known as a "sinkhole assault," which directs all network traffic onto a fake path by sending false information under the impression that it is the quickest one to the server. We explore sinkhole attacks to prevent them, and this work proposes a method of detection based on the redundancy process. Messages are transmitted across multiple paths to the suspicious nodes. Upon a thorough evaluation of the response, the attacked nodes are ultimately verified. Finally, a simulation is run to evaluate the method's efficacy. Furthermore, the simulation indicates that the strategy might be somewhat successful. Finally, it draws attention to difficulties and offers a prospective viewpoint for identifying sinkhole attacks.
Development of Augmented Reality Platform Using Image Processing with Deep Learning Techniques
Authors: N. Sripriya, M.G.Kavitha Poornima S,
Published: April 2024

The development and structuring of an augmented reality (AR) platform integrating image processing, deep learning, and spatial tracking techniques have yielded promising results. AR technology, which enhances real-world objects with computer-generated information, holds vast potential for education, entertainment, and industry. Image processing enables real-time object detection, recognition, and tracking within the user's environment, while deep learning models enhance pattern recognition accuracy. Spatial tracking techniques ensure seamless integration of virtual content with the physical world, providing immersive AR experiences. Ant colony optimization (ACO) algorithms further enhance the AR platform's functionality by optimizing object placement and interaction. ACO mimics ant behavior to find optimal paths through graphs, effectively optimizing spatial layout and user interaction patterns in dynamic environments. The integration of ACO into the AR platform facilitates efficient pathfinding for augmented objects and enhances the system's responsiveness to user inputs. The successful implementation of the AR platform underscores its potential to revolutionize various industries, including healthcare, education, and marketing. By leveraging AI-driven algorithms and optimization techniques, AR systems can deliver personalized and engaging experiences that blur the boundaries between the digital and physical worlds. These results pave the way for future advancements in AR technology, driving innovation and reshaping human-computer interaction paradigms.
Big Data on Exploring Influencing Factors of the Reading Achievement between China and Finland
Authors: Yan Zhang, Haijian Hu Yi Huang,
Published: April 2024

This paper is based on Big Data analysis in computer science to find the significant influencing factors in reading achievement in China and Finland and differentiate factors between the two countries so as to find the implications and suggestions for the government policy and then reach the education equality and educational core competition. Reading is the main subject in PISA 2018. In 2018, about 60,000 students from 79 participating nations and economies finished the evaluation, accounting for around 32 million 15-year-olds. In all, 12058 samples in China( B-S-J-Z) and 5496 samples in Finland participate in the PISA 2018. The Plausible Value in Cognitive Process Subscale of Reading entails locate information, understand, evaluate, and reflect. Reading achievement is related to student-level, school-family level and country level. We use student-level constructs to measure every construct including GRADE, MISS, POSS, READINT, CLSSIZE, SELF, SES, SEX, READACH and design a model of three-level HLM in Big Data of factors influencing reading achievement. The Big Data statistics shows the PISA outcome has unstable level in PISA in 2012, 2015, and 2018 and the outcome cannot be applied to other places in China because it just includes four cities, B-S-J-Z. Also, big data also reflects the higher teaching level and teaching staffs in China and Finland than OECD Average although we cannot get all the data of regions in two countries.
Application of Computer-Aided Technology in Teaching Spoken English
Authors: Liu Jiahua
Published: April 2024

Computer-aided technology has revolutionized the teaching of spoken English by offering innovative tools and resources that enhance the learning experience. Through interactive software and multimedia platforms, learners can engage in immersive language exercises, receive real-time feedback, and access personalized instruction tailored to their proficiency levels. This paper introduces a groundbreaking approach for integrating computer-aided technology into the teaching of spoken English, utilizing Automated Probabilistic Markov Chain Machine Learning (APMC-ML). Recognizing the significance of effective communication skills in the modern world, particularly in the realm of English language proficiency, this research endeavors to enhance the pedagogical process through innovative technological means. The methodology involves the development and implementation of an interactive computer-aided system that leverages APMC-ML algorithms to facilitate language learning. This system encompasses various modules designed to cater to different aspects of spoken English acquisition, including pronunciation, vocabulary, fluency, and conversational skills. Through a user-friendly interface, learners are provided with personalized learning experiences tailored to their individual proficiency levels and learning preferences. The application of APMC-ML in teaching spoken English represents a paradigm shift in language education, offering a data-driven approach that complements traditional teaching methods. By harnessing the power of machine learning and probabilistic modeling, educators can optimize the learning experience, expedite skill acquisition, and promote greater language proficiency among learners.
Research on Non-Standard Business Optimization of Insurance Companies Under Computer Big Data
Authors: Xinghou Yu, Yongpan Duan, Lan Yang Yonglin Tan,
Published: April 2024

This article mainly describes a lightweight framework based on J2EE (Struts+ Spring+ Hibernate). Then the paper puts forward the design and implementation scheme of the electric power enterprise insurance system based on J2EE. The technical architecture, function model, data model, information security mechanism and test specification of the system are emphatically described. The goal is to use this architecture to increase program reusability and reduce engineering complexity. The NP method is used to screen the customers in the insurance system, so as to improve the effectiveness of customer relationship management. Finally, the feasibility of the proposed algorithm is verified by computer simulation. This method can not only ensure that probability 1 converges to the optimal solution, but also improve the efficiency of customer feature selection.
Optimizing Data Extraction using Preprocessing for Enhanced Efficiency
Authors: Swati Nikam Santosh V. Chobe,
Published: April 2024

With the exponential growth of the internet, an abundance of information has become readily available. Extracting valuable data from the web is crucial for applications such as meta-querying and comparison shopping. However, the heterogeneous nature of web information poses a significant challenge to the extraction process. The web can be classified into the surface or visible web and the deep or invisible web. While conventional search engines can index the surface web, they fall short when it comes to the deep web.

To access the deep web, users must submit queries to web databases, and the results are encapsulated in dynamically generated web pages containing data records. Traditional search engines struggle to index these dynamic pages, necessitating a specialized program for efficient information extraction from the deep web. Web search engines generate result pages based on user queries, making it crucial to automatically extract data from these pages for various applications.

In this context, we propose an innovative data extraction method called Effective Data Extraction using Preprocessing (EDEP). The EDEP approach begins by parsing the input HTML page, constructing a tag tree, and subsequently eliminating irrelevant tags from the tree. Notably, our system efficiently handles scenarios where auxiliary information, such as recommendations or comments, is intermixed between query result records (QRRs), causing them to be non-contiguous. EDEP also effectively manages result pages containing single QRRs.

Through experimental results, it is evident that EDEP outperforms existing data extraction methods, showcasing its efficacy in handling the complexities associated with web data extraction.
Comprehensive Review on Plant Disease Detection and Identification
Authors: Indrabhan S. Borse Manesh P. Patil,
Published: April 2024

Image Agriculture is a major contributor to financial development and is the main source of income in many countries. The primary goal of disease prevention and treatment, notwithstanding the different challenges farmers face, including plant diseases, is to precisely identify and evaluate the disease while the plant is still growing. Object recognition and classification in images has been made possible by rapid advancements in deep learning, or DL, techniques. Recently, DL approaches have been applied to farming and other agricultural applications and have showed potential in a variety of areas. These plants are commonly affected by diseases such as Septoria leaf spots, bacterial spots, late plant blight, and curled yellow leaves. Our work presents a hybrid approach to early illness detection that applies region-based full convolutional networks (RFCN) for early disease identification and region-based convolutional neural networks (RCNN) for diseases classification. We construct a network model based on the EfficientNetB7 model that optimizes at several scales via dilated convolution. The network is constructed layer by layer, top-down, with several optimizations. Real-time crop images taken from crop fields are used in the experiment, and Efficient Net B7 and hybrid convolution models are used. When the suggested ensemble model is implemented in Python, the outcome demonstrates that it has high accuracy and low loss when compared to other current methodologies.
An Improved Knowledge Graph Representation for Ransomware Attacks Using LSTM-Based Named Entity Relation Technique and Twitter Data
Authors: Munshid Khalid Jameel
Published: April 2024

This research aims to examine the challenges associated with ransomware knowledge and develop a knowledge graph of ransomware attacks utilizing Twitter data. Ransomware is a constantly evolving global threat. The three main steps involved in the development of a knowledge graph from informal text are data collection and preprocessing, features extraction, and relation extraction. A ransomware ontology previously proposed has been used in this work for the extraction of the ransomware entities from unstructured data; this is aimed at making it fit the attacks reported on Twitter. The next process is the identification of the existing relationships in the dataset for the knowledge graph construction; the output of the process, which is the developed knowledge graph is evaluated for accuracy using a tracing technique to demonstrate its efficacy.
Predicting Heart Diseases in IoT-Based Electronic Health Records: A Federated Learning Approach
Authors: Pranjal Pandit, Yogesh J. Gaikwad, Amruta Mhatre Sulakshana Malwade, Asha R. Sanap, Yogesh Sudam Gite,
Published: April 2024

Predicting heart diseases is important for finding them early and treating them effectively. We present a shared learning method for predicting heart diseases using IoT-based electronic health records (EHRs) in this work. Federated learning lets many autonomous IoT devices work together to train a model, while protecting the safety and security of the data. Proposed method uses the fact that IoT devices are spread out to train a global model for predicting heart disease without putting private EHR data in one place. With the EHR data, each IoT device learns a model locally and only sends model changes to a central computer. The computer takes all of these changes and improves the world model. This model is then sent back to the IoT devices to be improved even more. This looping process makes sure that the world model keeps getting better while keeping data private. The proposed method tested by using a real-world collection of EHRs from IoT devices in trials. We looked at how well our shared learning method worked compared to more standard centralized learning methods. Our results show that the pooled learning method makes predictions that are as good as or better than the other methods while protecting data privacy. It also looked at how different IoT device properties, like the amount of data they send and receive and their processing power, affect the shared learning process. It is discovered that devices with more processing power and more data add more to the improvement of the global model. This shows how important it is to choose the right devices in shared learning systems. The paper study shows that pooled learning can be used to predict heart diseases in IoT-based EHRs and that it works well. Our method uses the ability of IoT devices to work together to make accurate predictions while protecting data privacy. This makes it suitable for use in real-life healthcare situations.
Leveraging IoT in Auto Component Manufacturing to Monitor Surface Roughness and Tool Temperature
Authors: Georgena Kannukkadan, Dhanashree Bhoite Tushar Bhoite, Rajesh Buktar,
Published: April 2024

Surface finish is an important requirement in Auto Component Manufacturing that defines the quality of work piece obtained from turning operations. Thus to reduce downtime due to rejection of work piece for its surface finish a continuous monitoring of surface roughness based on its cutting parameter and vibration signal is required. Similarly it is also important to monitor the cutting tool temperature to have a longer tool life. With the emerging trend of Internet-of-Things (IoT) it has become very easy to collect the required data, storing it, analysing it and also alerting the required personnel through the mobile application, e-mail and SMS. The stored data and analysis could reveals in depth the process, which leaves a large scope for improvement of the system. The paper discusses the implementation of IoT to collect data of vibration signal and cutting tool temperature of dry turning operation, for cutting parameter like 1) spindle speed 2) feed rate and 3) depth of cut for three different material mild steel (MS), brass and aluminium (Al). A correlation was obtained to predict surface roughness and tool temperature. The system developed could give a real time monitoring of surface roughness on a smart phone that can be used as a tool for optimizing the turning process to give improved quality and increased production volume.
Machine intelligence based early prediction methods for Cardiovascular Diseases (CVDs)
Authors: Sneha Kanawade, Priyadarshani Doke, Deepali Hajare Shivganga Udhan, Bankat Patil, Suvarna Patil,
Published: April 2024

Exploring early indications of heart disease is challenging in today's society. It may lead to death if not detected early. Early identification of cardiac disease in remote, rural, and semi-urban locations in developing nations can be greatly aided by an accurate decision support system (DSS). To assist in heart-related disease detection at an early stage, this system offers a DSS which uses artificial learning techniques using the patient's clinical details. A hybridized class balancing methodology using the chi2 feature selection algorithm, random oversampling, and undersampling techniques were used to find the suitable features from the presented dataset. Standard scalar approaches have also been utilized for data preprocessing. In the last stage of developing the suggested Artificial Intelligence (AI) system, the system used support vector machines (SVM), naive Bayes, random forest classifiers, logistic regression, and improved Artificial Neural Network (ANN) classifiers. Tests were conducted on the Python-based simulation environment to evaluate the proposed system. Evaluation of the system is completed using the Cleveland heart disease dataset by the machine repository at UCI. A 96.74% accuracy rate was reached, which is better than some previously published models for heart-related disease prediction.
PSO-Controlled WSN Environment to Mitigate Flooding and Improve Network Lifetime
Authors: Daljeet Singh Rahul Nawkhare,
Published: April 2024

The Restricted Flooding-based Route Discovery (RFBRD) – Particle Swarm Optimization (PSO) routing scheme introduced for Wireless Sensor Networks (WSNs) in this article not only reduces the energy loss due to unwanted RREQ (Route Request) flooding but also improves the lifetime of the network. Excessive flooding depletes energy and affects the lifetime of the network. Nodes in scarce regions are allowed to forward first and subsequent RREQ packets freely in the network and are relocated using PSO to improve their neighborhood for better neighbor connectivity and coverage. Whereas, nodes in the populated region or dense region are governed by energy ratios and are allowed to forward first RREQ packets only when they satisfy the energy conditions. This scheme is efficient in maintaining a proper balance of QoS (Quality of Service) parameters works well for high-density networks for more than 50 nodes and can restructure network topology obtaining better connectivity. Experimental analysis showed that the performance of AODV is superior in the case of a low-density network (N=40 nodes) while RFBRD-PSO outperforms in all other configurations (60, 80, and 100 nodes). The Packet delivery ratio was increased by 0.08% and the throughput was higher by 11 kbps in the case of RFBRD-PSO. The routing overhead is low by approximately 40% and the average end-to-end delay is found low by 0.04 as compared to the AODV routing. The energy residue in the case of RFBRD-PSO is less than the value of AODV is the cost paid for a higher packet delivery ratio. The neighborhood connectivity is improved by approximately 32%.
Filtered Ofdm System Model for Papr Reduction in the Growth Of 5g
Authors: Manish Patidar, Narendra Singh Manoj Kumar,
Published: April 2024

A proposed waveform for fifth generation (5G) communications is the filtered orthogonal frequency division multiplexing (F-OFDM) system. Filtering-based waveform frameworks distinguish themselves by suppressing out-of-band emission and asynchronous transmission. Meanwhile, the high Peak-To-Average Power Ratio (PAPR) remains a barrier to new waveform possibilities. Partials transmit sequence (PTS) is a successful strategy for reducing high PAPR in multicarrier networks. This work presents a multicarrier in OFDM and UFMC based on the Filter Bank Multicarrier (FBMC) waveform for communication systems. Furthermore, FBMC waveforms have limited orthogonality between subcarriers, which can mitigate Doppler and multipath effects. This FBMC approach uses the cosine law to filter the traffic signal, and the Welch algorithm is offered for segmentation, which lowers noise in estimated power spectra. As a result, a hybrid Selective Mapping (SLM) with Cuckoo Search and Ant Lion Optimization (CS-ALO) algorithms is suggested using the FBMC framework to reduce PAPR in the OFDM system. The proposed work is implemented, and detailed experimental research is carried out to show the increased performance in 5G networks. Based on a fair comparison, the F-OFDM scheme outperforms previous approaches in terms of BER and multicarrier system capacity. Simulation results show that the proposed waveform design and processing method outperform standard OFDM and F-OFDM methods in PAPR reduction and communication integrated systems.
The Role and Challenges of Machine Learning in the Construction of a Community of Human Destiny
Authors: Chuyu Zhang Wenjie Zhang, Zhiqiang Cai,
Published: April 2024

- The advent of machine learning (ML) has significantly transformed various fields, including the realm of social sciences and humanities. This paper explores the pivotal role and inherent challenges of machine learning in fostering the construction of a community of human destiny (CHD), a concept emphasizing shared global interests and collective human development. Machine learning, with its capacity to analyze vast datasets and uncover patterns, holds the potential to address complex issues such as poverty, inequality, and environmental sustainability, which are integral to the community of human destiny vision. However, the deployment of machine learning in this context is fraught with challenges, including ethical considerations, data privacy concerns, and the need for interdisciplinary collaboration. By examining theoretical frameworks, this paper aims to provide insights into how machine learning can be harnessed effectively to advance the goals of community of human destiny while also highlighting the importance of governance frameworks and ethical guidelines in ensuring that machine learning contributes positively to the common good. The paper argues that while machine learning presents significant opportunities for the community of human destiny, a cautious and responsible approach is essential to navigate the associated challenges and to ensure that technological advancements align with humanistic values and the overarching goal of a harmonious and sustainable global community.
Study on Main Controlling Factors and Fluid Identification Methods of Low-resistivity Reservoirs based on BP Nerve Network Verification in X Area of Ordos Basin
Authors: Tao Yi, Yulin Kou, Yushuang Zhu Chenyue Dang, Linjun Yu, Hongbo Zhang,
Published: April 2024

The low-resistivity oil layers refer to a formation whose resistivity response is opposite to the typical oil layers and similar to water layer. Distinguishing it from water layer in logging interpretation is very difficult, which brings great difficulties to oil and gas exploration and development. The exploration and development research shows that there is obvious resistivity anomaly in X area of Ordos Basin, and typical low-resistivity oil layers are developed in formation A in this area, so it is difficult to accurately divide oil layers and water layers only from logging response. In order to identify the fluid properties in low-resistivity oil layer areas, based on conventional logging data of formation A in X area, through petrophysical experimental analysis, combined with formation water analysis data, logging, oil test and other data, the main control factors of low-resistivity oil layers in X area are deeply studied in many aspects, and a more effective fluid identification method is put forward by combining the secondary evaluation of porosity and permeability and the flow unit method. The BP nerve network simulation cross plot is used to verify the identification results. The research shows that the main control factors of low resistivity oil layers in the study area are related to the salinity of water and pore connectivity, and also to the lithologic properties of different formations. Based on the study of the main control factors of low-resistivity oil layers, a multi-factor fluid identification method is proposed. The coincidence rate between the evaluation results of this identification method and oil test is 85.7%, which can identify the oil-water reservoirs in X area more effectively and accurately, finally identify the location of low-resistivity oil layers and boost the growth rate of oil and gas production.