bluuebunny/crossref_metadata_2025 huggingface dataset:
Study on Real-Time Spatial Distance Acquisition Method of Transmission Line Based on Laser Point Cloud and Image Monitoring
Authors: Feiran Wang, Geng Han, Zilong Hou, Xinyang Guo Zhengdong Li,
Published: April 2024

This paper introduces the principle of safety distance diagnosis of transmission lines by using airborne LiDAR detection technology. This project intends to realize the effective segmentation of ground objects and non-ground objects through the point cloud automatic filtering technology considering the surface fluctuation characteristics. Then, this paper realizes the initial recognition of ground objects based on spatial dimension information. Then this paper extracts the ground object information based on the image data with high precision. The 3D nodes of transmission lines are obtained by Hough transform and block centroid algorithm, and the floor features are extracted by area growth algorithm. The plant feature is extracted by using plant vertex and boundary point. The distance between the power line and the surrounding objects can be measured, and the elevation of the wire and the surrounding environment can be judged accordingly. The algorithm is tested by using the observation data of unmanned airborne LiDAR, and the simulation results show that the proposed method is feasible. Through the identification of buildings, plants and other objects along the line, the harm degree of them on the power line is judged and the effective treatment is carried out. This ensures the safe operation of transmission lines.
Application Research of Intelligent Search System Based on Clustering Algorithm
Authors: Lihong Hao, Saixiao Huang, Xiaoya Qin Tao Liu,
Published: April 2024

In this paper, a multi-agent oriented personalized information retrieval method is proposed. The system is composed of 5 agents and 2 other components. The design highlights the personalization and real-time of the information obtained by users, and has the characteristics of intelligent retrieval and active retrieval. Then this paper studies it from three aspects: user model, system learning model and individual information model. Then a grouping gravity search algorithm is proposed. Compared with conventional gravity search, group gravity search is carried out for a specific decoding strategy. This algorithm is a new gravity search algorithm suitable for packet coding, which makes the packet search process approximate to the iterative optimization of classical gravity algorithm. Finally, the effectiveness of the proposed method is verified on various typical experimental cases. Experiments show that the proposed algorithm has higher classification performance than other intelligent group algorithms.
Application of Clustering Algorithm in Fault State Classification of Low Voltage Watt-Hour Meter
Authors: Hao Wang, Chong Li, Yi Wang, Xiaoteng Sun Shengbo Sun,
Published: April 2024

In this paper, a low-voltage digital watt-hour meter monitoring system for smart grid is designed. The system includes information acquisition system, low-voltage digital meter and power service system. The low-voltage digital meter uses RFID chip inside, which can accurately locate the target through high-speed transmission and remote identification. It can effectively overcome the security risks caused by information collision and the problem of slow recognition speed. According to different fault categories, community clustering method is used to classify them. In the actual determination of the fault causes of 32 electric energy meters with this method correctly analyzed the fault causes of 26 electric energy meters. The accuracy rate reached 81.25%.
Enhancing Lip Reading: A Deep Learning Approach with CNN and RNN Integration
Authors: Rutuja Rajendra Patil, Devika A. Verma Seema Babusing Rathod, Rupali A. Mahajan, Poorva Agrawal,
Published: April 2024

This research introduces an innovative approach to enhance lip reading-based text extraction and translation through the integration of a double Convolutional Neural Network (CNN) coupled with Recurrent Neural Network (RNN) architecture. The proposed model aims to leverage the strengths of both CNN and RNN to achieve superior accuracy in lip movement interpretation and subsequent text extraction. The methodology involves training the double CNN+RNN model on extensive datasets containing synchronized lip movements and corresponding linguistic expressions. The initial layers of the model utilize CNNs to effectively capture spatial features from the visual input of lip images. The extracted features are then fed into RNN layers, allowing the model to grasp temporal dependencies and contextual information crucial for accurate lip reading. The trained model showcases its proficiency in extracting textual content from spoken words, demonstrating an advanced capability to decipher nuances in lip gestures. Furthermore, the extracted text undergoes a translation process, enabling the conversion of spoken language into various target languages. This research not only contributes to the advancement of lip reading technologies but also establishes a robust foundation for real-world applications such as accessibility solutions for individuals with hearing impairments, real-time multilingual translation services, and improved communication in challenging acoustic environments. The abstract concludes with a discussion on the potential impact of the double CNN+RNN model in pushing the boundaries of human-computer interaction, emphasizing the synergy between deep learning, lip reading, and translation technologies
IoHT Based Tele-Healthcare Support System for Feasibility and Perfor-mance Analysis
Authors: Asha Ambhaikar Lowlesh Yadav,
Published: April 2024

With the rapid development in Internet of Things (IoT) it come up with the subbranch Internet of Healthcare Things or the Internet of Medical Things. With the help of portable healthcare sensors and the wearable devices now, ambulance can be converted into critical diagnostics unit on the wheels. Monitoring the home care patient is not the just part of IoT application where doctor can remotely monitor the patient situation by the means of sensors. It requires a complete system may called as telemedicine where multiple components will play similar priority role to monitor the patient status using sensors, let the doctor connect the patient, data analytics calculate the progression, AI engine will detect the emergency and act upon. The proposed system is to design and developing centralized tele-healthcare support system and systematic analytics sharing with multiple level of user with privacy protection. This will allow different authorities to take advantages from patient generated records.
Design and Implementation of Machine Learning Algorithms in Automatic Grading of Students' Assignments
Authors: Fang Xu Duo Chen,
Published: April 2024

This study introduces machine learning methods for automatically grading students' assignments, catering to the changing demands of education in the digital age. By utilizing diverse techniques, the aim is to simplify the grading process, give prompt feedback, and improve overall efficiency in assessment practices. The research delves into the different types of assignments such as written responses and coding tasks, and presents a range of ML algorithms including NB, Decision Trees (DT), RF, SVM, Linear Regression (LR), KNN, and Ensemble Methods (EMs). The implementation involves extracting relevant features from assignments, preprocessing data to handle noise and outliers, and training models on a varied set of examples. The grading system prioritizes interpretability, accuracy, and efficiency while aligning with educational objectives and grading policies. Evaluation is done using suitable metrics like recall, accuracy, precision, F1 score for each algorithm. The study contributes to the progress of automated grading systems by providing valuable insights for educators on the potential impact of machine learning in enhancing assessment processes. Additionally, the designed algorithms demonstrate adaptability for different types of assignments and serve as a foundation for future enhancements in grading precision and efficiency.
Hide Digital Images in Encrypted Image Files After Two Stages
Authors: Abdullah Riyad Mubarak
Published: April 2024

This paper introduces a novel encryption technique for concealing digital images within encrypted files through a two-stage process. The methodology incorporates sparse coding and compressive sensing, leveraging a learned dictionary to recover the sparse coding of plain images. Comprising compressive sampling and random projection with a Gaussian measurement matrix, the approach achieves joint compression and encryption. Pseudorandom encryption and chaos-based block scrambling are subsequently applied to produce the final encrypted image. Experimental results using 256 × 256 test photographs from the USC-SIPI dataset demonstrate the superiority of the proposed method in terms of both Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The study also conducts key sensitivity analysis, revealing the method's susceptibility to changes in the secret key, and statistical attack analysis, demonstrating its robustness against attacks exploiting statistical information. In conclusion, the proposed encryption technique not only ensures cryptographic strength but also excels in image reconstruction quality, making it a promising solution for secure image transmission and storage
Influence of Load Models and DG Operational Power Factor on Optimization of Shunt Capacitor Placement in Distribution Grids Considering Voltage Stability
Authors: Van-Y Pham Nang-Van Pham,
Published: April 2024

The paper proposes a mathematical model of mixed-integer nonlinear programming (MINLP) to optimize the location and size of shunt capacitors in power distribution grids with distributed generation (DG), considering voltage stability constraints. At the same time, the paper also analyzes the influence of the load models, such as voltage-dependence load (ZIP load) and constant power load, together with DG’s operating power factor on the optimal solution. The objective function of the proposed optimization formulation is minimizing the total expenses, including the capital investment of capacitors and the expense incurred by network energy loss. This MINLP model includes constraints in the current and critical loading conditions, such as the system of power flow equations, nodal voltage limits, branch thermal limits, capacitor-related constraints, and restrictions of minimum security level. The proposed optimization model was evaluated on a modified IEEE 33-node distribution grid using the KNITRO commercial solver with the GAMS programming language. The calculation results show that the voltage stability constraints, load models, and operational power factor of the DG units have an essential influence on the optimal position and capacity of the shunt capacitors.
Using Simplified Distflow-Based Mixed-Integer Quadratically Constrained Programming Formulation for Optimum Selection of Conductor Size in Electrical Distribution Networks
Authors: Thi-Hoai-Thu Nguyen, Van-Y Pham, Hoai-Nam Nguyen Nang-Van Pham,
Published: April 2024

Determining conductor size is a subproblem that plays a vital role in the planning process of distribution systems. The problem of selecting the conductor cross-sectional area is usually made a model of mixed-integer non-linear programming (MINLP). It is important to note, however, that the MINLP model is not always capable of ensuring convergence to a solution that is globally optimum. In this research, a mixed-integer quadratically constrained programming (MIQCP) formulation is developed as a method for determining the conductor cross-sectional area in power distribution networks in an optimal manner. The goal function aims at minimizing the lifetime cost of lines, including initial capital cost together with operational expenses. The suggested optimization model’s constraints consist of power balance equations, thermal loading capacity of branches, nodal voltage restrictions, budget limitations for investment, and the need to have the same wire size in the main feeder. By using the simplified DistFlow approach for electrical distribution networks and precisely linearizing the product of a binary variable and a continuous variable, the MIQCP formulation is derived from the MINLP model. It is likely possible to solve the MIQCP formulation efficiently in the GAMS environment by using available commercial solvers like CPLEX. The developed MIQCP model is validated using three medium voltage distribution grids of IEEE 33 buses, IEEE 85 nodes, and the Vietnamese real 102 buses. The calculation results revealed the accuracy along with the efficacy of the proposed optimization procedure.
A Generalized Two-Level Ensemble Method for Spam Mail Detection
Authors: C. Nagaraju B. Aruna Kumari,
Published: April 2024

Email is the most cost-effective way to communicate with people across the world.  It offers a simple and convenient way to send and receive messages. However, it is susceptible to various types of threats. The most significant risk to emailers is spam, which refers to unsolicited emails that are often sent in large numbers to multiple recipients. Malicious spam includes links to phishing websites. These links not only pose a threat to our system, but also make our personal information accessible to hackers. Spammers often create fake profiles and email accounts making it easier for them to deceive unsuspecting victims. In this paper, the content of the email is used to detect spam which provides more information than a URL. It has been found that several techniques have been proposed to efficiently identify spam emails but current email spam detection methods have not yet achieved high accuracy. It is necessary to improve their performance in spam detection, a generalized two-level ensemble technique is explored irrespective of the type of spam datasets. The proposed two-level ensemble algorithm compares with various machine learning techniques such as SVM, Regression, and kNN, along with their variants, Finally, a K-fold and voting algorithms are applied to make the final prediction.
AI-Powered Innovations in Electrical Engineering: Enhancing Efficiency, Reliability, and Sustainability
Authors: Chandratreya Abhijit
Published: April 2024

Artificial Intelligence (AI) has emerged as a transformative force in the field of Electrical Engineering, revolutionizing traditional practices and unlocking unprecedented possibilities. This research paper investigates the integration of AI-powered innovations to enhance efficiency, reliability, and sustainability within electrical engineering systems. Through a comprehensive review of existing literature, this study delves into key applications of AI, including predictive maintenance, optimal resource allocation, and fault detection, among others. Utilizing advanced machine learning algorithms and data analytics techniques, AI facilitates real-time decision-making processes, enabling proactive maintenance strategies and optimizing system performance. Moreover, AI-driven approaches contribute to the enhancement of reliability by predicting potential failures and implementing pre-emptive measures, consequently reducing downtime, and improving operational continuity. Furthermore, the implementation of AI in electrical engineering fosters sustainability by optimizing energy consumption, mitigating environmental impacts, and facilitating the integration of renewable energy sources into power grids. By leveraging AI technologies, electrical engineering systems can adapt to dynamic operational conditions, maximize resource utilization, and minimize environmental footprints, thereby paving the way for a more efficient, reliable, and sustainable future. This paper underscores the transformative potential of AI in shaping the landscape of electrical engineering and provides insights into future research directions to harness the benefits of AI-powered innovations further.
A Hybrid IoT-Enabled Transfer Learning Model for Accurate Diagnosis of Coronary Heart Disease
Authors: Yashwant Dongre, Prachi P. Vast, Hrishikesh B. Vanjari Manisha R. Kuveskar, Sheetal K. Pawar, Shraddha V. Pandit,
Published: April 2024

Coronary heart disease (CHD) is the top cause of death in the world, which shows how important it is to get a correct evaluation as soon as possible. The Internet of Things (IoT) opens up new ways to improve healthcare, and transfer learning has shown promise in making machine learning models work better, especially when it comes to medical analysis. IoT-Enabled Transfer Learning Model (HIETLM), which is new, is proposed in this study as a way to accurately diagnose congenital heart disease. Wearable IoT devices are used by the HIETLM to continuously collect vital data from patients, such as their heart rate, blood pressure, and level of exercise. These pieces of information are sent to a central computer to be processed and analyzed. The suggested model has two main parts: a base model that was learned on a big external dataset, and a transfer learning part that was fine-tuned using data from a single patient. The base model has already been trained on a big set of general health data to learn general trends and traits that are useful for diagnosing CHD. This training helps the model understand complicated patterns and connections that are hard to learn from a small collection. The transfer learning part then tweaks the base model using the patient-specific data to make it fit the unique traits of each patient. To test how well the HIETLM worked, we did tests with a group of patients who were thought to have congenital heart disease. From the data, we can see that the HIETLM is more accurate, sensitive, and detailed than other models. The suggested model could help make CHD diagnoses more accurate and faster, which would improve patient results and lower healthcare costs.
Ensemble and Transformer Encoder-based Models for the Cervical Cancer Classification Using Pap-smear Images
Authors: Usman Ali Khan, Sultan Al-Garni Maysoon Alzahrani,
Published: April 2024

Cervical cancer poses a health concern for women globally ranking as the seventh most common disease and the fourth most frequent cancer among women. The classification of cytopathology images is utilized in diagnosing this condition with a focus on automating the process due to potential human errors in manual examinations. This study presents an approach that integrates transfer learning, ensemble learning and a transformer encoder to classify cervical cancer using pap-smear images from the SIPaKMeD dataset. By combining these methods human involvement in the classification task is minimized. Initially individual models based on transfer learning are. Their unique characteristics are combined to create an ensemble model. This ensemble model is then input into the proposed transformer encoder specifically utilizing the Vision Transformer (ViT) model. The results highlight the effectiveness of this methodology. The VGG16 model demonstrates accuracy of 97.04% and an F1 score of 97.06% when applied to classifying five categories using the SIPaKMeD dataset. However surpassing this performance is the learning model, with an accuracy of 97.37%. Notably outperforming all models is the transformer encoder model achieving an accuracy of 97.54%.Through the utilization of transfer learning, ensemble learning and the transformer encoder model this research introduces a method, for automating the classification of cervical cancer. The findings underscore the capability of the suggested approach to enhance the precision and effectiveness of diagnosing cancer.
A novel data Analysis architectural framework for data reduction on edges for Smart City Healthcare
Authors: Bhramaramba Ravi Swapnaja Rajesh Hiray,
Published: April 2024

The most difficult task is managing and digesting the massive amount of heterogeneous data created by IoT. It questioned the efficiency and computational storage capacity of today's infrastructure resources. Furthermore, because there is so much data, it becomes difficult to give the user enough information to make a decision. A technology known as Complex Event Processing (CEP) has been created to glean important insights from massive real-time data streams. The goal of this research is to create a system that can extract complicated events from large amounts of sensor data and only transmit those events to a cloud server. Global healthcare services have been expanding at an exponential rate because of the vast amount of data that clinical and medical organizations generate daily. Healthcare systems can use e-health services to meet the medical and assistive requirements of people. Edge technology is proven in this direction. Mixing the old classic concepts with the innovative we have proposed the Novel data analysis architecture for smart heart disease prediction. We have created the fuzzy-based inference system at the edges for the data analysis and used the resulting Complex Events at the end predictions.
Forecasting the Future: A Comprehensive Review of Time Series Prediction Techniques
Authors: Sandhya Arora Milind Kolambe,
Published: April 2024

Time series forecasting is a critical aspect of data analysis, with applications ranging from finance and economics to weather prediction and industrial processes.  This review paper explores the evolution of time series forecasting techniques, analyzing the progression from classical methods to modern approaches. It synthesizes key advancements, discusses challenges, future directions and provides insights into emerging trends. Traditional forecasting methods often struggle with capturing the complex patterns and dynamics present in real-world time series data. This study explores the efficacy of cutting-edge models, such as long short-term memory (LSTM) networks, and recurrent neural networks (RNNs), in capturing intricate temporal dependencies. It also aims to guide researchers and practitioners in selecting appropriate methods for diverse time series forecasting applications. We categorize existing approaches, discuss their strengths and limitations, and highlight emerging trends in the field.
Anomaly Detection in Log Files Based on Machine Learning Techniques
Authors: Sándor R.  Répás Salam Allawi Hussein,
Published: April 2024

This article provides a comprehensive overview of contemporary techniques for detecting anomalies in log files in light of the growing reliance on computer systems and the volume of log files generated. Log files are crucial for identifying questionable or malicious activities since they shed light on system behavior and performance. The work addresses the challenges associated with identifying anomalies in log files, including their dynamic structure, high volume, and chaotic nature. Several anomaly detection strategies are assessed based on how well they work, how quickly they can be executed, and how well they can be applied to different types of log files. These strategies include statistical techniques, machine learning algorithms, and deep learning techniques. Furthermore, because cyber threats are getting more complex, AI applications are becoming crucial to network and cyber security. By utilizing anomaly detection, predictive analysis, and reactions to adjust to changing attack patterns, artificial intelligence can significantly enhance security.
Ai in Fraud Detection: Evaluating the Efficacy of Artificial Intelligence in Preventing Financial Misconduct
Authors: Pranav Kumar Chaudhary, Aakash Kishore Chotrani Raja Mohan, Mythili Boopathi, Piyush Ranjan, Madhavi Najana,
Published: April 2024

AI is anticipated to enhance competitive advantages for financial organisations by increasing efficiency through cost reduction and productivity improvement, as well as by enhancing the quality of services and goods provided to consumers. AI applications in finance have the potential to create or exacerbate financial and non-financial risks, which could result in consumer and investor protection concerns like biassed, unfair, or discriminatory results, along with challenges related to data management and usage. The AI model's lack of transparency may lead to pro-cyclicality and systemic risk in markets, posing issues for financial supervision and internal governance frameworks that may not be in line with a technology-neutral regulatory approach. The primary objective of this research is to explore the effectiveness of Artificial Intelligence in preventing financial misconduct. This study extensively examines sophisticated methods for combating financial fraud, specifically evaluating the efficacy of Machine Learning and Artificial Intelligence. When examining the assessment metrics, this study utilized various metrics like accuracy, precision, recall, F1 score, and the ROC-AUC. The study found that Deep Learning techniques such as “Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks /Long Short-Term Memory, and Auto encoders” achieved high precision and AUC-ROC scores in detecting financial fraud. Voting classifiers, stacking, random forests, and gradient boosting machines demonstrated durability and precision in the face of adversarial attacks, showcasing the strength of unity.
Development of an Automated Assamese Dialect Translation System Based on Rule-Based Techniques: Conceptualization and Preliminary Implementation
Authors: Kshirod Sarmah, Raju Narzary, Utpal Bhattacharjee Hem Chandra Das,
Published: April 2024

To translate a particular word or collection of words from one dialect to another, a system called the dialect translation module is used in this work. The suggested system's results can help with understanding and analysing the dialectal differences between the dialects. This will also benefit individuals keen on acquiring communication skills in one or more of the dialects. When translating, consider both mainstream Assamese (standard Assamese) and the Kamrupia (Palasbari) dialect of Assamese. This study delves into the analysis of text, word construction, morphology, grammar, and ambiguity. Towards the conclusion, a method for developing a deliberate translation system is suggested.
Deep Learning in Agritech: Exploring Techniques and Architectures for Plant Disease Detection
Authors: Thelma D. Palaoag, Carl Angelo S. Pamplona Nerissa L. Javier,
Published: April 2024

Disease detection in agriculture is crucial for maintaining crop health and productivity. Traditional methods of detection rely on manual observation and expert knowledge, which can be time-consuming and subjective. In recent years, deep learning techniques have emerged as a promising approach for automated disease detection. This paper investigates the performance of three state-of-the-art deep learning models, ResNet34, MobileNetv2, And Yolov8, in detecting and categorizing leaf diseases among corns.  The study revealed that MobileNet2 demonstrates computational effectiveness in classifying corn leaf diseases, with a low average loss and the ability to discriminate between specific illnesses. Yolov8 is identified as a strong candidate for real-world applications due to its consistently high precision (95.73%), recall (94.81%), accuracy (96.18%), and F1 score (95.22%) across all disease categories. It excels in distinguishing between Blight and Common Rust and accurately recognizing healthy leaves. ResNet34 and MobileNetv2 also show competitive results, but Yolov8 performs better overall, particularly in terms of precision and recall. However, the choice of the optimal model depends on the specific application's demands, computational effectiveness, and the balance between recall and precision. Further investigation and refinement endeavors are recommended to augment the potential of these models and address any remaining obstacles in disease identification.
Improving Abiotic Stress Mitigation via Predictive Modeling of Water Quality Parameters in Recirculated Aquaculture Systems
Authors: Thelma D. Palaoag, Marvin D. Mayormente Minerva M. Fiesta,
Published: April 2024

A sustainable aquaculture solution can be provided by Recirculated Aquaculture Systems or RAS, nevertheless, Abiotic stress factors can negatively impact aquatic organisms' growth and well-being. This study’s purpose is to demonstrate how Random Forests machine learning method helps to develop a predicting model that can aid in forecasting and in the mitigation of abiotic stressors in Recirculating Aquaculture Systems by regulating water quality influences.
The study used the historical data on water quality, such as temperature, dissolved oxygen, pH, ammonia, and TDS levels in constructing a Random Forest-based predictive model. Based from the results reveal, the developed prediction model using random forests machine learning method was 90% accurate in making prediction and improved abiotic stress in RAS.
Understanding the complex relations between water quality indicators and abiotic stress variables in RAS is crucial for identifying major abiotic stress drivers and developing effective models for forecasting water quality parameters, which results in real-time insights and actionable information for making proactive decisions and employing adaptive management techniques.
Furthermore, RAS improves aquaculture productivity while reducing environmental impacts, which results in increased productivity, resource utilization, and system performance. This study makes a vital contribution to the aquaculture sector by proposing a data-driven method to improve the control of water quality parameters in RAS and, eventually, raise the sustainability and effectiveness of Recirculating Aquaculture Systems
Ear Biometry: Protection Safeguarding Ear Acknowledgment Framework utilizing Transfer Learning in Industry 4.0
Authors: Shilpi Sharma, Swapnita Srivastava, Vipul Narayan Rahul Sawhney,
Published: April 2024

Human-Recognition using biometric features proposes a noble way of uniquely identifying individuals considering they do not require people to keep loads of passwords in mind to prove their identity. Biometrics have aided in people unable to render other’s identity as well and have advanced over the years. Identification using ear biometric technique is considered to outrun other features since passive human involvement and ease-of-access are its strong set of attributes, not seen in any other biometric techniques. It has managed to identify criminals in the Crime Branch and has various helpful applications in the industry. Even in times of  pandemic, it can serve its purpose in identifying individuals due to its feasibility. Models for Ear Image Identification have been proposed by various researchers over time utilizing Deep Learning and its Networks achieving high accuracy results, presenting faster and accurate identification models, boosting Ear Biometry as a secure Human Identification Tool. The learnings of this study on the AMI Ear Dataset and the OCEar dataset prove Ear Uniqueness of individuals demonstrating the identification of individuals, introducing passive identification into play, as well as computes on another dataset OCEar to study the similarity of both ears for identifying an individual.
NDN Security: Cryptographic Approaches for Safeguarding Content-Centric Networking against Threats
Authors: Gitanjali R. Shinde, Parikshit N. Mahalle, Nilesh P. Sable Riddhi R. Mirajkar,
Published: April 2024

The future of the internet demands innovative approaches to communication paradigms and security mechanisms. Named Data Networking (NDN) introduces a ground breaking data-centric communication paradigm, shifting the focus from host-centric networking to efficient content retrieval. This novel framework relies on the dynamic interplay of Interest and Data packets to enable seamless content dissemination and caching. NDN's security framework, built upon digital keys, certificates, and trust policies, ensures the integrity and confidentiality of data transactions within its ecosystem. Cryptographic keys exchanged in real-time through Interest and Data packets, forming the bedrock of secure interactions within NDN. This dynamic key exchange mechanism bolsters security and facilitates controlled data generation. Certificates play a pivotal role in establishing trust, solidifying the connection between names and public keys, while trust policies align with application-defined guidelines to govern key-data entity associations. This paper delves into the security challenges that NDN effectively addresses. By investigating vulnerabilities and assessing the susceptibility of common attacks, it underscores the resolute defense mechanisms inherent in the NDN framework. The inherently anonymous and non-identifying nature of NDN renders eavesdropping and traffic analysis infeasible, bolstering confidentiality. Digital signatures provide robust data integrity, thwarting any attempts at unauthorized modification. The comprehensive data signing mechanism effectively prevents masquerade attacks, and the integration of unique naming and nonce in Interest packets averts replay attacks. In essence, this research not only sheds light on the essential security components of NDN but also illuminates how this paradigm addresses a multitude of security challenges. NDN's forward-thinking content-centric approach and its robust security mechanisms hold the potential to reshape networking paradigms. As we look to the future of the internet, NDN stands as a promising contender for advancing secure, efficient, and transformative networking models.
Investigating the Role of Machine Leaning and Blockchain for Forecasting Stock Market Trends
Authors: Ritu Jain, Anjali Dadhich, Roshni Rajput Dinesh Singh Dhakar,
Published: April 2024

There is a sudden deluge of evolutionary approaches in the domain of data processing and computation, which are significantly affecting several facets of applications globally. Artificial intelligence, machine learning and Blockchain happen to be at the forefront of evolutionary computation beating conventional approaches. Finance applications currently are heavily reliant of data driven models. Stock trend analysis happens to be one such approach, which lays the foundation for forecasting decisions to be made. The leeway in such applications is critically small as minimal inaccuracies in forecasting may lead to major losses. This paper presents the current perspective in terms of evolutionary algorithms such as artificial intelligence and Blockchain and how they are transforming software development, the application of machine learning algorithms to regression problems. Finally, the stock trend analysis based on in and out of sample datasets has been performed for standard S&P datasets. A comparative analysis with previous work clearly indicates the improved performance of the proposed work with respect to baseline approaches in the domain.
A Systematic Literature Review on the Islamic Capital Market: Insights Using the PRISMA Approach
Authors: Rupali Atul Mahajan, Arup Kadia, Monika Singh Rajesh Dey, Salina Kassim, Sudhanshu Maurya,
Published: April 2024

This paper employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach to conduct a comprehensive systematic literature review on the Islamic capital market (ICM). The ICM, governed by Shariah principles, has garnered considerable attention in both academic and practitioner spheres. Through the systematic analysis of scholarly articles, this review synthesizes existing literature on the ICM, examining its structure, development, regulatory frameworks, financial instruments, performance evaluation, and integration with conventional financial markets. This paper identifies gaps in the literature and proposes avenues for future research, aiming to enhance understanding and promote further inquiry into the ICM's role in global finance.
Defect Detection of Cell Phone Screen Using a Faster Regional Convolutional Neural Network with Multi-head Attention Mechanism
Authors: Shuhui Yang Dayong Yu,
Published: April 2024

Since the glass outer screen of a cell phone is the main sensory part of the human eye when using a cell phone, the advantages and disadvantages of the cell phone screen directly affect people's sense of use. Therefore, the defect detection requirements for cell phone screens are high and need to meet the needs of high-volume factory inspection. Most of the traditional defect detection methods use visual methods, the detection results are overly dependent on the subjectivity and experience of workers, the efficiency of this method is low, and the accuracy is poor. Currently, machine learning-based detection methods are applied in numerous industries. In this paper, a faster Regional Convolutional Neural Network (R-CNN) with multi-head attention mechanism for defect detection of cell phone screen is proposed. To enhance the network's capability in extracting feature information, a four-head attention mechanism is added to the last convolutional layer of the ResNet50 network. An improved Region of Interest (ROI) Align is proposed to replace the original ROI Pooling to reduce the localization error of cell phone screen defects. Replace the original Rectified Linear Unit (ReLU) activation function with the Copy Exponential Linear Unit (CELU) activation function to expedite the convergence capability of the network. Finally, by comparing with other classical model training, the evaluation results indicate that the proposed method achieved an average accuracy of 95.71%, which is a 5.34% improvement compared to the original faster R-CNN network.
Camera Analysis of Commercial Vehicle Camera Monitor System
Authors: Yao Zhang Xiaoxiao Zhao, Xian Liu,
Published: April 2024

This article conducts research and analysis on the camera part of the commercial vehicle camera monitor system, which meets the field of view requirements of GB15084-2022 “Performance and Installation Requirements for Indirect Vision Devices on Motor Vehicles”. By calculating the field of view angle of the camera, it is concluded that a camera with a horizontal field of view angle of 120°and a vertical field of view angle of 61.5°can meet the requirements of the field of view angle; The camera adopts a bias design, which can effectively reduce the side distortion of the carriage; The camera heating scheme adopts a heating time of 100s and a heating rate of 0.36℃/s heating logic, which can remove frost on the surface of the lens. This conclusion provides important reference significance for the selection of parameters such as camera field of view angle in commercial vehicle camera monitor systems.
Research on Rapid Construction Technology of Deep Water Rock-socketed Bridge Foundation
Authors: Li Shaoyi
Published: April 2024

The construction of deep water rock-socketed bridge foundation is pretty difficult and people routinely use the method of combining underwater blasting and double-wall steel box cofferdam support for construction. Combined with the site construction conditions and analysis of FEM software, through schemes comparison and technical research, the paper puts forward the construction scheme of rotary drilling pilot hole, high-pressure rotary jet grouting pile for grouting and water stopping, and cofferdam support for locking steel pipe pile, which completes the foundation construction smoothly and rapidly. By adopting the technology, the construction period is greatly shortened, the material utilization rate is improved, the construction cost is reduced, and the scheme has reference significance for foundation construction under similar environments.
Research on the Implementation Strategy of Competence-Oriented Postgraduate Cultivation Programme for Local Applied Universities with Big Data Technology
Authors: Jing Cao, Min Liu Xihuang Lai, Zhihui Yang,
Published: April 2024

Prioritising the innovation capacity of postgraduates is paramount, with an effective training model serving as the linchpin. As information technology rapidly evolves and the era of big data looms, postgraduate education faces unprecedented challenges and opportunities. It is imperative for institutions to harness the potential of big data development, leveraging new technologies to enhance the talent development process. The postgraduate programme is the baton for talent development, and incorporating new information technology requirements and multidisciplinary synergies into the programme environment can reinforce the enhancement of innovative practical skills and data literacy, among others. In this way, the institutions provide society with a supply of high quality talent and help to promote the development of new quality productivity. Due to factors such as the regional economy, teaching staff, student quality and academic reputation, some local applied universities find it difficult to fully implement various good ideas for the postgraduate development programme, which to some extent affects the synergy between talent cultivation and local economic development needs. Taking North China University of Technology as an example, this paper adopts the deep learning model in big data technology and integrates capacity training into the daily teaching and practice process of postgraduate students according to the principle of “classified training, optimized links and improved capacity”. The group explored the theme of harnessing the potential of digital technology to support the growth of postgraduate education. Through interviews with students, on-campus and off-campus tutors, alumni and other stakeholders in talent development, they re-imagined the framework for postgraduate education, embodying the principles of strengthening the theoretical base, enhancing the integration of industry and education, and prioritising capacity building, and taking advantage of the digitalisation and networking of education. The research results show that the digital transformation of the education process with the help of big data technology has a driving effect on improving the innovative practical ability and overall quality of graduate students. This measure provides a reference for similar universities to revise their talent training programmes, and enriches the solutions to the problem of "good programmes are difficult to implement" in graduate student training.
Computer Modeling Analysis of Electric Vehicle's Choice Behavior Considering Latent Variables
Authors: Shijun Lu, Jianhu Wang Lixiao Wang, Renyuan Zhao,
Published: April 2024

At present, there are few researches on the influence of psychological latent variables on the choice behavior of electric vehicles, and lack of consideration of individual heterogeneity. In this study, computer modeling was used to construct not only a traditional discrete choice model that takes into account individual heterogeneity, but also a hybrid choice model,which takes into account psychological latent variables are constructed to explore the mutual effects of observable personal attributes, vehicle attributes and difficult to directly observe psychological latent variables on consumers’ choice behavior of electric vehicles. The estimation results of hybrid choice model with psychological latent variables and the traditional discrete choice model without considering psychological latent variables are compared. The result show that perceived usefulness, perceived risk and purchasing attitude have a significant impact on consumers’ choice behavior of electric vehicles. Compared with traditional discrete choice model, the hybrid choice model, which takes into account psychological latent variables has significantly higher prediction accuracy and has better interpretation ability and fitting effect. The research results can provide theoretical support for further expanding the electric vehicle market and formulating relevant policieshe usage of the word Power quality in recent times acquired intensified interest due to the complex industrial processes. The usage of intelligent tools to improve power quality is increasing day by day, as assumption of present day power system as a linear model is unsatisfactory. This paper deals with analysis of Differential Evolution (DE), Hybrid Differential Evolution (HDE) and Variable Scaling Hybrid Differential Evolution for harmonic reduction in the source current with optimal tuning of PI controller gain values. Shunt Active power Filter is one of the better solution to suppress the source current harmonics which are induced into power system because of nonlinear loads. Current controller called HBCC is considered for gating operation of switches in Voltage Source Inverter. The Intelligent tuned PQ theory is used for reference current generation. The then obtained compensating currents are injected at point of common coupling for current disturbance mitigation. Simulations of MATLAB/SIMULINK environment of the present work shows the efficacy.
Research on the Sentiment Analysis and Evolutionary Mechanism of Sudden Network Public Opinion Based on SNA-ARIMA Model with Text Mining
Authors: Mingying Shi, Mengru Wang, Yongcong Luo Jiaqi Mo, Ziqi Wang, Yuhan Jiang,
Published: April 2024

This article is based on the massive text data in the digital era, using text mining methods to explore the evolution mechanism of online public opinion, in order to achieve the control of public opinion trends in sudden network events and regulate the stability of online and offline public opinion. We introduced time series for decision-making assistance, combined with time series autoregressive integrated moving average model (ARIMA) for prediction, and comprehensively evaluates the evolution of public opinion from two dimensions: sentimental orientation and social network analysis (SNA). Based on this, a new exploration model for public opinion evolution, SNA-ARIMA, is constructed. The research results indicate that the process of public opinion dissemination exhibits distinct phased characteristics, and each stage is comprehensively influenced by different key nodes or factors. This study provides decision support for managing public opinion crises, making the identification of key nodes in online public opinion events, the evolution mechanism of public opinion, and the guidance of public opinion crises more systematic, forward-looking, and scientific.
Loss And Temperature Rise Analysis of Permanent Magnet Motor Based on Magneto-thermal Coupling Field
Authors: Chunyu Qu
Published: April 2024

Because of the situation of motor performance degradation caused by permanent magnet demagnetization and increased loss of built-in permanent magnet synchronous motor under high temperature working conditions, this paper optimizes the rotor magnetic circuit structure of the motor and uses amorphous alloys. The material stator core replaces the original ordinary silicon steel stator core to suppress the motor temperature increase and reduce the motor loss. Through simulation, comparative analysis and calculation, the result could show that losses and temperature increase are significantly reduced after optimization. Finally, temperature rise testing was conducted on the prototype, confirming the correctness of the improved model.