bluuebunny/crossref_metadata_2025 huggingface dataset:
Frequency Analysis of Rainfall with Different Methods based on the Missing Data Processed by Random Forest Algorithm
Authors: Boyao Zhan, Manqiu Hao, Xiaomin Ji Xin GUO, Zhijie Yin, Cheng Gao,
Published: April 2024

Global warming is causing dramatic climate change, leading to rainfall that triggers a more severe risk of flooding. The conventional moment method and linear moment method’s basic theories for the design of flood frequency analyses were introduced and compared for frequency analysis of rainfall. The rainfall data from 149 long-series representative rainfall stations in Jiangsu Province were processed using the random forest (RF) algorithm to address the missing data encountered during the actual rainfall monitoring process. The frequency was calculated using conventional and linear moment methods, and the differences and advantages of the two methods were analyzed by comparing the calculation results of different sites under each method. The results show that under low design frequencies, both the conventional moment and linear moment methods exhibit minimal errors, rendering them suitable for calculating design rainfall. The linear moment method outperforms the conventional moment method in terms of the unbiasedness of the estimation process and for very large values, and that the parameters estimated by the linear moment method are more accurate. In practical hydrological frequency calculations, different computation methods can be chosen according to specific needs to enhance calculation accuracy.
Research on Provincial Distribution of Total Carbon Emissions in the Power Generation Industry under Carbon Neutrality Constraints
Authors: Chengwu Liu Xiaoxuan Ma, Qiqi Wang,
Published: April 2024

The power generation industry in China is at the forefront of implementing carbon trading market policies, playing a crucial role in carbon emission reduction. Carbon allocation in each province is pivotal for future economic development and achieving a gradual and orderly carbon peak. By analyzing total carbon emissions from 2012 to 2021 and examining emission responsibilities from a producer perspective, this study extends the STIRPAT model to evaluate factors influencing carbon emissions in the national and provincial power generation sectors. Key findings highlight the urgent need for industrial structure changes in provinces like Xinjiang, Tibet, Guangdong, Shandong, and Jiangsu, which bear significant emission reduction responsibilities. The spatial and temporal distribution of carbon emissions is shifting from the southeast coast to central and western regions. Factors such as energy consumption, electric power intensity, urbanization rate, power structure, economic development level, and industrial composition significantly impact carbon emissions. To achieve carbon peak goals, a multifaceted approach is essential for reducing emissions in China's power generation sector, emphasizing the need for strategic interventions across various perspectives.
Spatial-temporal Evolution and Driving Factors of Desertification Based on Computer Interpretation in Tarim River Basin
Authors: JianHu Wang, Zengjin Liu, Kaixu Wang Shijun Lu, Guoqing Yu, Lixiao Wang,
Published: April 2024

The Tarim River Basin is an important life hinge in Xinjiang, which plays a vital role in maintaining ecological stability. In recent years, it is faced with challenges such as the expansion of sandy land, the decrease of precipitation and sand-dust storm, which lead to the aggravation of desertification and the contradiction between man and land. This study used remote sensing images from 2000 to 2020 to analyze desertification trends through automatic interpretation by computer, combined with NDVI(Normalized Difference Vegetation Index) and FVC(Fractional Vegetation Cover) indicators. The results showed that the total area of desertification land decreased, but the area of serious desertification land increased. Precipitation, Sunshine, wind speed, air temperature, cultivated land area change, urbanization and other factors affect desertification, of which cultivated land area change, temperature change and precipitation change are the most important factors. Policies focused on preventing wind erosion have been inadequate as the economy has developed and conflicts between people and land have intensified.
An Analysis and Improvement Scheme for the Weakness of Kerberos V5 Authentication
Authors: Sha Feng, Dongming Yu Juanjuan Gong, Tao Men,
Published: April 2024

The security protocol of network system can ensure the reliability of network communication. Kerberos protocol is a kind of security verification mechanism based on shared key, which has been adopted for a long time on the Internet. This text introduces the authentication process of Kerberos v5 protocol. To the defect that the authentication server don’t authenticate the user’s identity but it hold safe in building on the basis whether the user can correctly decrypt the ciphertext message in the protocol, we put forward a scheme that adding in the Challenge/Response mechanism to improve its security in Symmetric Cipher Mechanism. Passing the improved scheme’s analysis we can perceive the method that adding in Challenge/Response identity authentication technology will greatly resist Offline Password Conjecture Attack.
Optimization of Online Advertising Decision Combination Based on the Attribution of Consumer Behavior Big Data
Authors: Baozhu Feng Han Nie,
Published: April 2024

In the field of internet advertising, associated products based on various big data algorithms are widely presented on consumers’ browsing pages in order to increase their purchasing interest. With the help of computer big data processing, online advertisers can better grasp the shopping needs of consumers. This study proposes a chance constrained programming optimization model (CC-PR) for budget allocation of advertisers in a dynamic and complex consumer behavior context, based on the premise of analyzing consumers’ shopping journey from the perspective of the attribution of consumer behavior effects. It models different online behaviors generated by consumers’ interest transfer at different shopping stages. The characteristic of this method is to simulate changes in consumer psychology, overcome the difficulty of predicting multi-stage behavior of consumers, and extract a model of dynamic generalizable laws of consumer psychology. It dynamically adjusts advertising strategies based on changes in consumer tendencies at different stages. In order to verify the effectiveness of the proposed strategy, this paper introduces batch data from large shopping platforms and compares our method with the baseline method through experiments. The results show that the CC-PR method proposed in this paper has superior performance. A large amount of consumer behavior data can help advertisers make more accurate advertising decisions, and the method proposed in this article is also a good example of the application of computer big data processing in the field of online advertising.
Evolution and Development of Artificial Intelligence Interpretation Technology in the Age of Large-scale Language Models
Authors: Peipei Zhou Hao Peng,
Published: April 2024

With the continuous development of artificial intelligence technology, large-scale language models have become an important breakthrough in the field of interpreting intelligence technology, and the interpreting profession is facing unprecedented technological changes; Through reviewing the relevant research background, it was found that the rise of large models has brought new opportunities and challenges to the development of artificial intelligence interpreting technology; Firstly, the language generation ability of the large model is powerful, which can achieve more accurate and natural interpretation and translation results. Secondly, large models have strong learning abilities and can continuously optimize the performance of interpretation systems through large-scale data training. In addition, the large model also has the characteristic of fast response, which can provide efficient interpretation services in real-time scenarios; The age of large models provides new opportunities and prospects for the transformation and development of artificial intelligence interpretation technology. In the future, with the continuous evolution of large model technology and the expansion of application scenarios, artificial intelligence interpretation technology is expected to play a more important role in cross language communication, international cooperation, and other aspects. However, attention should also be paid to the application limitations of large models and issues such as collaboration with human translators.
Construction and Exploration of Experimental Teaching Content in Software Engineering from a Systems Theory Perspective
Authors: Wang Xianping
Published: April 2024

Software engineering, as a branch of traditional computer science, distinguishes itself from other computer-related disciplines by its emphasis on practical application. Experimental teaching in software engineering, integral to professional education, serves as a crucial means to enhance students’ practical skills. The content of experimental teaching, serving as a vessel for such instruction, plays a pivotal role in the holistic development of students. Addressing the current fragmented state of experimental teaching in applied universities, this study introduces a holistic and systematic view based on systems theory. By integrating the fundamental principles and methods of systems theory with the distinctive characteristics of software engineering, this study focuses on analyzing the holistic technology of software implementation and the interrelationships among various technical elements of software systems within the field of software engineering. From a holistic-part-holistic analytical perspective, this research constructs an analytical model for experimental teaching content in software engineering under the lens of systems theory. The study elaborates on the construction method of this model and its specific implementation details from the perspectives of talent cultivation programs, engineering elements, and concentrated practical exercises. Practical evidence indicates that the implementation of this experimental teaching content effectively enhances students’ practical skills in software engineering, elevates students’ recognition of their field of study, boosts their confidence in job placement, and provides robust support for seamless transition from graduation to employment. Furthermore, this study sheds light on the construction of experimental teaching content in computer-related fields in engineering disciplines at other applied universities.
Design of Yellow Peach Quality Identification System Based on PlC and Machine Vision
Authors: Yan Wang, Jinjing Zhang Lin Li, Fengjiao Jiang, Nan Li,
Published: April 2024

China is the main producer and exporter of canned fruit, leading the world inhe processing and consumption of canned fruits. Most of the fruit can processing lines are semi-automated, while the fruit quality is mostly identified by naked eye and thus needs to be automatically identified again before the sealing of the cans on the production line for prevention of inferior pulp from entering the canned bottles. Based on this, this paper designs a fruit quality identification system for PLC canned yellow peach production line based on machine vision taking canned yellow peaches as an example, and analyzes the difficulties in identifying the control of sub-packaging. Software programming and hardware design were carried out for the visual inspection of canned yellow peaches, and the simulation and implementation of the system were completed. This system reduces labor costs, improves the production environment, and provides favorable solutions to the construction of semi-automated production workshops that meet the needs of enterprise development. Its promotion is of significant value.
Effects of Exercise Intervention on Aerobic Capacity and Metabolic Indexes of Obese Adolescents from the Perspective of Computer Application
Authors: Jian Yang Xiaowei Wei, Chunyan Wei,
Published: April 2024

The effects of 4-week aerobic training prescription intervention on aerobic capacity, body composition, blood lipid and C-reactive protein (CRP) in obese adolescents were analyzed by digital technology and computer software. The subjects were obese patients, aged 13-18 years old, 40 adolescents with body fat ratio >30%, including 20 males and 20 females. Body shape, fasting blood glucose, blood lipid, blood insulin and serum hs-CRP were measured before and after the intervention, and the data were divided by statistical analysis. Conclusion: Abdominal fat accumulation in obese adolescents is excessive, serum hs-CRP level is increased, waist circumference is significantly correlated with hs-CRP level, lipid metabolism disorders (TC, HDL-C, LDL-C) and insulin resistance are closely related to the increase of hs-CRP level, which may be a comprehensive risk factor for cardiovascular and other pathological changes in obese patients. 4 weeks of aerobic exercise can effectively reduce the body fat percentage of obese adolescents, improve waist circumference and hip circumference, improve lipid metabolism, significantly reduce the fasting insulin and hs-CRP levels of obese adolescents, and will play a positive role in preventing and reducing the occurrence and development of cardiovascular diseases in obese adolescents.
Application of Using Improved Genetic Algorithm in Art Design
Authors: Hongying Song Min Li,
Published: April 2024

Art design encompasses the creative process of conceptualizing and crafting visual and aesthetic elements to convey messages, evoke emotions, and stimulate thought. It involves the deliberate selection and arrangement of colors, shapes, textures, and forms to create meaningful and impactful compositions across various mediums such as painting, sculpture, graphic design, and multimedia. This paper proposes an innovative approach to enhance art design through the application of an improved genetic algorithm (GA) coupled with Statistical Spider Swarm Optimization (SS-O). By integrating GA's evolutionary principles with the adaptive capabilities of SS-O, the proposed framework aims to optimize the generation of artistic compositions across various media. Through a series of experiments and simulations, the effectiveness of the hybrid algorithm is evaluated in terms of its ability to generate aesthetically pleasing designs while balancing artistic diversity and coherence. The improved GA component facilitates the exploration of diverse design solutions, while SS-O provides dynamic optimization capabilities by leveraging statistical analysis of swarm behavior. Simulation results demonstrate the efficacy of the approach, with generated designs achieving fitness scores ranging from 0.88 to 0.95, indicative of high aesthetic appeal. Moreover, the diversity index values ranging from 0.75 to 0.82 underscore the algorithm's ability to produce a varied range of artistic compositions. These numerical outcomes signify the potential of the proposed approach to revolutionize the creative process in art and design domains, offering new avenues for generating captivating visual expressions.
Novel Multimedia Feature Fusion Classification (Mffc) Model for Sports Game Design Enhancement
Authors: Xiaodong Wang Yang Qiao, Jianting Liu,
Published: April 2024

Multimedia feature fusion is reshaping the landscape of sports game design, offering immersive and dynamic experiences for players and spectators alike. By integrating various multimedia elements such as audio, video, graphics, and interactive interfaces, game designers can create engaging environments that simulate the excitement and intensity of real-life sporting events. From lifelike player animations to realistic crowd reactions, multimedia feature fusion enhances the realism and authenticity of sports games, transporting players into the heart of the action. Furthermore, interactive interfaces enable players to customize their gaming experience, from choosing team strategies to controlling player movements, adding depth and complexity to gameplay. With advancements in technology, such as virtual reality and augmented reality, multimedia feature fusion is poised to revolutionize sports game design, offering unprecedented levels of immersion and interactivity for players worldwide. This paper explores the application of multimedia feature fusion in sports game design, utilizing the Multimedia Feature Fusion Classification (MFFC) framework alongside key feature extraction with the genetic model. By integrating various multimedia elements including audio, video, graphics, and interactive interfaces, the MFFC framework enhances the realism and excitement of sports games, offering immersive experiences for players. Through the genetic model, key features are extracted and optimized to improve gameplay dynamics, player interactions, and overall gaming experience. The effectiveness of the proposed approach is evaluated through simulated experiments and empirical validations, demonstrating significant enhancements in game realism, user engagement, and satisfaction. Results indicate a 30% increase in player immersion and a 25% improvement in gameplay dynamics compared to traditional game design methods.
Practice of Digital Media Art Education Based on Virtual Reality Technology
Authors: Xu Baomin
Published: April 2024

Digital media art education has entered a new era with the integration of virtual reality (VR), offering students immersive and interactive learning experiences. Through VR technology, students can explore the creative possibilities of digital media art in a simulated environment that transcends traditional classroom boundaries. From interactive installations to virtual exhibitions, VR enables students to experiment with diverse mediums, techniques, and concepts in a safe and engaging space. Moreover, VR facilitates collaboration and peer feedback, allowing students to showcase their work, receive critiques, and collaborate with peers from around the world in virtual studios and galleries. This paper explores the practical implementation of digital media art education utilizing virtual reality (VR) technology, augmented by Multimedia Linear Statistical Feature Classification (MLSC). VR technology offers immersive and interactive learning environments, allowing students to explore and create digital media art in three-dimensional spaces. The incorporation of MLSC enhances the educational experience by providing personalized feedback and assessment based on statistical analysis of multimedia features. Through simulated experiments and empirical validations, the effectiveness of the VR-based digital media art education approach with MLSC is evaluated. Results demonstrate significant improvements in student engagement, creativity, and learning outcomes. The students using the VR platform with MLSC achieved a 40% increase in project completion rates and a 30% improvement in artistic proficiency compared to traditional methods. These findings highlight the potential of combining VR technology with MLSC to revolutionize digital media art education, preparing students for careers in the dynamic and interdisciplinary field of digital media.
Campus Culture Construction of Colleges and Universities Based on Virtual Reality Technology
Authors: Ouyang Xiu
Published: April 2024

This paper presents a novel approach to campus culture construction in colleges and universities utilizing virtual reality (VR) technology enhanced by Cartesian point Probabilistic LSTM (CPP-LSTM). The study aims to enhance campus culture by creating immersive virtual environments that promote engagement, collaboration, and learning among students and faculty members. Through simulated experiments and empirical validations, the efficacy of the CPP-LSTM-enhanced VR technology is evaluated in creating realistic and interactive virtual campus environments. Results demonstrate significant improvements in user engagement and satisfaction compared to traditional methods. For instance, users interacting with the CPP-LSTM-enhanced VR platform reported a 30% increase in perceived sense of belonging and a 25% improvement in overall satisfaction ratings. Additionally, the CPP-LSTM model enables the generation of personalized virtual experiences based on user preferences and feedback, leading to more immersive and impactful campus culture construction. These findings underscore the potential of VR technology with CPP-LSTM in revolutionizing campus culture construction and fostering a vibrant and inclusive campus community.
Innovative Integration of Convolutional Neural Networks for Enhanced Face Recognition
Authors: Rajender Singh Chhillar Kavita,
Published: April 2024

The face Recognition technique is important in Computer Vision nowadays. The research study focuses on a Face Recognition system that uses deep learning to identify face photos. Face detection and categorization are carried out using various Convolutional neural network (CNN) models using deep learning methods. Prior research has mostly focused on either the ResNet or DenseNet-based CNN models. The current study combines ResNet and DenseNet to create a hybrid model. The suggested work aims to improve efficiency and accuracy. During the simulation's training and testing phases, categories are taken into account. The present study is centered on the Labeled Faces in the Wild (LFW) dataset. The photos go through an initial noise reduction procedure. Picture quality assessment involves considering measures like MSE, PSNR, and SSIM. Once the suggested model has completed training, it produces high-quality images. The suggested system includes the Innovative CNN approach framework, which combines DenseNet with a noise reduction approach, a segmentation mechanism, and a ResNet model based on CNN. Comparative research was performed to assess the precision of several filtered image collections using different convolutional neural network models. The simulation results show that the proposed model had higher performance and accuracy than traditional ResNet and DenseNet models.
Empowering Agriculture: Leveraging Intelligent Systems for Sustainable Farming
Authors: Sandeep Kumar, Monali Gulhane, Nitin Rakesh Shilpa Choudhary,
Published: April 2024

Climate change, market volatility, water scarcity, and pest control issues are just a few of the challenges that farmers face. Limited access to resources and education, as well as the slow adoption of new technologies, aggravate these problems. Creating intelligent systems is necessary to handle these pressing issues. This method makes use of key components such as crop type, weather, and soil properties to forecast agricultural yields. This system offers significant yield prediction capabilities for 53 crops by utilizing complex algorithms including XG-Boost, random forest, and decision tree. It takes into account crucial factors to accurately assess crop production potential, such as temperature, rainfall, nutrient levels (N, P, and K), soil pH, and temperature data. Cutting-edge machine learning techniques look at past data and trends to provide farmers with crucial information they need to make wise decisions. In order to solve the problems farmers, encounter in the modern agricultural sector, this intelligent system aims to enhance resource efficiency, farming systems' resilience and production, and the use of resources.
Electromyography Sensor Controlled Bionic Hand for Disabled Persons
Authors: Yashika Gaidhani, Monica Kalbande Tejaswini Panse,
Published: April 2024

The word “Bionic” is amalgam of domain Biology as well as Electronics. In today’s era, we come across new emerging technology which is rapidly evolving. Bionic hand is one of its kinds. However, with upgrade technology the demand for bionic hand will continue to increase day by day in upcoming years with wider awareness. The major purpose behind the project is to help an individual to improve the sensation, integration with the body and the control. The development and operation of bionic hand has been explained and controlled by Electromyography Muscle Sensor. Signals from an amputee muscle are gathered by bionic arms to function. The muscle sensor is the main component which senses electrical activity of our muscles and translate those activity which results in flexing of finger with the help of servo motors. 3-D printing is used in the design and construction of the bionic arm. The result we obtain here after demonstration is 80% movement of little finger, ring finger, middle finger and fore finger as well as 30% movement of Thumb finger. Along with this when pressure is strongly applied, the signals from the muscle goes to the sensor and hence result in approximate 75% movement of all five fingers together.
Enhancing Papaya Leaf Disease Detection with CNN and Transfer Learning Fusion for Precise Disease Diagnosis
Authors: Suresh D. Shirbahadurkar Snehal J. Banarase,
Published: April 2024

Papaya cultivation plays a vital role in global agriculture, providing a crucial source of nutrition and economic stability. However, the threat of diseases poses a significant challenge to papaya plant health. To address this challenge, we proposed an innovative approach for enhancing the detection of papaya leaf diseases using Convolutional Neural Networks (CNNs) and transfer learning fusion. Our proposed framework leverages the strengths of CNNs, known for their ability to extract intricate features from images, and the transfer learning approach combines these techniques to create a robust and efficient model for accurate papaya leaf disease diagnosis. Transfer learning approach with two pre-trained models: VGG-16 and ResNet-50 are taken to detect papaya leaf diseases. The training process involves utilizing a pre-trained model and fine-tuning it on a specific papaya leaf disease dataset. For experimentation, five different classes of papaya leaves such as Fresh Papaya Leaf, Papaya Black Spot, Papaya Leaf Curl, Papaya Ringspot, and Powdery Mildew of Papaya are considered. Experimental results demonstrate the effectiveness of our proposed approach in accurately identifying and classifying various papaya leaf diseases. The proposed model has received an accuracy of 99.79% using ResetNet-50 followed by CNN whose accuracy was 99.42% and VGG-16, whose accuracy was 98.25%. This fusion approach aims to create a robust and efficient model capable of distinguishing subtle patterns indicative of various papaya leaf diseases. This method not only improves the efficiency of disease detection but also demonstrates the adaptability of the model to various disease patterns. Our study contributes to the advancement of automated papaya leaf disease detection, providing a reliable and precise tool for early diagnosis. Overall, our approach offers a promising solution for improving papaya cultivation and ensuring a sustainable future for global agriculture.
Control Strategy for Ensuring Uninter-rupted Operation of a Solar Water Pump with Islanding Technique
Authors: S. G. Kadwane A. S. Lilhare,
Published: April 2024

This research explores strategies to bolster the resilience of solar water pump systems through the implementation of islanding techniques and seamless grid integration. In the face of increasingly dynamic energy landscapes, the need for reliable and adaptable solutions for water pumping is paramount. The study investigates the application of islanding techniques, focusing on their effectiveness in enhancing system resilience and ensuring uninterrupted water supply. Furthermore, the integration of these solar water pumps with the grid is examined, aiming to strike a harmonious balance between renewable energy utilization and grid connectivity. The findings contribute to the advancement of sustainable water pumping technologies, offering insights into the synergy of islanding techniques and grid integration for resilient solar-powered water pump systems.
Enhancing Breast Cancer Collagen Segmentation with Improved Swim Transformation Algorithm
Authors: Ritesh V. Patil Swati Bula Patil,
Published: April 2024

Breast cancer is a major health problem around the world, and accurately separating collagen fibers in breast cancer cells is important for both detection and planning treatment. Because it can make the differences between fibers and the background stand out more, the Swim Transformation (SWT) algorithm has shown promise in separating collagen fibers. However, the original SWT algorithm has some flaws, such as being easily affected by noise and artifacts. We suggest a better version of the SWT algorithm in this study to fix these problems and make collagen segmentation in breast cancer tissues better. A new step in the preparation process was added to cut down on noise, and adaptable thresholding was added to make it easier to find collagen fibers. We also add a step called "post-processing" to get rid of any errors and make the segmentation results better. We used a collection of microscopic images of breast cancer tissues in tests to see how well the proposed method worked. For segmentation, we looked at how our better SWT algorithm did compared to the original SWT algorithm and other cutting-edge segmentation methods. Based on our data, the suggested method does a better job in terms of accuracy, sensitivity, and precision. In addition, we add a deep learning model for finding breast cancer that uses Long Short-Term Memory (LSTM) and Bidirectional LSTM models to our work. A set of images of breast cancer is used to teach a deep learning model that can tell from collagen segmentation results whether or not there are dangerous cells. The paper shown an improved SWT algorithm for separating collagen in breast cancer cells. This algorithm fixes the problems with the first one and gets better results for separating collagen. It's possible that the suggested formula will help doctors find and plan better treatments for breast cancer by making it easier to separate collagen fibers more accurately.
Operational Elements Influencing the Occurrence of Unfavorable Incidents in the Railway System.
Authors: Ram B. Ghogare, Swapnaja Amol Ubale, R. M. Avchar Swagat M. Karve, Ashwini B. Gavali, Manjushree V. Gaikwad,
Published: April 2024

The article delves into the intricacies of rail transport, addressing the repercussions of adverse events such as delays, incidents, and accidents within the railway system. It explores the impact of disruptions on the day-to-day responsibilities of railway employees, with a primary focus on Station managers. The study employs two distinct approaches: first, an experimental analysis involving traffic management personnel, where factors contributing to failures are examined through a questionnaire survey. Second, a comprehensive perspective is taken by leveraging expert knowledge, combined with Fault Tree Analysis and Event Tree Analysis methodologies. This combination is used to illustrate how alterations in the system, including erroneous decisions, can lead to subsequent undesirable events.
Deep Learning Model for Retrieving Color Logo Images in Content Based Image Retrieval
Authors: Gagandeep Kaur, Vedant Pinjarkar, Rutuja Rajendra Latika Pinjarkar, Jaspal Bagga, Poorva Agrawal,
Published: April 2024

Content-Based Image retrieval (CBIR) has gained a magnificent deal of consideration because of the digital image data's epidemic growth. The advancement of deep learning has enabled Convolutional Neural Networks to become an influential technique for extraction of discriminative image features. In recent years, convolutional neural networks (CNNs) have proven extremely effective at extracting unique information from images. In contrast to text-based image retrieval, CBIR gathers comparable images based primarily on their visual content. The use of deep learning, especially CNNs, for feature extraction and image processing has been shown to perform better than other techniques. In the proposed study, we investigate CNNs for CBIR focusing on how well they extract discriminative visual features and facilitate accurate image retrieval.  Also Principal Component Analysis and Linear Discriminant Analysis are combined for optimization of features resulting in boosting the retrieval results.  Using hierarchical representations learned by CNNs, we aim to improve retrieval accuracy and efficiency. In comparison with conventional retrieval techniques, our proposed CBIR system shows superior performance on a benchmark dataset.
An Efficient Multiple Sclerosis Segmentation Framework using Hybrid Dilated Convolution –based Adaptive Mobilenet Mechanism.
Authors: Beiji Zou, Xiaoyan Kui, Kevin Njagi Gilbert Langat,
Published: April 2024

Multiple Sclerosis (MS) is considered a very popular neurological condition in adults. Poor walking stability is considered the primary sign of MS. The Magnetic Resonance Imaging (MRI) is ensured to be a delicate approach for discovering disease progression. Severity quantification of the disease via MS lesion volume validation with MR imaging is significant for monitoring and learning the disorder and its treatment. Numerous techniques for MS lesions segmentation demand experts seed points as input, yet do not sufficiently permit the specialists to perform effectively or intuitively. Interestingly, various approaches also consider that the points are optimally determined. Hence, it is necessary to rectify some of the troubles that existed in classical MS classification mechanisms by developing a powerful process with deep learning in this work. In the beginning, the requisite MS images are fetched from the existing online source and forwarded to the module of image segmentation. In this module, an efficient image segmentation operation is carried out employing Transformer Unet++ with MobileNetv3 (TUnet++-MNetv3). Further, the segmented images are given to the module named MS classification, where the Hybrid Dilated Convolution based Adaptive MobileNet (HDC-AMNet) is recommended to classify the MS. Moreover, the Enhanced Single Candidate Optimizer (ESCO) is adopted to optimize the parameters that exist in the MobileNet. Thus, the designed MS classification task achieves improved performance rates by contrasting it with its baseline techniques employing numerous metrics.
Indirect Field Oriented Control of Induction Motor
Authors: Bhavik Brahmbhatt Chetan K. Bariya,
Published: April 2024

The various control strategies for the control of the inverter-fed induction motor have provided good steady-state but poor dynamic response. In the last decades, many control techniques have been developed providing good performance. Field-oriented control is one of the most significant control methods for high performance AC electric machines and drives. In particular, for induction machines, indirect field oriented control is a simple and highly reliable scheme which has essentially become an industry standard. With the advent of the vector control methods, an induction motor can be operated like a separately excited dc motor for high performance applications.     The speed control is observed in closed-loop control applications. The speed regulation is achieved with a PI controller which converts the command speed into the command torque and therefore the speed of the drive. In this paper PI controller base indirect field orientation control of 3-phase 415V, 50Hz Induction motor using cascaded multilevel inverter is proposed in MATLAB/SIMMLINK 7.10.0 (R2010a).
Intrusion Detection using Network Traffic Profiling and Machine Learning for IoT
Authors: Eman H. Abd-Elkawy, Albia Maqbool Jihane Ben Slimane,
Published: April 2024

The proliferation of the Internet of Things (IoT) in various sectors, including healthcare, smart cities, and industrial automation, has significantly enhanced operational efficiency and service delivery. However, this widespread adoption has introduced new vulnerabilities, making IoT networks a prime target for cyberattacks. Traditional security mechanisms often fall short in protecting IoT devices due to their limited computational resources and the unique nature of IoT network traffic. This paper introduces a novel intrusion detection system (IDS) that leverages network traffic profiling and machine learning techniques tailored for the IoT ecosystem. By analyzing the behavioral patterns of network traffic, the proposed system can accurately identify malicious activities and potential threats in real-time, ensuring the integrity and confidentiality of IoT networks. The methodology encompasses data collection, feature extraction, model training, and evaluation stages, employing a combination of supervised and unsupervised machine learning algorithms to optimize detection accuracy. Experimental results, conducted on real-world IoT network datasets, demonstrate the effectiveness of our approach in detecting a wide range of cyber threats with high precision and recall rates. This research contributes to the cybersecurity domain by providing a scalable, efficient, and adaptive IDS framework that can be integrated into various IoT infrastructures to mitigate the risk of cyber intrusions.
A Novel Approach of Traffic Congestion and Anomaly Detection with Prediction Using Deep Learning
Authors: Mohamed Ben Ammar Jihane Ben Slimane,
Published: April 2024

Traffic congestion and anomalies present significant challenges to urban mobility, impacting economic activity and quality of life. Traditional traffic management systems often fall short in accurately predicting and mitigating these issues due to their reliance on static models and limited data sources. This study introduces a novel deep learning framework designed to enhance traffic congestion and anomaly detection and provide accurate traffic flow predictions. Leveraging a comprehensive dataset encompassing various traffic patterns and conditions, we employ a convolutional neural network (CNN) model, renowned for its efficacy in handling spatial data, combined with long short-term memory (LSTM) networks to capture temporal dependencies. Our approach distinguishes itself by incorporating real-time data and employing advanced feature extraction techniques, enabling the dynamic adjustment of traffic management strategies. The methodology section outlines the data preprocessing steps, model architecture, training process, and evaluation metrics employed. Experimental results demonstrate the model's superior performance over existing methods in terms of accuracy, precision, recall, and computational efficiency. The discussion elaborates on the model's practical implications for smart city initiatives and its potential to revolutionize traffic management systems. This study not only addresses the gaps identified in the literature review but also opens avenues for future research in applying deep learning to urban traffic challenges.
Exploring Spectral Patterns of Tomato Phytophthora Infections using the VMRNMAF Deep Learning Framework
Authors: Sree Kala T. Suresh D.,
Published: April 2024

Identifying and mitigating diseases affecting tomato crops pose significant challenges for researchers and agricultural producers alike. Effective disease detection is crucial for optimizing yields and implementing targeted treatments. This study presents a comprehensive framework, Vector Machine Recurrent Neural with Manual Activation Function (VMRNMAF), for early prediction of Phytophthora infestans and other common tomato diseases. Leveraging image preprocessing techniques and feature identification processes, VMRNMAF accurately distinguishes between healthy and infected tomato segments. By integrating Recurrent Neural Networks (RNNs) and manual activation functions in image segmentation, this framework enhances disease identification accuracy. Throughout deep learning methodologies, VMRNMAF streamlines the identification of bacterial, viral infections, offering a promising solution for improving tomato crop management practices. Experimental results demonstrate the efficacy of VMRNMAF in accurately identifying diseased areas within tomato crops, underscoring its potential for enhancing agricultural sustainability and productivity.
An Efficient Metaheuristic Technique for Wireless Sensor Network Integrated Micro Grid Environment Using Improved Whale Optimization Algorithm (Iwoa)
Authors: T. Sreekala K. Sreelatha,
Published: April 2024

Wireless Sensor Network (WSN) nodes require careful power management to maintain long term operation without regular battery changes. Battery replacement can be challenging and expensive in a microgrid scenario where sensors may be installed in remote or i naccessible areas. To extend the life of WSN nodes, energy efficient design and power management strategies should be used. A potential strategy for effectively monitoring, controlling, and managing the distributed energy resources (DERs) inside a microgri d is theincorporation of WSNs with microgrids. This research work uses Improved Whale Optimization Algorithm (WOA) for optimizing the routing and performance. The humpback whale's hunting style served as the inspiration for the WOA, a metaheuristic optimization algorithm. WOA is an optimization technique that is used with MG and WSNs. During the optimization phase, WOA establishes a balance between exploitation and exploration. To increase the lifespan of a WSN in a microgrid, effective energy management i s frequently needed. When considering the dynamic energy availability from renewable sources and the energy requirements of the microgrid components, WOA is used to optimise energy allocation and routing in WSNs. This may lead to increased energy efficiency and longer network longevity. The IWOA attains 97.5% PDR that is higher than the existing state of art techniques.
Design of an Iterative Method for Enhanced Retinal Image Analysis Using Stacked Deep Learning Operations
Authors: LK. Vishwamitra Jagadale Sachin Mohan,
Published: April 2024

The necessity for advanced diagnostic techniques in ophthalmology has become increasingly evident, particularly for conditions such as glaucoma and diabetic retinopathy, which necessitate precise retinal image analysis. Current methodologies, while effective to a degree, fall short in terms of accuracy, efficiency, and adaptability, especially in semantic segmentation and disease classification tasks. These limitations underscore the need for more sophisticated and reliable approaches to retinal image analysis. In response, this work introduces a comprehensive framework leveraging Fully Convolutional Neural Networks (FCNNs) for optic disc segmentation and Cup-to-Disc Ratio (CDR) estimation. The selection of FCNNs is predicated on their demonstrated proficiency in semantic segmentation, ability to discern spatial dependencies, and capacity to learn intricate structures within fundus images without relying on manually engineered features. This approach aims to surpass existing segmentation accuracy benchmarks, targeting over 95% Intersection over Union (IoU) while reducing the mean absolute error in CDR estimation to below 10%. To address the challenges of data variability and quality, the framework incorporates Contrast Limited Adaptive Histogram Equalization (CLAHE) for synthetic data generation, enhancing local contrast while preserving image integrity levels. This method is expected to improve contrast in augmented images by 30%, simultaneously enhancing image quality metrics. Furthermore, the introduction of an overlapping sliding window technique with adaptive patch sizes ensures meticulous coverage and analysis of fundus images, significantly elevating lesion detection sensitivity and reducing false positives. Lastly, the framework employs an innovative multi-disease classification strategy utilizing ensemble learning and stacking of CNN architectures. This synergistic approach amalgamates multiple base models to diminish overfitting risks and enhance generalization capabilities, aiming for a classification accuracy surpassing 95% in binary assessments and 85% for specific retinal diseases. The proposed model not only addresses the current gaps in retinal image analysis but also sets a new standard for precision, reliability, and efficiency in diagnosing and managing ocular diseases, marking a significant step forward in the application of artificial intelligence in ophthalmology.
Machine Learning and Enhanced Encryption for Edge Computing in IoT and Wireless Networks
Authors: Vaishali Raut , Prasanna Palsodkar , Mithun G Aush Bhalchandra M Hardas ,
Published: April 2024

Machine Learning (ML) and better encryption methods is a key part of fixing the security and speed problems that Edge Computing causes in the Internet of Things (IoT) and Wireless Networks. The goal of this study is to improve the security of edge devices and networks so that critical data created and processed at the edge stays private and secure. Anomaly detection, threat identification, and adaptable security mechanisms depend on machine learning algorithms in a big way. These algorithms allow for proactive defenses against cyber dangers that are always changing. The proposed system used homomorphic encryption and quantum-resistant cryptography, to make data more private and secure. Even in edge devices with limited resources, these security methods keep data transfer and storage safe. The combination of machine learning and stronger encryption not only protects the IoT environment but also makes the best use of resources by changing security measures on the fly as threats change. This study adds to the development of safe and effective edge computing models, which helps IoT and wireless networks become more popular. The results can be used in many situations, from smart cities to industrial robotics. This makes sure that the advantages of edge computing can be enjoyed without putting the safety and privacy of the linked systems at risk.
ECG Signal Classification for Detection of Hyperkalemia
Authors: Prasad Lokulwar,Vibha Bora Achamma Thomas,
Published: April 2024

Potassium Imbalance is a serious problem that is the leading cause of sudden cardiac deaths in chronic kidney patients (CKD). Currently blood test is the gold standard for detection of potassium imbalances. Electrocardiogram (ECG) signals provide a non- invasive way to view the cardiac activity of the heart. It can also be used to detect potassium imbalance in Chronic Kidney patients. However the unique characteristics and complexity of ECG signals make it a very challenging process. This paper presents a machine learning classifier for detection of hyperkalaemia in patients using features extracted from ECG. Feature extraction plays a very crucial role in the machine learning process as it helps to capture the essential information for the learning task which can be used for applications such as classification and detection. Ten statistical features were extracted from ECG signals of patients having potassium within normal range and those with elevated levels of potassium. Classification performance of four different classifiers namely Naïve Bayes Classifier, Support Vector Machine(SVM), K Nearest Neighbors(kNN) and Artificial Neural Networks(ANN) was compared using statistical features. kNN and ANN performed best with classification accuracy of 97.9%.The results we found are in-line with other state-of-art hyperkalaemia classification approaches