bluuebunny/crossref_metadata_2025 huggingface dataset:
Hybrid Deep Learning Algorithm for Insulin Dosage Prediction Using Blockchain and IOT
Authors: N. Laxmi Et al.
Published: December 2023

This paper addresses the problem of predicting insulin dosage in diabetes patients using the PSO-LSTM, COA-LSTM, and LOA-LSTM algorithms. Accurate insulin dosage prediction is crucial in effectively managing Diabetes and maintaining blood glucose levels within the desired range. The study proposes a novel approach that combines particle swarm optimization (PSO) with the long short-term memory (LSTM) model. PSO is used to optimize the LSTM's parameters, enhancing its prediction capabilities specifically for insulin dosage. Additionally, two other techniques, COA-LSTM and LOA-LSTM, are introduced for comparison purposes. The algorithms utilize a dataset comprising relevant features such as past insulin dosages, blood glucose levels, carbohydrate intake, and physical activity. These features are fed into the PSO-LSTM, COA-LSTM, and LOA-LSTM models to predict the appropriate insulin dosage for future time points. The results demonstrate the effectiveness of the proposed PSO-LSTM algorithm in accurately predicting insulin dosage, surpassing the performance of COA-LSTM and LOA-LSTM. The PSO-LSTM model achieves a high level of accuracy, aiding in personalized and precise insulin administration for diabetes patients. By leveraging the power of PSO optimization and LSTM modeling, this research improves the accuracy and reliability of insulin dosage prediction. The findings highlight the potential of the PSO-LSTM algorithm as a valuable tool for healthcare professionals in optimizing diabetes management and enhancing patient outcomes.
A Machine Learning Classification Paradigm for Automated Human Fall Detection
Authors: Sarthak Turki Et al.
Published: December 2023

For elderly people, falls are a severe worry since they can result in serious injuries, loss of independence, and deterioration of general health. In fact, among older persons, falls constitute the main reason for injury-related hospitalisations and fatalities. There is an obvious demand for fall detection systems that can help avoid or lessen the negative effects of falls given the enormous impact of falls on the senior population. Systems for detecting falls are created to notify carers or emergency services when a person has fallen, enabling quicker responses and better results. Elderly people who live alone or have mobility or balance impairments that make them more likely to fall may find these systems to be especially helpful.

The difficulty of categorising various actions as part of a system created to meet the demand for a wearable device to collect data for fall and near-fall analysis is addressed in this study. Three common activities—standing, walking, and lying down—four distinct fall trajectories—forward, backward, left, and right—as well as near-fall circumstances are recognised and detected.

Overall, fall detection systems play a significant role in the care of elderly people by lowering the chance of falls and its unfavourable effects. In order to better meet the demands of this vulnerable group, it's expected that as the older population grows, there will be a greater demand for fall detection systems and ongoing technological developments.
Round Robin based Arbitration Mechanism for Signaling Approach based Router Architecture
Authors: Jaya R. Suryavanshi Et al.
Published: December 2023

In Network-on-Chip the effectiveness of the network resource allocation is demonstrated by the flow control mechanism. There are two types of flow control mechanisms: buffered and bufferless. Compared to buffered flow control methods, buffer less flow control mechanisms are easier to use, need less power, and take up less space. When there are congestion and resource conflicts, it experiences higher packet loss and packet misrouting inside the network. A good buffered control mechanism useful as it overcomes the limitations of buffer less mechanism. There are numerous buffered and bufferless flow control methods available. In this paper, signaling-based Virtual Output Queue Router Arbiter Mechanism is used to explore credit-based flow control. This mechanism worked on new concept that is “stress value”. This information is generated in the form of credit whenever any input buffer has free space. Then, using this credit data, the node's stress value is determined. Free buffer space takes precedence over stress value if it is bigger. The stress value will increase if there is less available buffer space. To handle the congestion problem, the signaling block then sends this stress value to a neighboring router. To help the arbitrator make a more accurate decision, the crediting system constantly operates in tandem with arbitration.
Upset or Collapse Detection System for ASD Children Using Smart Watch with Machine Learning Algorithm
Authors: M.E.L. Subair Ali Et al.
Published: December 2023

ASD is characterised by severe and violent behavioural issues that are referred to as "meltdowns (upset) or tantrums (collapse)" and can include aggression, hyperactivity, intolerance, unpredictability and self-injury. This research work intends to develop and implement a non-invasive real-time Upset or Collapse Detection System (UCDS) for people with ASD. With a certain model of smart watch, the non-invasive biological indications such as Pulse Rate (PR), Skin Temperature (ST), and Galvanic Skin Reaction (GSR) can be artificially captured.  In order to create the UCDS, deep learning algorithms like CNN, LSTM, and the hybrid of CNN-LSTM are given the physiological signals that are captured to a server. The deep learning algorithm could recognise aberrant upset or collapse states from real-time physiological signs after being trained.  Deep learning algorithms including CNN, LSTM, and CNN-LSTM are used to train and test the proposed UCDS system, and it is discovered that hybrid CNN-LSTM beat them all with an average training and testing accuracy of 96% and a low mean absolute error (MAE) of 0.10 for training and 0.04 for testing.  Furthermore, the suggested UCDS system is supported by 93% of the ASD caretakers.
Cloud-based Near Real-Time Multiclass Interruption Recognition and Classification using Ensemble and Deep Learning
Authors: Minhaj Khan Et al.
Published: December 2023

Due to speedy development in internet facilities, detecting intrusions in a real-time cloud environment is challenging via traditional methods. In this case, advanced machine or deep learning methods can be efficiently used in anomaly or intrusion detection. Thus, the present study focuses on designing and developing the intrusion detection scheme using an ensemble learning-based random forest method and deep convolutional neural networks in a near real-time cloud atmosphere. The proposed models were tested on CSE-CICIDS2018 datasets in Python (Anaconda 3) environment. The proposed models achieved 97.73 and 99.91 per cent accuracy using random forest and deep convolutional neural networks, respectively. The developed models can be effectively utilised in real-time cloud datasets to detect intrusions.
Enhancing Alzheimer Disease Segmentation through Adaptively Regularized Weighted Kernel-Based Clustering
Authors: Suja G Et al.
Published: December 2023

Image segmentation is important in image analysis because it helps to locate objects and boundaries within a picture. This study offers Adaptively Regularized Weighted Kernel-Based Clustering (ARWKC), a unique segmentation technique built exclusively for recovering brain tissue from medical pictures. The proposed approach incorporates adaptive regularization and weighted kernel-based clustering techniques to increase the accuracy and resilience of brain tissue segmentation. The picture is initially preprocessed with the ARWKC method to improve its quality and eliminate any noise or artifacts. The adaptive regularization method is then utilized to effectively deal with the visual variation of brain tissue in clinical images. This adaptive regularization contributes to more accurate and consistent segmentation outcomes. The weighted kernel-based clustering method is then used to find and group pixels with comparable properties, with a focus on brain tissue areas. This clustering approach employs a weighted kernel function that takes into account both geographical closeness and pixel intensities, allowing the algorithm to capture local picture features and improve segmentation accuracy. Extensive experiments were conducted on a collection of medical images to evaluate the efficacy of the ARWKC algorithm. The well-known k-means clustering method, often used in image segmentation applications, was utilized as a benchmark for comparison. In terms of accuracy and resilience for brain tissue segmentation, the experimental findings showed that the ARWKC method surpasses the k-means clustering approach.
Investigating the Impact of Multiple Inputs on Predicting Glucose Level in Individuals with Type-1 Diabetes through Deep Learning Technique
Authors: Sunandha Rajagopal Et al.
Published: December 2023

Type-1 Diabetes is a chronic auto-immune disorder featured by the disability of human body to produce insulin, an enzyme helps to digest carbohydrates. Insulin works with the take-up of glucose from the circulatory system into cells. Without sufficient insulin, glucose aggregates in the blood, prompting hyperglycemia. Uncontrolled hyperglycemia, whenever left untreated, can bring about dangerous intricacies, which can harm the vital organs. Managing type-1 diabetes presents huge difficulties for impacted people, as it requires a sensitive equilibrium of insulin administration, sugar consumption, active work, and cautious observing of blood glucose levels. Despite the progress made in type-I diabetes management, challenges persist. Achieving optimal blood glucose control remains elusive for many individuals. Understanding the complex interplay between these variables and their impact on blood glucose regulation is vital for emerging more effective treatment strategies.

Accurate and timely prediction of glucose level plays a critical role in the effective management of diabetes. Through this paper we aim at investigating the impact of multiple inputs on forecasting of glucose level in individuals with type-1 diabetes by means of deep learning techniques.  In our paper, we use the Long-Short-Term-Memory (LSTM) network to study the forthcoming blood glucose pattern when various combinations if inputs, previous blood glucose reading, insulin administrated and carbohydrate intake are provided as inputs. We used the ShanghaiT1DM dataset for the study.
Pupil Position by an Improved Technique of YOLO Network for Eye Tracking Application
Authors: S. Mary Rexcy Asha Et al.
Published: December 2023

This Eye gaze following is the real-time collection of information about a person's eye movements and the direction of their look. Eye gaze trackers are devices that measure the locations of the pupils to detect and track changes in the direction of the user's gaze. There are numerous applications for analyzing eye movements, from psychological studies to human-computer interaction-based systems and interactive robotics controls. Real-time eye gaze monitoring requires an accurate and reliable iris center localization technique. Deep learning technology is used to construct a pupil tracking approach for wearable eye trackers in this study. This pupil tracking method uses deep-learning You Only Look Once (YOLO) model to accurately estimate and anticipate the pupil's central location under conditions of bright, natural light (visible to the naked eye). Testing pupil tracking performance with the upgraded YOLOv7 results in an accuracy rate of 98.50% and a precision rate close to 96.34% using PyTorch.
Improving Cache Hits On Replacment Blocks Using Weighted LRU-LFU Combinations
Authors: Wijaya Marvin Chandra
Published: December 2023

Block replacement refers to the process of selecting a block of data or a cache line to be evicted or replaced when a new block needs to be brought into a cache or a memory hierarchy. In computer systems, block replacement policies are used in caching mechanisms, such as in CPU caches or disk caches, to determine which blocks are evicted when the cache is full and new data needs to be fetched. The combination of LRU (Least Recently Used) and LFU (Least Frequently Used) in a weighted manner is known as the "LFU2" algorithm. LFU2 is an enhanced caching algorithm that aims to leverage the benefits of both LRU and LFU by considering both recency and frequency of item access. In LFU2, each item in the cache is associated with two counters: the usage counter and the recency counter. The usage counter tracks the frequency of item access, while the recency counter tracks the recency of item access. These counters are used to calculate a combined weight for each item in the cache. Based on the experimental results, the LRU-LFU combination method succeeded in increasing cache hits from 94.8% on LFU and 95.5% on LFU to 96.6%.
BSCSML: Design of an Efficient Bioinspired Security &Privacy Model for Cyber Physical System using Machine Learning
Authors: Megha Sanjay Wankhade Et al.
Published: December 2023

With the increasing prevalence of Smart Grid Cyber Physical Systems with Advanced Metering Infrastructure (SG CPS AMI), securing their internal components has become one of the paramount concerns. Traditional security mechanisms have proven to be insufficient in defending against sophisticated attacks. Bioinspired security and privacy models have emerged as promising solutions due to their stochastic solutions. This paper proposes a novel bio-inspired security and privacy model for SG CPS AMI that utilizes machine learning to strengthen their security levels. The proposed model is inspired by the hybrid Grey Wolf Teacher Learner based Optimizer (GWTLbO) Method’s ability to detect and respond to threats in real-time deployments. The GWTLbO Model also ensures higher privacy by selecting optimal methods between k-privacy, t-closeness & l-diversity depending upon contextual requirements. This study improves system accuracy and efficiency under diverse attacks using machine learning techniques. The method uses supervised learning to teach the model to recognize known attack trends and uncontrolled learning to spot unknown attacks. Our model was tested using real-time IoT device data samples. The model identified Zero-Day Attacks, Meter Bypass, Flash Image Manipulation, and Buffer-level attacks. The proposed model detects and responds to attacks with high accuracy and low false-positive rates. In real-time operations, the proposed model can handle huge volumes of data efficiently. The bioinspired security and privacy model secures CPS efficiently and is scalable for various cases. Machine learning techniques can improve the security and secrecy of these systems and revolutionize defense against different attacks.
A Structured Cloud-Based Software Testing Model with a Case Study Implementation
Authors: Santhosh S Et al.
Published: December 2023

Cloud-based testing methodologies were gaining significant popularity and adoption in the software testing industry. Cloud-based testing offers several advantages, such as scalability, flexibility, cost-effectiveness, and the ability to access a wide range of testing tools and environments without the need for extensive infrastructure setup. Cloud testing methods are having challenges with respect to testing priority, practical use cases, performance, lengthy test time, integrating and streamlining, data security, etc. since they are addressing specific purposes. To address these challenges, there is a need for a structured testing model with respect to the cloud environment. This article proposes a new structured cloud-based testing model for enhancing the testing service in the cloud environment. The proposed model addresses the order of testing and the priority, data security, and performance by using Smoke and Sanity testing methods.
Communication and Content Trust Aware Routing For Clustered IoT Network
Authors: Mohammad Osman Et al.
Published: December 2023

Security has become a major concern in practical applications related to Internet of Things, a Trust Aware Routing is found as second line of defence. To ensure a secure and hassle-free communication in IoT, this paper proposes a new routing strategy called as Communication and Content Trust Aware Routing (CCTAR) for Clustered IoT network. CCTAR is applied on a clustered IoT network in which the entire nodes are clustered into different clusters. Distance, initial energy, transmission range, angle of overlap and the sensing range are the fur major metrics used to cluster the network into hierarchical clusters followed by Cluster Head Selection. Next, the Trust Aware routing computes three different trust metrics namely Nobility rust, bilateral trust and Data oriented trust to determine the trustworthiness of Cluster Heads. The experimental evaluation of the proposed mechanism shows its superiority in terms of malicious nodes identification, Storage overhead reduction and Network lifetime improvisation.
Evaluation of Garbage Management Based on IoT
Authors: Naveen Raja S.M Et al.
Published: December 2023

Smart Waste Monitoring: To track the amount of waste in bins and containers, IOT-enabled garbage management systems use sensors and connected devices. These sensors can communicate real-time data to a centralized monitoring system and can identify the fill level. This data aids in streamlining waste collection routes, cutting back on pointless pickups, and enhancing garbage management effectiveness as a whole. Effective Resource Allocation: By giving precise data on waste generation patterns and trends, IOT-based garbage management systems enable optimal resource allocation. This information can be used by municipal authorities to make well-informed decisions on waste collection schedules, resource deployment, and staffing levels. IOT-based waste management solutions have the potential to make trash management procedures more effective and efficient while also being more affordable. The best garbage collection routes, operational cost reductions, and resource utilization may all be achieved with the aid of research into the best deployment strategies for IOT sensors and devices. Environmental Impact and Sustainability: Research Objective: Clearly identify the research objective, for example, by assessing how well IOT-based garbage management systems gather waste and allocate resources. Data gathering: Compile pertinent information on the methods used for trash generation, collection, and resource use. On-site observations, employee interviews, and database access for waste management operations are all effective ways to accomplish this. Gather information on IOT sensor technologies and their capabilities as well. Taken As alternative for Smart Waste Bins, Waste Level, Sensors, AI Recycling, Robots, E-Waste Kiosks. Taken for Evaluation preference is Reliability, Mobility, Service Continuity, User Convenience., and Energy Efficiency. Smart Waste Bins has performed more when compare to with other Real-Time Monitoring: The Internet of Things (IOT) can be used in waste management to enable real-time monitoring of trash cans or bins can be used to enhance garbage sorting procedures. Smart bins with cameras and sensors can automatically recognize and sort various types of rubbish. These smart bins can identify and categorise rubbish by utilizing IOT technology.  on their material composition or recycling category.
Reducing Bit Error Rate in WiMAX using Spread Spectrum Techniques in Comparison with OFDM based Modulations
Authors: Simardeep Kaur Et al.
Published: December 2023

This research investigated the Bit Error Rate (BER) performance of the WiMAX(Worldwide Interoperability for Microwave Access), a wireless system which uses IEEE 802.16 standard. For the implementation of OFDM model and analyzing the performance of WiMAX system using the Bit Error Rate (BER) and Signal to Noise Ratio (SNR) MATLAB software tool is used. We have used different adaptive modulation techniques such as QPSK, 16-QAM and 64-QAM and spread spectrum techniques such as FHSS (Frequency Hopping Spread Spectrum) and DSSS (Direct Sequence Spread Spectrum). In the current research the Bit Error Rate is improved by using spreading signals.  Spread spectrum enables a signal to be transmitted across a frequency band that is much wider than the minimum bandwidth required by the information signal.  The simulation results of this research show that FHSS and DSSS have given low BER at low SNR as compared to other modulation techniques.
A Unique Pipeline Model to Improve Anomaly Detection in High Dimensional Data
Authors: Upasana Gupta Et al.
Published: December 2023

This paper presents a comprehensive method for dimension reduction and detecting anomalies in high-dimensional data (on healthcare datasets) using R. Realizing that traditional linear methods such as Principal Component Analysis (PCA) often ignore the complexity of the non-linear manifold of the data, our approach exploits iterative learning, on the belief that high-dimensional data is largely based on a low-dimensional manifold. The methodology starts by preparing the data using R libraries like Keras, dplyr, and ggplot2, addressing challenges like missing values ??and visualizing meaningful information. Using the Mahalanobis distance, the paper identifies and removes country-specific outliers. The pipelined model integrates Principal Component Analysis (PCA) for data transformation and combines an Autoencoder with t-SNE for dimensionality reduction. This refined dataset is then used to train a Multi-Layer Perceptron (MLP) artificial neural network, which facilitates anomaly detection based on reconstruction errors, illustrated by the point cloud. Additionally, the paper explores metric multidimensional scaling using artificial neural networks, tests large datasets such as healthcare and wine, and compares the results of the work using conventional techniques. This study highlights the effectiveness of integrating various pre-processing, visualization, and artificial neural network strategies through R for effective anomaly detection.
Modelling of Whale Optimization Algorithm with Deep Learning for Skin Cancer Diagnosis on Dermoscopic Images
Authors: Vijay Arumugam R. Et al.
Published: December 2023

Skin cancer diagnoses through the analysis of dermoscopy images have undergone a revolutionary change with the incorporation of advanced Machine Learning (ML) approaches. By using contemporary models such as Convolutional Neural Network (CNN) and Deep Learning (DL) structures, this technique allows automated and accurate detection of malignant and benign skin tumours. This model learns intricate patterns and features directly from the image, which allows them to distinguish between different types of skin tumours based on colour, texture, and structural characteristics. This manuscript presents the Whale Optimization Algorithm with Deep Learning for Skin Cancer Diagnoses on Dermoscopy Images (WOADL-SCDDI) technique. The objective of the WOADL-SCDDI method is to analyze the Dermoscopic images for classifying and recognizing skin tumours. In the proposed WOADL-SCDDI technique, median filtering-based preprocessing and SegNet-based segmentation are involved. In addition, the WOADL-SCDDI technique offers a ResNet50 model for feature extraction with WOA-based hyperparameter tuning. Finally, Quasi Recurrent Neural Network (QRNN) model can be applied for skin tumour recognition and classification process. A comprehensive set of simulations have been conducted to highlight the better efficiency of the WOADL-SCDDI technique. The accomplished outcomes portrayed the improvements of the WOADL-SCDDI method compared to existing DL models.
Virtual Eye – Revolutionizing Vision Assistance For People With Disabilities
Authors: Venkatesh . S Et al.
Published: December 2023

Visually challenged individuals have faced numerous challenges in their daily lives. These challenges include: Visually challenged individuals have difficulty reading printed materials, including books, magazines, and newspapers. This limitation can significantly impact their education, as they may not have access to all the materials they need to learn. Moving around in unfamiliar places can be a daunting task for the visually impaired. They may struggle to access digital or printed materials, as these are often not available in accessible formats. It might be difficult for those who are blind to identify objects. This can be frustrating, especially in situations where they are alone and need to identify objects.

To address this issue, we are developing a mobile application for visually challenged individuals by providing a range of features such as text-to-speech, speech-to-text, image-to-audio, and PDF-to-audio. It enables visually challenged individuals to access information, read books, identify objects, communicate, and navigate with ease and independence. The app's user-friendly interface can be accessed both manually and by voice command, making it easy to use for people with varying levels of technical expertise. Overall, the Virtual Eye app helps visually challenged individuals lead more fulfilling and independent lives.

Overall, Virtual Eye application is an essential tool for visually challenged individuals, helping them navigate their daily lives with ease and independence. With this app, they can access information, communicate, and identify objects without the need for a third party, enhancing their quality of life and sense of autonomy.
Comprehensive Review on Automated Fruit Disease Detection at Early Stage
Authors: Nutan Dolzake Et al.
Published: December 2023

Fruits are now cultivated in many different countries, which has increased global fruit output to 2,914.27 thousand tons. Numerous countries want to increase their fruit production in the next years, thus the number of countries producing fruits is expected to keep growing. But despite this, a variety of challenges and problems are still experienced while growing crops. These include problems with the fruit's general quality, the cost of manufacturing, the state of the seed, and the fruit's own illness. The main causes of fruit diseases' detrimental impacts are microbes and fungus. Early fruit disease detection is used to foresee fruit disease, which helps farmers save money by lowering the amount of capital they have to spend. To stop fruit illnesses in their early stages, it is crucial to figure out the best way to identify fruit infections. Many studies on a variety of fruits, including the papaya, apple, mango, olive, kiwifruit, orange, etc., have employed deep learning approaches. This study compares several ways for image capture, pre-processing, and segmentation as well as deep learning techniques. The study discovered that the best deep learning strategy for a particular collection of data may change depending on the system's computational power and the data being used. The results of this study show that a convolution neural network is more accurate and can predict a wide range of fruit diseases.
The Impact of Project-Based Learning on High School Education—Based on Systematic Literature Review
Authors: Huang Manhua
Published: December 2023

Project-based learning (PBL) is a student-centered pedagogical approach that empowers students to actively engage in learning by integrating theoretical knowledge with practical application. It encourages students to synthesize their skills and knowledge to develop practical solutions for real-world problems. Currently, some scholars have begun to explore the impact of the implementation of PBL on high school education. Consequently, a substantial body of literature on PBL in high school education has emerged. A systematic literature review can systematically analyze the research status of the implementation of PBL in high schools, and comprehensively explore the core influencing factors and implementation effects of PBL. In this study, a systematic literature analysis method was employed to compile a total of 47 empirical journals published between 2019 and 2023, focused on the application of PBL in high school education. The analysis served to provide an overview of the research landscape surrounding PBL in high school education. This overview encompassed various aspects, including the number of article publications, sources of journals, research methodologies, academic disciplines, and key factors influencing the of PBL in high school education.

The results showed that the number of publications had been decreasing over the past five years. The analyzed journals predominantly focused on practical subjects like geography, science, chemistry, and physics, with the majority of studies originating from China. Research primarily employed mixed methodologies with sample sizes typically under 100 samples. The predominant research theme centered on STEM subjects. Key factors influencing successful PBL implementation included a relaxed and enjoyable learning environment, a collaborative learning approach, and engaging students through intriguing driving questions. Effectiveness in high school PBL was notably influenced by both students and teachers. In conclusion, this review offered recommendations for PBL in high schools, encompassing various grade levels, preferred databases, optimal search periods, and language considerations. It served as a valuable resource for future research and the development of PBL teaching models in high schools.
Performance of Machine Learning Models in Predicting Sentiments of Post-Covid Patients
Authors: S. Roja Et al.
Published: December 2023

With the widespread use of social media platforms, sentiment analysis of user-generated content has become a crucial task in understanding public opinion and trends. In this paper, we compare the performance of three popular machine learning models, namely Random Forest, Support Vector Machine (SVM), and Logistic Regression, in predicting sentiments of post-COVID patients on social media tweets. The study utilizes a dataset of labeled tweets representing positive, negative, and neutral sentiments. The preprocessing of textual data involves tokenization, stop-word removal, and conversion to lowercase to create a suitable input for the models. We utilize Term Frequency-Inverse Document Frequency (TF-IDF) vectorization to transform the text data into numerical features. The sentiment labels are converted to numeric representations for model training and evaluation. The three machine learning models are trained and evaluated on the dataset using metrics such as accuracy, precision, recall and F1-score. The evaluation results are presented and analyzed for each model, providing insights into their strengths and weaknesses in predicting sentiments. The experimental results demonstrate that Random Forest achieves the highest accuracy and F1-score, closely followed by SVM, while Logistic Regression performs slightly lower in comparison. However, all three models exhibit strong predictive capabilities, and their performances vary depending on the specific sentiment class. The findings provide valuable information for researchers and practitioners seeking to employ sentiment analysis in social media monitoring and other related applications. Overall, this study contributes to the understanding of the capabilities of Random Forest, SVM, and Logistic Regression models in sentiment analysis of social media tweets, and offers valuable insights for selecting the most suitable model for specific sentiment prediction tasks.
De-Noising of Apple Fruit Images using Dilated Convolution Neural Network model with Mixed Activation Function
Authors: C Radhiya Devi Et al.
Published: December 2023

In the agricultural sector, fruit diseases contribute to substantial economic losses, emphasizing the need for effective disease detection methods. Image processing techniques play a crucial role in minimizing damage and financial losses. Pre-processing, a vital stage in image processing, involves noise elimination and quality enhancement, laying the groundwork for subsequent tasks such as segmentation, classification, and disease detection. This study focuses on, proposing a novel approach for denoising diseased apple fruit images using the Dilated Convolution Neural Network model with Mixed Activation Function (DCNMAF). Performance evaluation, utilizing metrics like PSNR and SSIM, highlights the superiority of the proposed model over existing methods. These advancements underscore the effectiveness of the proposed DCNMAF model, showcasing notable enhancements in both accuracy metrics.
Phylogenetic Analysis of Blast Resistant RMG7 (Resistance to Magnaporthe Grisea 7) Gene in Cereals (Wheat and Rice)
Authors: Gonesh Chandra Saha Et al.
Published: December 2023

Understanding the emergence and evolution of plant pathogens has benefited greatly from phylogenetic approaches. Phylogenetic analysis of the Blast Resistant RMG7 (Resistance to Magnaporthe grisea 7) gene would involve the study of its evolutionary relationships with related genes across different species. This study aimed to understand the gene's origin, divergence, and evolutionary history, which can provide insights into its function and potential applications in crop improvement. The study obtained the total of 36 DNA sequences of the RMG7 gene from different organisms (wheat and rice) from publicly available NCBI(https://www.ncbi.nlm.nih.gov) database on 20 May 2023 to investigate its evolution. The Sequences were selected based on the representation of a diverse range of species, including those known to have blast resistance or related traits. Using the generic time reversible model and the maximum likelihood method, evolutionary history was determined. These methods used statistical algorithms to estimate the evolutionary relationships between the sequences, forming a tree that represents their common ancestry. An evolutionary tree evolved using the aligned RMG7 sequences to determine the common ancestor of each strain. The Geneious software, for which purpose (sequence alignment), which tool (such as muscle, geneious aligner, cluster) was used to carry sequence alignment, including parameters (in Geneious: local alignment or global, and at which identity value (65% identical) used in doing the alignment. The phylogenetic analysis showed that the resulting tree to interpret the evolutionary relationships among the RMG7 gene sequences. The Intensified clusters or branches that group sequences with similar blast resistance characteristics or closely related species. Overall, this research provides valuable insights into the evolutionary history, genetic diversity, and relatedness of the RMG7 gene in cereals, particularly in wheat and rice. The findings have implications for understanding the mechanisms of blast resistance and for enhancing crop protection strategies against Magnaporthe grisea.
Revolutionizing Healthcare: The Role of AI-Based Medical Expert Systems in Building a Better Future
Authors: Wani Yash , Gomashe Vinay , Kale Shyam , Sardeshpande Varad , Ugalmugale Pratik , Hingmire Amruta , Deshmukh Rushali A.
Published: December 2023

Modern society has an increasing need for better architecture and medical care. However, this difficulty is not sufficiently addressed by present medical architecture. The Medicinal Expert technique can be used to help persons in need in order to address this issue. A tremendous amount of medical data, including patient medical histories, records, and new medications, can be managed and maintained using this technology. It can help with decision-making and fill in for specialists when they are not present. The Medicinal Expert approach is a complex computer software system that generates forecasts using empirical data and expert knowledge. Based on the available training data and knowledge base, these systems function intelligently. Additionally, there are numerous Medical Expert System tools that support clinicians, help with diagnosis, and are crucial for instructing medical students. In this study, we introduce an AI-based Medical Expert System, its features, and its potential to help patients and medical students. We also go through some key findings from recent and prior research on expert systems, as well as how these systems can make the world a better place.
Sustainable Agriculture Practice using Machine Learning
Authors: Ashwini P. , Srividya K. , Vadivelan N.
Published: December 2023

The changing climate has caused unpredictable rainfall, unusual temperature drops, and heat waves, leading to considerable damage to the environment. Fortunately Machine Learning has provided effective tools to address global issues, including agriculture. By employing different ML algorithms, it is possible to solve the agricultural problems caused by these climate changes. The objective of this article is to develop a system for crop recommendation and disease detection in a plant. Publicly available datasets were used for both tasks. For the crop recommendation system, feature extraction was performed, and the dataset was trained using various Machine Learning algorithms, namely Decision Tree, Logistic Regression, Random Forest, Support Vector Machine (SVM) and Multilayer Perceptron. The random forest algorithm achieved an excellent accuracy of 99.31%.For the plant disease identification system, CNN architectures like - VGG16, ResNet50, and EfficientNetV2 - were trained and compared. Among these, EfficientNetV2 achieved high accuracy of 96.07%.
Hybrid Temporal Dynamics Feature Extraction in Recommendation Systems for Improved Ranking of Items
Authors: Awati Chetan J. , Shirgave Suresh K.
Published: December 2023

In today's retail landscape, shopping malls and e-commerce platforms employ various psychological tactics to influence customer behavior and increase profits. In line with these strategies, this paper introduces an innovative method for recognizing sentiment patterns, with a specific emphasis on the evolving temporal aspects of user interests within Recommendation Systems (RS). The projected method, called Temporal Dynamic Features based User Sentiment Pattern for Recommendation System (TDF-USPRS), aims to enhance the performance of RS by leveraging sentiment trends derived from a user's past preferences. TDF-USPRS utilizes a hybrid model combining Short Time Fourier Transform (STFT) and a layered architecture based on Bidirectional Long Short-Term Memory (BiLSTM) to retrieve temporal dynamics and discern a user's sentiment trend. Through an examination of a user's sequential history of item preferences, TDF-USPRS produces sentiment patterns to offer exceptionally pertinent recommendations, even in cases of sparse datasets. A variety of popular datasets, including as MovieLens, Amazon Rating Beauty, YOOCHOOSE, and CiaoDVD are utilised to assess the suggested technique. The TDF-USPRS model outperforms existing approaches, according to experimental data, resulting in recommendations with greater accuracy and relevance. Comparing the projected model to existing approaches, the projected model displays a 6.5% reduction in RMSE and a 4.5% gain in precision. Specifically, the model achieves an RMSE of 0.7623 and 0.996 on the MovieLens and CiaoDVD datasets, while attaining a precision score of 0.5963 and 0.165 on the YOOCHOOSE and Amazon datasets, respectively.
Empowering Smart Cities with Fog Computing: A Versatile Framework for Enhanced Healthcare Services and Beyond
Authors: Kumar Priyanka Rajan , Goel Sonia
Published: December 2023

Fog Computing represents a distributed computing infrastructure strategically positioned at the network's edge, acting as an intermediate layer between remote cloud services and the data-generating smart devices on the ground. Leveraging this concept, a flexible and efficient smart city design emerges, offering a diverse range of applications, including smart healthcare, car parking, power management, water management, and waste management. The implementation of Fog computing enables reduced data processing latency and equitable workload distribution across fog nodes. The smart city system comprises several layers, namely connection, real-time processing, neighborhood linking, main processing, and data server layers. The flexibility of this framework allows for the scaling up or down of layers depending on specific smart city applications. In a case study focused on Smart healthcare services, the iFogSim platform was utilized to evaluate the system's performance. Notably, the results demonstrated a significant reduction in network usage, data processing latency, and processing costs when compared to traditional cloud computing solutions. Consequently, this improvement in efficiency translated into an enhanced user experience, offering superior scalability and reliability to users utilizing smart city services, including healthcare facilities.
Development of Mobile Application-Based Assisttive Technology to Improve Slow Learner Students' Conjecturing Ability
Authors: Sutarto Et al.
Published: December 2023

Development of software for mobile applications has grown in significance in the current digital era. The goal of this project is to create mobile-based software that can enhance slow learners' conjecturing skills during the learning process. Rapid Application Development (RAD), a technique well-known for its iterative and collaborative approach, was utilized to create this program. Software development that is quick, flexible, and more user-centered is made possible by RAD. The steps of software development, such as user requirement analysis, application design, implementation, and testing, were discussed in this study. We also emphasize the importance of actively involving users in the development process to ensure that the software meets their needs and expectations. The results of software quality testing show that the software developed meets ISO-9126 standards. It covers important aspects such as reliability, efficiency and usability, earning Good criteria. Meanwhile, only the maintainability and portability aspects received sufficient criticality. It can be concluded that in general the quality of the software developed is good in accordance with ISO-9126.
Artificial Intelligence in Battle against Antimicrobial Resistance: Opportunities and Challenges
Authors: Debasish Swapnesh Kumar Nayak Et al.
Published: December 2023

Due to the overuse and abuse of antibiotics, antimicrobial resistance (AMR) poses a serious risk to socioeconomic development and public health. A paradigm shift is required to address this dilemma, and artificial intelligence (AI) appears as a possible remedy. AI, including machine learning (ML) and deep learning (DL), has demonstrated significant promise in several medical research fields, especially in the fight against AMR. Applications of AI in AMR use cutting-edge computational methods to analyze gene expression and whole-genome sequencing data, assisting in discovering infectious disease etiology and disease subtypes. These AI-driven systems have several advantages over more conventional ones, including less need for human involvement, more accuracy, and lower costs. However, they also encounter difficulties, such as inconsistent performance across datasets, with data volume critically influencing model efficacy. The accessibility and expense of high-throughput sequencing data, particularly next-generation sequencing data, also pose challenges to the wider application of AI models for AMR investigation. Despite these difficulties, AI has significant promise in the fight against AMR, and its advantages and disadvantages must be carefully considered in order to build successful tactics for dealing with this urgent worldwide problem. We assess research papers on AMR analysis using AI on various datasets and contrast the effectiveness of various AI models. We thoroughly reviewed the DL models used up to this point in the field of AMR, and we additionally discussed the challenges that come with deploying these approaches. This paper offers a thorough overview of AI's applications in AMR analysis, highlighting both its benefits and drawbacks.
Design and Analysis of a Buck-Boost Converter for a Photovoltaic Electric Vehicle System
Authors: Swati Shilaskar Et al.
Published: December 2023

The Electric Vehicle (EV) sector uses rechargeable batteries to power up their vehicles. However, these batteries are made from resources that are quickly depleting. To solve this issue the use of renewable energy sources is crucial. Along with that, systems that boost the existing voltage are necessary to save space and energy resources. This study proposes systems that will help power up the EV using Photo Voltaic (PV) solutions. Firstly, buck, boost and buck-boost converters are designed to fulfil the industry requirements of EV applications. The varying voltage (8V-18V) from the solar panel is converted to a steady voltage source (12V) using a buck-boost converter. This is used to charge the 12V LiFePo4 battery. Using a buck converter, the 12V source is converted to 5V to power the car’s dashboard. The boost converter is used to convert 120V to 420V which powers the car. The buck-boost converter uses a closed loop PI control system. This increases its efficiency significantly. The buck and boost converters are simulated and analyzed in LT Spice while the buck-boost converter is simulated using MATLAB/SIMULINK. The power efficiencies of the buck, boost and buck-boost converters are over 96%.
Use of Convolutional Neural Network and SVM Classifiers for Traffic Signals Detection
Authors: Snehal Chaudhary Et al.
Published: December 2023

Road signals are crucial for preserving a safe and effective flow of traffic. They give directions to cars, warn them of potential dangers, and notify them of the conditions of the road ahead. Road signs make roadways safer for both vehicles and pedestrians by regulating traffic and reducing accidents. Failure to obey traffic signals can be harmful and result in collisions. Drivers must always be conscious of their surroundings and pay attention to traffic signs. If a driver misses a signal, they should proceed with caution and safety to prevent injuring themselves or others, and they should seek assistance to reroute themselves.  Through the use of machine learning techniques, this project will create a traffic signal recognition system that will identify the traffic signals that are present on the road and inform the driver if the system determines that the motorist has missed a traffic signal or is thus violating traffic laws.
Towards Constructive Cost Analysis for demand based Reusable Domain Specific Components
Authors: N Md Jubair Basha Et al.
Published: December 2023

The prevailing software development methodology embraced by a majority of organisations is characterised by its agility. The neglect of normal analysis and design procedures is a consequence of the significant pressures associated with designing a product within specified time and budget constraints. This phenomenon could potentially result in a death of software of superior quality, while simultaneously impeding the constructive reuse of components. In the majority of component approaches, the demand of domain specific sofwatre components occurs during the later stages. In this paper, various components can be identified as demand based reusbale domain specific software components, which might also help in reusing these components in the subsequent increments. The strategy for extraction of components & procedure for reusing the existing components is described and a sample case to realize the same is presented.Still there is a dire need to early identify  the demand based domain specific sofwatre components  and perform the constructive cost analysis for the reusable  domain specific software components. The issues related to the estimation of cost reuse measures are still challenging. This paper presents the constructuve cost analysis for the demand based reusable domain specific sofwtare components and proposes reuse measures for the family of applications with the quantized values. By analyzing these cost measures, the budget and effort in the development can be reduced. The results are estimated from the HR Portal domain specific softwrae application as a case study and its respective scenario has been explored in a better manner.