bluuebunny/crossref_metadata_2025 huggingface dataset:
Artificial Intelligence for Meiosis and Mitosis Analysis
Authors: Mahto Rajendra Kumar
Published: April 2024

In cellular biology, meiosis and mitosis are essential processes that control cell division and replication as well as the transfer of genetic material. A thorough understanding of these intricate processes is essential for many fields, such as cancer research, genetics, and developmental biology. In this study, we suggest building a Mitosis and Meiosis Analysis System (MMAS) that uses artificial intelligence (AI) methods to make automated analysis and meiotic event characterization easier. The MMAS uses machine learning models, deep learning frameworks, and sophisticated image processing algorithms to precisely recognize and categorize various meiotic and mitotic stages from microscopy images. The MMAS seeks to increase the accuracy and efficiency of cellular biology research while streamlining the analysis process and minimizing manual labor by utilizing artificial intelligence. Furthermore, by providing insightful information about the dynamic character of mitotic and meiotic events, the MMAS helps scientists understand the underlying mechanisms and their implications for a range of physiological and pathological conditions. We hope to improve our knowledge of meiosis and mitosis and hasten research findings in cellular biology by putting the MMAS into practice.
Leveraging 5G Network Capabilities for Smart Grid Communication
Authors: Bhat Shaman
Published: April 2024

This paper presents a comprehensive investigation into the architecture and components of 5G networks, focusing on their suitability for smart grid applications. With the increasing complexity and demands of modern energy infrastructure, there is a growing need for reliable and low-latency communication systems to support critical smart grid operations. This paper explores key features of 5G networks, including network slicing, massive MIMO (Multiple Input Multiple Output), and low-latency communication protocols like URLLC (Ultra-Reliable Low Latency Communication), and examines how these features can be harnessed to address the unique requirements of smart grid environments. Through a detailed analysis of each component, this paper sheds light on the design considerations, deployment strategies, and performance implications of leveraging 5G technology for smart grid communication. Furthermore, practical insights, challenges, and future research areas are discussed to guide the development of next-generation smart grid solutions.
Exploring the Terrain: An Investigation into Deep Learning-Based Fusion Strategies for Integrating Infrared and Visible Imagery
Authors: Gayatri Phade Priyanka Bhatambarekar,
Published: April 2024

Infrared and visible image fusion technologies influence distinct image features acquired from distinct sensors, preserving complementary information from input images throughout the process of fusion, and utilizing redundant data to enhance the quality of the resulting fused image. Recently, deep learning methods (DL) have been employed by numerous researchers to investigate image fusion, revealing that the application of DL significantly enhances the efficiency of the model and the quality of fusion outcomes. Nevertheless, it is very important to note that DL can be implemented in various branches, and currently, a comprehensive investigation of deep learning-based methods in image fusion is in process.The paper aims to provide an exhaustive review of the evolution of image fusion algorithms grounded in deep learning over the years. Precisely, this paper undertakes a particular exploration of the fusion techniques applied to infrared and visible images through deep learning methodologies. The investigation includes a qualitative and quantitative comparison of extant fusion algorithms using established quality indicators, along with a thorough discussion of diverse fusion approaches. The current research status concerning infrared and visible image fusion is presented, with a forward-looking perspective on potential future directions. This research makes an effort to contribute valuable insights into various image fusion methods developed in recent years, thereby laying a solid foundation for subsequent research goings-on in this domain.
Fish Species Classification Using Deep Learning and Appearance-Based Feature Extraction
Authors: S. Albin Jose J.M. Jini Mol ,
Published: April 2024

As aquatic ecosystems face increasing threats and challenges, the accurate and efficient classification of fish species has become a critical task for fisheries management, biodiversity conservation, and environmental monitoring. This research explores the application of deep learning techniques in conjunction with appearance-based feature extraction for the automated and precise identification of fish species from images. The study employs a comprehensive dataset of underwater fish images, encompassing various species, sizes, and environmental conditions. Convolutional Neural Networks (CNNs) are used for feature extraction and classification, allowing for the capture of intricate visual characteristics that distinguish different fish species. Moreover, appearance-based features, such as colour patterns, fin shapes, and other morphological attributes, are integrated into the model to enhance classification accuracy. Additionally, the study introduces Improved Sequential Forward Selection (ISFS) as a method to select relevant features for classification, further enhancing the efficiency and effectiveness of the model. The research evaluates the performance of the proposed methodology against existing classification methods, emphasizing the advantages of deep learning and appearance-based feature extraction in terms of classification accuracy, robustness, and efficiency. In addition, the study explores the potential for real-world applications in fisheries management, species monitoring, and ecological research. The findings of this research demonstrate that the combination of deep learning and appearance-based feature extraction provides a promising approach for fish species classification, offering a valuable tool for conservation efforts and ecological studies. This research contributes to the growing body of knowledge in the field of computer vision and biodiversity conservation and opens up new possibilities for automated fish species identification in aquatic environments.
A Study of Irrigation Management in Smart Farming and IoT for Greenhouse Tomato Production
Authors: Awang Bono ,Roni Kastaman , Edy Suryadi , Yanti Rubiyanti Irfan Ardiansah
Published: April 2024

In this study of the relevant literature, we look at the development of smart farming and precision agriculture in greenhouse tomato cultivation, paying particular attention to the role that the Internet of Things (IoT) and automated irrigation systems have played thus far. Five terms were used to compile this literature review: automated watering systems, greenhouses, the Internet of Things, tomatoes, and smart farming. This article summarizes recent advancements in smart farming tech, their impact on greenhouse tomato farming, and their potential to boost future crop yield and quality. The evaluation starts by introducing 'smart farming' and its significance in agriculture, followed by a review of the greenhouse sector and an overview of the Internet of Things (IoT) within it, with subsequent examination of the pros and cons of employing smart farming and IoT in greenhouse tomato cultivation through specific industry technology examples. The following overview section discusses the benefits of cultivating tomatoes in a smart greenhouse, techniques for achieving optimal results in greenhouse tomato farming, and includes case studies to demonstrate the advantages of automated irrigation for smart greenhouse tomato cultivation, while this literature review analyzes the current state of innovative farming technology and its effect on greenhouse tomato cultivation, covering smart farming, greenhouse cultivation, the Internet of Things, tomato cultivation, and automated irrigation, summarizing significant results and recommendations for future research, including promising increases in crop yield and quality.
Enhancement of Cryptocurrency Mining through Artificial Intelligence for Workstation Systems
Authors: Bindu Babu , Sathish , Suriya , Abishek , Indraja Praveen Kumar
Published: April 2024

For workstation computers, it is highly recommended to use AI-powered bitcoin mining to maximise processing time and resource allocation. Conventional PC mining systems have a number of difficulties, such overheating, system availability algorithms, and controlling power consumption in relation to bitcoin production. The study has introduced an AI board called LSAI48266x in order to encounter the traditional PC mining systems. It uses intelligent algorithms to solve the problems existence with traditional PC based crypto mining. Even in the absence of human workers at the workstation PCs, LSAI48266x detects the best times to mine cryptocurrency by incorporating a variety of variables. The goal of the suggested solution is to minimise total electricity use while optimising CPU power utilisation. Using AI to increase computer resource efficiency during idle times is the major goal of the idea, which also aims to reduce excessive power consumption, preserve system dependability, and increase income.
Designing Power-Efficient BIST Architecture: Leveraging Reversible Logic for Scalable Digital Systems
Authors: Ramakrishna S , Rajashekhar B Shettar Suhas Shirol ,
Published: April 2024

This paper presents a novel approach to optimizing power usage in scalable Built-In Self-Test (BIST) controllers. While BIST mechanisms are crucial for maintaining the reliability of digital circuits, they can be excessively power-hungry during testing phases, particularly in applications where energy consumption is a concern. We propose an innovative architecture incorporating reversible logic gates and circuits to overcome this challenge. Reversible logic is renowned for its low power consumption as it retains information. By integrating reversible logic into our architecture, we can significantly reduce power usage during test cycles, making it an ideal solution for scalable systems ranging from 8 to 32 bits. Our trials showed substantial power savings compared to traditional BIST approaches without sacrificing test coverage or efficiency. Our research provides new opportunities to develop energy-efficient testing methods for digital circuits, contributing to broader efforts in sustainable electronics design.
Fraud Detection in Banking Data by Machine Learning Techniques
Authors: Nageswara Rao Moparthi Sahithi Arjun ,
Published: April 2024

An important part of the study is looking at how to use machine learning to find scams in bank data. This is a very big problem in the banking world, where finding and stopping scams is very important. The study adds class weight-tuning hyper parameters to make scam detection better. These factors make it easier for the model to tell the difference between real and fake transactions, which makes the scam detection system more accurate. Cat Boost, Light GBM, and XG Boost are three well-known machine learning methods that are used in a smart way in this work. Each program is good at something different, and using them together should make the scam detection method work better overall.To fine-tune the hyper parameters, deep learning methods are used in the work. This combination improves the fraud detection system's speed and ability to change, making it better at finding new fraud schemes. Real-world data are used in the project to do thorough reviews. These tests show that using Light GBM and XG Boost together works better than other ways when looking at different factors. By this measure, the suggested method is better at finding fake actions than other approaches. It has a Stacking Classifier that combines results from the Random Forest and Light GBM classifiers with certain settings. Using a Gradient Boosting Classifier as the final predictor, this ensemble method improves the accuracy of predictions by using the best features of different models.
Frontiers of Computing - Evolutionary Trends and Cutting-Edge Technologies in Computer Science and Next Generation Application
Authors: Samir N. Ajani,
Published: April 2024

The cutting edge of computing is always changing because people are always looking for new ways to do things and combining cutting-edge tools. There are a number of natural trends that will affect the next generation of apps. Quantum computing is one of the most important trends. It is a new way of doing things that uses the rules of quantum physics to do calculations at speeds that are impossible with regular computers. Quantum computing could help solve hard issues in security, optimization, and modeling, which would lead to new science discoveries and technological progress.Neuromorphic computing, which is based on the structure of the human brain, is another new realm. The goal of this revolutionary way of computing is to create artificial neural networks that can learn, change, and process information in ways that are similar to how biological systems do it. There is a lot of promise for neuromorphic computing to improve machine learning and make AI apps more efficient and complex.The world of computing is also changing because artificial intelligence (AI) and edge computing are coming together. Edge computing brings computing power closer to the data source, which lowers delay and improves real-time processing. This is why it is so important for the growth of technologies like self-driving cars, smart cities, and the Internet of Things (IoT).Adding blockchain technology is another step forward that will make sure deals in open systems are safe and clear. Blockchain is used for more than just cryptocurrency. It has an impact on supply chain management, healthcare, and banking, among other areas. The quantum computing, neuromorphic computing, the combination of AI and edge computing, and the use of blockchain technology are all on the cutting edge of computing. Together, these trends bring computer science into a new era and lay the groundwork for groundbreaking uses that will shape the future of technology.
Efficient Frontier Rules for Secure NFC Payments and Data Transmission
Authors: Sandip Shinde,
Published: April 2024

As a result of advancements in technology areas including mobile communication, semiconductors, and 5G internet, things have changed very quickly.Near field communication [NFC] has gained popularity due simplicity in  use and function.  Given that the weaknesses in the current technique were discovered, this paper  proposed a new protocol for more secure NFC payments and data transfer. And thus conducted a thorough analysis of NFC payments and discovered that the data carried over the air was weak and easily intercepted by using tools like as Pflipper zero, Hackrf-one, and RTL-SRD, and later could be decoded utilising cutting-edge computations and GNU. This suggested protocol entails local-level verification for the creation of a transmission channel between a card and a Point of Sale [POS] at the local-level, allowing a secured point-to-point connection between the two end points. In addition to the first step indicated, the channel will make sure that no data is being eavesdropped by an unauthorised devices or antenna. If so, both the channel and any data that was transmitted over it are immediately destroyed. This protocol would offer more security declarations and aid by making payments secured, safe, and morally correct.
AI-Enhanced Digital Forensics: Automated Techniques for Efficient Investigation and Evidence Collection
Authors: Anushka Deepak Kadage
Published: April 2024

The abstract summarizes AI-enhanced digital forensics topics. It highlights the importance of AI in digital forensic investigations and outlines its major features, historical perspectives, and methodological evolution. The abstract describes how automated methods can streamline evidence collection and investigation. The historical perspective highlights digital forensic procedures from rudimentary file system investigations to AI-driven methods. This progression reflects digital crime's dynamic character and forensic method developments. The AI-enhanced digital forensics methodology includes establishing an effective component model, identifying datasets, gathering data, arranging studies, and considering ethical considerations. Representative datasets and ethical considerations are stressed in the abstract to ensure ethical and responsible AI application in forensic investigations. AI-based systems are evaluated using accuracy, false positive/negative rates, speed and efficiency, scalability, and durability. A straightforward comparison of these parameters across AI algorithms using bar graphs and grouped bar charts helps forensic investigators chooses strategies. In conclusion, AI-enhanced digital forensics is well understood, and performance evaluations, methodological concerns, historical evolution, and ethics are important. AI is being used in digital forensics as technology advances, giving investigators a strong tool to navigate the digital world accurately and efficiently. To use AI responsibly and effectively for justice, technique and ethics must be constantly improved
Predictive Identification of Malignant Melanoma from Fractional Differentiation-based Image Enhancement
Authors: Vijay U. Rathod
Published: April 2024

Recent advancements in computer vision and artificial intelligence allow for the automatic detection of some abnormalities in medical photographs. One of these is a skin lesion, and prompt and accurate detection of these conditions substantially aids in treatment. When image processing is combined with the fundamental edge detection technique, there has been potential demonstrated in the automatic identification and delineation of boundaries inside skin lesions. In this work, we investigate how to improve edge-detection in photos using fractional differentiation. We present a fractional differential filter based method for edge detection in images of skin lesions. The original photos are then improved with the help of the derived images. During acquisition, acquired images are classified using deep learning models. In the experiments, a well-researched dataset of HAM10000 is employed. The outcomes demonstrate how well these filters work to cut through noise and pick up minute edge characteristics that can provide useful information while performing recognition tasks.
Enhancing Security and Reliability in Industrial IoT Networks through Machine Learning
Authors: Praful V. Barekar,
Published: April 2024

Industrial Internet of Things (IIoT) networks are very important to modern manufacturing because they allow processes to be monitored, controlled, and improved in real time. IoT systems are linked to each other, which makes them vulnerable to cyberattacks, system breakdowns, and communication problems. This makes them less reliable and secure. To deal with these problems, we need advanced technologies that can find problems before they happen, reduce risks, and keep processes running smoothly. In this paper, we suggest a new way to make IIoT networks safer and more reliable by using machine learning methods together. Our system uses machine learning techniques to look at network traffic trends, spot strange behaviors, and find possible security holes in real time, using the huge amounts of data that IIoT devices produce. Our system can successfully find and stop a wide range of cyberattacks, such as hacking attempts, malware infections, and denial-of-service (DoS) attacks, because it is always learning from past data and changing to new threats. We also use machine learning models to predict when systems might fail or perform worse, so we can do preventative maintenance and keep downtime to a minimum.To prove that our method worked, we did a lot of tests using real-world IIoT datasets and checked how well our system worked by looking at how accurate it was at finding things, how often it gave false positives, and how fast it responded. These results show that our approach based on machine learning makes IIoT networks much safer and more reliable than standard rule-based approaches. In addition, our framework is strong against new and unknown threats, which shows that it could be used in a wide range of business settings.Overall, the paper research shows that machine learning has a lot of potential to make IIoT networks more reliable and to make sure that processes in industrial settings run smoothly and safely
Smart IoT-enabled Healthcare Systems: Real-time Anomaly Detection and Decision Support using Deep Learning Models
Authors: Sherif Tawfik Amin,
Published: April 2024

Smart healthcare systems that use Internet of Things (IoT) technologies are changing the medical field by letting data about patients be monitored and analyzed in real time. This paper suggests a new way to improve these kinds of systems by adding deep learning models to help find problems and make decisions.IoT devices are used in the suggested system to gather real-time information about things like vital signs, patient behavior, and surrounding factors. Deep learning algorithms, especially convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are then used to handle this data and look for changes in the patient's health that aren't normal. These strange things could be signs of possible health problems or accidents, which would require quick action.Deep learning models are also trained on big datasets to find trends and connections in the data. This lets them help healthcare workers make decisions. For instance, the system can figure out how likely it is that a patient will get a certain illness by looking at their present health and their medical background.One of the best things about this method is that it can change and get better over time. More information is put into the models over time, making them more accurate and good at finding problems and giving useful information. This makes the method very useful for keeping an eye on people with long-term illnesses or finding diseases early. In adding deep learning models to healthcare systems that are connected to the internet of things (IoT) is a useful way to make patient care better. These tools could save lives and improve health by finding problems and helping people make decisions in real time.
Effect of Supervised Sense Disambiguation Model Using Machine Learning Technique and Word Embedding in Word Sense Disambiguation
Authors: Rupesh Mahajan,
Published: April 2024

Natural language processing includes a subfield called word sense disambiguation, which focuses mostly on words that might have several meanings. Polysemous terms are also referred to as confusing phrases in some circles. The performance of word sense disambiguation depends on how effectively the ambiguous word is recognized by the machine. The discussed word embedding model for the ambiguous words represents the words from the document space to vector space with no data loss. The most identified challenge of ambiguous word representation is the features. The selection and representation of ambiguous words with respect to the features is the tedious task of word embedding. The discussed word embedding model uses countable features of available context for disambiguation. The proposed model is implemented for ambiguous words with context information. The available context of ambiguous/polysemous words is used for disambiguation. The unavailability of the context is the challenge in this model. The Recurrent Neural Network with Large Small Term Memory is used for the classification. The output of the RNN-LSTM is the sense values which are further mapped with the freely available lexical resource WordNet for retrieving the correct sense(meaning).
Breast Tumour Segmentation Using Advanced UNet with Saliency, Channel, and Spatial Attention Models
Authors: Vaishali Dasharath Shinde,
Published: April 2024

Cancers are getting pretty common these days and in that the second most common cancer in the world after lung cancer is breast cancer. The primary screening techniques for early diagnosis of cancerous nodules in women breast are Ultrasound, Mammography, and MRI analysis. However, accurately identifying and outlining the boundaries of tumor regions remains challenging due to their unpredictable shapes, random variations, and blurry outlines. Currently, the CAD based image analysis systems have gained huge attention in biomedical image processing systems along with Machine Learning (ML) and Deep Learning (DL) methods. The conventional machine learning methods suffer from accuracy related issues therefore deep learning-based schemes have been adopted. Specifically, UNet based approach is widely employed for biomedical image segmentation. This research focuses on development of a UNet based deep learning architecture for breast cancer image analysis and segmentation. The proposed model improves the segmentation accuracy by adding saliency model, channel and spatial attention model to consider the low- and high-level features. These modules help to achieve the clear boundary during segmentation. The performance of proposed model is evaluated by using publicly available mammogram dataset known as DDSM dataset.
Enhancing Rice Crop Resilience: Leveraging Image Processing Techniques in Deep Learning Models to Predict Salinity Stress of Rice during the Seedling Stage
Authors: Geeta Bharamagoudar Sharada K Shiragudikar ,
Published: April 2024

One of the most significant staple crops in the world is rice. Rice seedlings are particularly susceptible to salt stress during the seedling stage, which can negatively affect crop quality and yield. Traditional approaches for assessing the susceptibility of rice crops to salt stress during the seedling stage are deemed inadequate and time consuming. The study emphasizes the necessity of employing a deep learning model instead of traditional methods to identify and classify salinity stress in rice seedlings using field images. To predict salinity stress in rice crops, this research examines the significance of image processing methods employed in deep learning models. To enhance the clarity and visual representation of salinity-induced stress symptoms, we explore several image enhancement techniques, such as noise reduction, contrast augmentation, and image normalization. To further capture and quantify the distinct visual features related to salinity stress, feature extraction techniques such as texture analysis, shape analysis, and color-based segmentation are used. We employ a deep learning model such as VGG16 and VGG19 models to use these extracted features as input to effectively classify the severity of salinity stress in rice seedlings as 1,3,5,7,9 scores. A comprehensive set of rice seedling images from field taken under various salinity stress conditions is used to assess the suggested method. The effectiveness of image processing techniques in improving the discriminatory power of deep learning models for salinity stress prediction is demonstrated by experimental results with 99.40%. The combination of image enhancement and feature extraction methods significantly improves the overall accuracy and reliability of the predictions, enabling farmers to make informed decisions regarding crop management and potential interventions to mitigate salinity stress.
“Hybrid MAC Methodology for Improving the Qos in Fiber Wireless Network”
Authors: Hardeep Singh Saini Prabhjot Kaur ,
Published: April 2024

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

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

To achieve this goal, the study employs techniques that involve and Utilizing an Orthogonal Frequency Division Multiplexing (OFDM) modulator, a free-space optical (FSO) communication channel, an OFDM demodulator, and Opti-system for the analysis and enhancement of received signals, our study demonstrates that the proposed MAC protocol surpasses conventional MAC protocols in terms of delay, data throughput, and transmission efficiency.
Gaussian-Based Dilated 1-D CNN for Classifying B-Cell Epitopes in Zika and Dengue Protein Sequences
Authors: Jyoti Yogesh Deshmukh
Published: April 2024

The main goal of designing peptide vaccines, conducting immunodiagnosis, and producing antibodies is to accurately identify linear B-cell epitopes. However, experimental analysis to determine these epitopes is costly. This study focuses on developing a Gaussian-based dilated 1-D CNN model for classifying epitopes and non-epitopes in protein sequences related to Zika and Dengue viruses. The Immune Epitope Database (IEDB) was used, containing a total of 1741 and 7020 linear B-cell epitopes for Zika and Dengue viruses, respectively. Physicochemical features of the protein sequences dataset were extracted using the Gaussian distribution to extract optimal features based on feature probability distribution. The proposed model achieved an accuracy score of 83.00% and 85.00%, precision of 87.00%, recall of 83.00% and 85.00%, and an F1-score of 84.00% and 86.00% over the Zika and Dengue datasets. The suggested model outperforms existing methods, demonstrating the potential of deep learning approaches in bioinformatics for enhancing epitope prediction in viruses, with implications for drug discovery and vaccine development.
The Role of detection Algorithm in Tracking Techniques with the help of Deep Learning for an Enhanced user Experience
Authors: Pallavi Parlewar
Published: April 2024

The drum set is a prominent instrument in a variety of music genres including pop, rock, and jazz. However, it costs money and space to get from nothing to purchasing a kit. The objective of these studies is to enable drummers to hone their skills, at least blithely, without a full set of drums and to expedite the initiation of drummers to their drumming experiences without incurring any costs. Drums do not fit inside a single bag, making them more difficult to travel than other instruments. We propose a virtual drum prototype that enables air-drumming using just two sticks, a computer equipped with a camera, and readily available markers that resemble the drumstick tips, such colourful papers. The detection algorithm combines tracking techniques with deep learning technology for an enhanced user experience. This was done using the Python-based OpenCV, as well as the concept of color-based blob recognition was used to find the markers. The capacity to function as a USB controller and MIDI controllers as well as the possibility for further development as a released program have all been shown by this prototype. Results from experiments show how useful and successful this method is for producing a lifelike and immersive virtual drumming experience.
Cervical Cancer Prediction Using Machine Learning
Authors: Rashmi Ashtagi
Published: April 2024

Cervical cancer constitutes a profound global health challenge, affecting a substantial number of women annually. If this cancer is not diagnosed and attended with hypervigilance, it can spread to other body parts, damage tissues, and typically deteriorate the immune system of the body. This ultimately becomes deadly and, in many cases, non-curable. This research paper looks into machine learning used to predict cervical cancer. The 5-year relative survival rate after the spread of the disease is almost 50%. Hence, detecting the tumor in advance can prevent its proliferation and consequently assist in the process of curing the disease. 

The study emphasizes the global impact of cervical cancer, shedding light on its prevalence and its connection to the human papillomavirus (HPV). In a managerial but approachable manner, this research discusses the factors like weakened immune systems, smoking, and contraceptive use that contribute to the risk of cervical cancer. Four essential diagnostic tests—Hinselmann, Schiller, Citology, and Biopsy—are discussed as integral components of the predictive model. Diverse Machine Learning algorithms used show promise in enhancing accuracy for early cervical cancer prediction. With potential implications for public health, this paper concludes by providing insights into future research directions.
Empowering IoT Healthcare Systems with Deep Learning: From Sensor Data Fusion to Predictive Modeling and Intervention
Authors: Minal Shahakar
Published: April 2024

Adding Internet of Things (IoT) technology to healthcare systems has changed the way patients are cared for by letting them be monitored and data collected in real time. This essay looks at how deep learning can be used to improve IoT-enabled healthcare systems, with a focus on combining sensor data, making predictions, and coming up with ways to help.Sensor data fusion is a key part of putting together data from different sources, like medical equipment, smart tech, and electronic health records. Deep learning algorithms, especially CNN and RNN are very good at handling different types of data streams. This makes it possible to get a full picture of a patient's health. Healthcare professionals can get a full picture of a patient's health by combining information from many sources, such as bodily signs, exercise levels, and outdoor factors. Based on past data, predictive modeling uses the power of deep learning to guess what will happen with people's health in the future. IoT healthcare systems can predict how a disease will get worse, find risk factors, and suggest early treatment using methods like long short-term memory (LSTM) networks and attention mechanisms. These prediction models allow for quick treatments, methods for preventive care, and the best use of resources, which improves patient results and lowers healthcare costs in the long run.Deep learning also makes it easier to come up with smart management methods that are specific to each patient's needs. Machine learning algorithms can make personalized treatment suggestions and adaptable care plans by looking at real-time monitor data along with old patient records. These treatments could include changes to medications or lifestyles, or tips for medical workers. These give patients and healthcare staff more information to help them make better choices and better handle chronic conditions. When IoT technology and deep learning are combined, they have the ability to completely change the way healthcare is provided. IoT-enabled healthcare systems can improve patient tracking, analysis, and treatment by using advanced algorithms for sensor data fusion, predictive models, and smart actions. This leads to better quality of care and better health results.
Integration of Wearable Sensors and Electrical Muscle Stimulation in Lower Limb Robotics for Gait Rehabilitation
Authors: Mandar Malawade
Published: April 2024

Combining wearing devices with electrical muscle stimulation (EMS) in robotic lower limbs has shown promise in helping people learn how to walk again. This new method blends the benefits of smart technology and EMS to help people with neurological or joint problems improve their motor function and walking patterns. Wearable devices, like inertial measurement units (IMUs) and electromyography (EMG) monitors, record information about how the body moves and what muscles are doing while the person moves. These monitors give the user and the therapy team feedback in real time, which makes it possible for personalized and flexible recovery plans. Data from personal monitors can also be used to look at how people walk and see how they're doing over time. EMS is used to target certain muscles and make them stronger, more coordinated, and easier to control. By combining EMS with robots for the lower limbs, therapists can focus on certain muscle groups and make recovery routines more successful. EMS can also be timed to move with the robotic suit to help the person walk in a more natural and useful way. Wearable sensors and EMS work well together in lower limb robots for many reasons. EMS therapy can be made more specific and flexible because wearing monitors let doctors change the frequency and strength of the therapy in real time. The person is also encouraged to be more involved with this method because they get instant feedback on their actions and can see how they're doing over time. The combining wearing sensors and EMS in lower limb robots has a lot of promise to make gait therapy work better. Future study should focus on finding the best ways to combine these tools and looking at how they affect the quality of life and ability to walk for people who have movement problems in the long run.
Enhancing Neurorehabilitation through Closed-Loop Control of Robotic Exoskeletons and Brain-Computer Interfaces
Authors: T. Poovishnu Devi
Published: April 2024

Neurorehabilitation is an important part of getting better for people who have nerve illnesses or accidents. Brain-computer interfaces (BCIs) and robotic exoskeletons have shown promise in better recovery results by allowing focused therapy and making neurons more flexible. This paper gives an outline of the current study on closed-loop control systems that combine robotic exoskeletons and brain-computer interfaces (BCIs) for neurorehabilitation. It also talks about the pros and cons of these systems. Closed-loop control systems try to make two-way communication possible between the user's brain activity (measured by BCIs) and the robotic suit. This way, treatments can be changed in real time based on the user's neurological signs. This method makes it possible for more personalized and flexible therapy plans, which can help people recover their movement skills and become more useful. When BCIs are combined with artificial exoskeletons, they offer many benefits, such as exact control over movement parameters, increased involvement and motivation through immersive feedback, and the chance to induce learning by coordinating brain activity and physical output. To make these systems work better, though, problems like signal processing delay, calibration issues, and the need for user training must be fixed. Recent research has shown that closed-loop control systems can be used and might be helpful in a number of neurorehabilitation situations, such as helping people who have had a stroke or spinal cord injury get their movement skills back and helping people with neurological conditions regain their independence. In the future, researchers should work on improving these systems' algorithms and hardware, running large-scale clinical studies to show that they work, and finding new ways to improve neurorehabilitation results by combining modern technology.
Electrical Impedance Tomography for Real-Time Monitoring of Muscle Activity in Robot-Assisted Rehabilitation
Authors: Smita Patil
Published: April 2024

This work looks at how Electrical Impedance Tomography (EIT) can be used in robot-assisted rehabilitation to make it easier to watch how muscle activity changes in real time. Quantitative methods are used in this study to look into how well EIT works, including full analyses and comparisons with common tracking methods like Electromyography (EMG). Both EIT and EMG are used to record the participants' mean and peak muscle activation levels as they do a set of Upper Limb, Lower Limb, and Full Body exercises. The study's findings show that EIT is incredibly flexible when it comes to capturing complex patterns of muscle activation across a wide range of rehabilitation routines. Each participant's involvement is shown by different patterns of muscle activation, and these patterns can be seen across different types of exercise. We can learn a lot about how muscles work during rehabilitation by measuring their mean and peak activation levels. This helps us see how well EIT works as a tracking tool. A very important step in validating EIT is to compare it to EMG readings. This shows how consistent and accurate EIT is in real-time monitoring situations. The high level of agreement between the two methods shows that EIT is a reliable alternative to standard monitoring methods, providing an easy to use and non-invasive way to measure muscle activity. The results of this study are even stronger because they include quantitative analyses and a detailed look at system performance data. We carefully check things like signal-to-noise ratio, reconstruction accuracy, and reliability, which shows how strong and dependable the combined EIT system is. These metrics are important for showing how well the system can measure things accurately and reliably in changing rehabilitation situations. This study adds to the growing amount of research on EIT uses in rehabilitation by showing that it can be a useful tool for keeping track of real-time muscle activity. This study opens the door for EIT to be widely used in rehabilitation by showing how muscle activation changes over time and proving its effectiveness against well-known methods. This will eventually lead to better patient outcomes and more effective rehabilitation.
Robotic-Assisted Ankle Rehabilitation Utilizing Electrical Stimulation and Virtual Reality Training Paradigms
Authors: Sandeep Shinde
Published: April 2024

This study investigates the ankle rehabilitation systems in great detail, including technical features, clinical considerations, patient-related factors, and economic factors. Using a parameterized method, different parameters were tested to find out how well, efficiently, and easily these systems could be used. It was looked at how technological features like robotic configuration, sensory feedback, and control methods can be used to make rehabilitation more personalized. To make sure the best results for patients, clinical considerations focused on practices based on evidence, safety features, and integration with clinical workflows. To look at the human-centered parts of ankle rehabilitation, things like user experience, adherence, and result measures were looked at. To find out if putting ankle rehabilitation systems into healthcare situations would be financially viable, economic factors like cost-effectiveness, reimbursement, and return on investment were looked at. Numbers were added to give quantitative information about each parameter, which made it easier to do a thorough review of ankle rehabilitation systems. Overall, this study gives important information to doctors, hospital managers, and others involved in improving the outcomes of ankle-related patients by choosing the best rehabilitation programs and making sure they are carried out properly.
Crush Analysis of External Inversion of Tubes with Variable Thickness Distribution
Authors: Keivan Hosseini Safari, Mohammad Mahdi Kheirikhah Vahid Yousefi Ramandi, Mahmoud Mosavi Mashhadi
Published: April 2024

Axial crush tests on Uniform Thickness (UT) and Functionally Graded Thickness (FGT) tubes have been performed for external inversion process, and crashworthiness performance, and benefits of FGT tubes compared with their UT equivalents. Furthermore, in order to find detail verification about crush process of FGT tubes, Finite element (FE) models developed using explicit FE code under axial loading. Then, these FE models validated by experimental tests to ensure that they can accurately predict the responses of FGT inversion tubes. Our study shows that tube thickness distribution, die radius, and coefficient of friction between die and tube have great influence on the responses of FGT tubes inversion.  The results give new design ideas to improve crashworthiness performance of inversion tubes.
DOI: https://doi.org/10.52783/pst.272
Long-term assessment of collagenase treatment for Dupuytren’s contracture: A 10-year follow-up study
Authors: Passiatore Marco , Cilli Vitale , Cannella Adriano , Caruso Ludovico , Sassara Giulia Maria , Taccardo Giuseppe , De Vitis Rocco
Published: April 2024

BACKGROUND

Enzymatic fasciotomy with collagenase clostridium histolyticum (CCH) has revolutionized the treatment for Dupuytren’s contracture (DC). Despite its benefits, the long-term outcomes remain unclear. This study presented a comprehensive 10-year follow-up assessment of the enduring effects of CCH on patients with DC.

AIM

To compare the short-term (12 wk) and long-term (10 years) outcomes on CCH treatment in patients with DC.

METHODS

A cohort of 45 patients was treated with CCH at the metacarpophalangeal (MCP) joint and the proximal interphalangeal (PIP) joint and underwent systematic re-evaluation. The study adhered to multicenter trial protocols, and assessments were conducted at 12 wk, 7 years, and 10 years post-surgery.

RESULTS

Thirty-seven patients completed the 10-year follow-up. At 10 years, patients treated at the PIP joint exhibited a 100% recurrence. However, patients treated at the MCP joint only showed a 50% recurrence. Patient satisfaction varied, with a lower satisfaction reported in PIP joint cases. Recurrence exceeding 20 degrees on the total passive extension deficit was observed, indicating a challenge for sustained efficacy. Significant differences were noted between outcomes at the 7-year and 10-year intervals.

CONCLUSION

CCH demonstrated sustained efficacy when applied to the MCP joint. However, caution is warranted for CCH treatment at the PIP joint due to a high level of recurrence and low patient satisfaction. Re-intervention is needed within a decade of treatment.
Crisi celiaca: una complicanza rara e insidiosa della celiachia
Authors: Barca Maria , Boccia Maria , Improta Marta , Discepolo Valentina
Published: April 2024

Coeliac Crisis is an urgent and potentially fatal complication of Coeliac Disease (CeD), which is most frequent in early childhood. The crisis may occur at onset or develop in CeD patients with poor compliance to the gluten-free diet (GFD). Stress, surgery and infections can act as triggers. The diagnosis is based on the acute onset of rapidly progressive diarrhoea associated with at least two conditions among severe dehydration, metabolic acidosis, dyselectrolytemia, renal dysfunction, neurological alterations, hypoproteinemia and weight loss. Haematological manifestations, such as thrombocytopenia, increased INR, aplastic anaemia and leukopenia, are rare. The cornerstones of therapy are intravenous rehydration, correction of hypoalbuminemia, acidosis and dyshionemia along with a strict GFD. Steroids may be also used in refractory cases. The paper describes a recent case of a patient presenting with a severe life-threatening coeliac crisis characterized by inability to walk due to lower limb weakness, severe abdominal distension, diarrhoea and peripheral oedema.