bluuebunny/crossref_metadata_2025 huggingface dataset:
Transforming Biologics Delivery through Microneedles
Authors: Goswami Amit Kumar , Radha Gadela V. , Venuganti Venkata Vamsi Krishna
Published: October 2024

This review explores the potential of microneedles (MNs) in enhancing the delivery
of biologics vital for treating conditions, including infectious diseases, cancer, and autoimmune
disorders. The COVID-19 pandemic has amplified the demand for biologics, prompting research
and development. The global biologics market is expected to grow substantially due to the rise
of personalized medicine. Large, complex molecules, including proteins, peptides, and vaccines,
are known as biologics, and a potential technique for their delivery is microneedles. MNs come
in various forms: solid, hollow, coated, dissolvable, and hydrogel MNs. Traditional drug delivery
methods have limitations, while transdermal drug delivery via Microneedles offers a promising
alternative. Microneedles painlessly penetrate the skin's barrier, forming temporary microchannels
for effective medication administration. This minimally invasive, self-administered
technique increases patient comfort and compliance and eliminates the complications of oral
medications and injections, indicating a bright future for biologic drug administration. Microneedles
hold the promise to reshape healthcare delivery by facilitating broader access to vaccines,
insulin, and other crucial biologics.
PEGylated Titanium Dioxide Nanoparticle-bound Doxorubicin and Paclitaxel Drugs Affect Prostate Cancer Cells and Alter the Expression of DUSP Family Genes
Authors: Tuncbilek Zuhal , Cakmak Nese Keklikcioglu , Tas Ayca , Ayan Durmus , Silig Yavuz
Published: October 2024

Background:

Prostate cancer (PC) is among the cancer types with high incidence and mortality. New
and effective strategies are being sought for the treatment of deadly cancers, such as PC. In this context, the use
of nanocarrier systems containing titanium dioxide (TiO2) can improve treatment outcomes and increase the effectiveness
of anticancer drugs.

Objective:

This study aimed to evaluate the cytotoxic activity of doxorubicin (DOX) and paclitaxel (PTX) drugs
on the PC cell line by attaching them to PEGylated TiO2 nanoparticles and to examine their effect on the expression
levels of dual-specificity phosphatase (DUSP) genes.

Methods:

Free DOX and PTX drugs, DOX and PTX compounds bound to the pegylated TiO2 system were applied
to DU-145 cells, a PC cell line, under in vitro conditions, and MTT analysis was performed. Additionally,
the IC50 values of these compounds were analyzed. In addition, the expression levels of DUSP1, DUSP2, DUSP4,
DUSP6, and DUSP10 genes were measured using RT-PCR. Additionally, bioinformatics and molecular docking
analyses were performed on DUSP proteins.

Results:

The cytotoxic activity of PTX compound bound to PEGylated TiO2 was found to be higher than that of
DOX compound bound to PEGylated TiO2. Additionally, when the expression levels were compared to the control
group, the expression levels of DUSPs were found to be lower in the drugs of the drug carrier systems.

Conclusion:

Accordingly, it was predicted that the PEGylated TiO2 nano-based carrier could be effective in
PC.
In Silico and ADMET Studies of Spiro-Quinazoline Compounds as Acetylcholine Esterase Inhibitors Against Alzheimer’s Disease
Authors: Aljuaid Abdulelah , Abdulaziz Osama , Allahyani Mamdouh , Almehmadi Mazen , Alzahrani Abdullah Yahya  Abdullah , Verma Shivani , Yusuf Mohd , Asif Mohammad
Published: October 2024

Background:

Alzheimer's disease (AD) is a prevalent neurodegenerative condition characterized
by progressive cognitive decline and memory impairment resulting from the degeneration
and death of brain neurons. Acetylcholinesterase (AChE) inhibitors are used in primary pharmacotherapy
for numerous neurodegenerative conditions, providing their capacity to modulate acetylcholine
levels crucial for cognitive function. Recently, quinazoline derivatives have emerged as a
compelling model for neurodegenerative disease treatment, showcasing promising pharmacological
features. Their unique structural features and pharmacokinetic profiles have sparked interest in their
potential efficacy and safety across diverse neurodegenerative disorders. The exposure of quinazoline
derivatives as a potential therapeutic way underscores the imperative for continued research exploration.
Their multifaceted mechanisms of action and ability to target various pathways implicated
in neurodegeneration offer exciting prospects for developing novel, effective, and well-tolerated
treatments. Further investigations into their pharmacological activities and precise therapeutic roles
are essential to advance our understanding of neurodegenerative disease pathophysiology and promote
the development of modern therapeutic strategies to address this critical medical challenge.

Methods:

Quinazoline derivatives have gained eminent acetylcholinesterase (AChE) inhibitory activity.
Their ability to effectively modulate AChE activity makes them promising candidates for
treating neurological disorders, particularly Alzheimer's disease (AD). Their intricate molecular
structures confer selectivity and affinity for AChE, offering potential for the development of novel
therapeutic agents targeting cholinergic pathways. Hence, in this study, we designed, synthesized,
and characterized a series of spiro[cycloalakane-1,2'-quinazoline derivatives (1-6) to assess their
possible AChE inhibiting ability using docking into the active sites.

Results:

The AChE inhibitory potential of spiro[cycloalkane-1,2'-quinazoline derivatives (1-6) was
explored via docking studies of the AChE active site. The findings revealed significant inhibitory
activity and highlighted the promising nature of these derivatives.

Conclusion:

The synthesized spiro[cycloalkane-1,2'-quinazoline derivatives (1-6) exhibited their
notable potential as AChE inhibitors. The observed significant inhibitory activity suggested that these
derivatives warrant further exploration as candidates for developing therapeutic agents in AChE
inhibitory pathways. This study emphasizes the relevance of quinazoline derivatives in searching for
novel treatments for neurological disorders, particularly associated with cholinergic dysfunction,
and they could be a useful alternative therapeutic agent.
Appropriate Selection of Frequency Range and Damping Ratio to Reduce the Resonance Effect, Lower Power Consumption, and Improve the Accuracy of the Capacitive Accelerometer
Authors: Ghemari Zine , Belkhiri Salah , Saad Salah
Published: October 2024

Introduction:

In this work, we explored the capacitive accelerometer in depth by highlighting
its advantages over other accelerometers. However, a major challenge associated with using
capacitive accelerometers lies in choosing the appropriate frequency range.

Method:

Incorrect selection of the frequency range can cause several problems. It can decrease the
accuracy of the accelerometer, increase the measurement error, and increase power consumption.
To overcome these challenges, we undertook detailed modeling of the physical behaviour of the capacitive
accelerometer. This modeling takes into account the mechanical and electrical properties of
the sensor, including its mass, its rigidity, and the characteristics of its capacitive circuit. By simulating
the developed model, we analyzed how the accelerometer reacts to different frequencies of
vibratory movements.

Result:

This analysis led to the extraction of an important mathematical relationship that links the
natural frequency of the accelerometer to the frequency of the vibratory movements to which it is
subjected. Using this mathematical relationship, we determined the optimal frequency range for capacitive
accelerometer operation. This precise determination of the frequency range makes it possible
to significantly reduce the measurement error by avoiding resonance regimes and ensuring that
the sensor operates in its maximum precision zone.

Conclusion:

Additionally, this approach helps improve overall measurement accuracy and minimize
sensor power consumption by avoiding inefficient operating conditions.
Key LncRNAs Associated with Distant Metastasis in Breast Cancer: A System Biology Analysis
Authors: Mohammadi Shakila , Dehghani-Samani Mina , Firouzi-Farsani Khatereh , Dibaj Mohsen , Zhaeentan Shahrzad
Published: October 2024

Introduction:

Breast cancer (BC) is the most prevalent cancer among women globally.
Metastasis is the leading cause of mortality in most cancers. Early BC detection before metastasis
can enhance survival rates. Understanding BC metastasis mechanisms could aid in developing
metastasis-specific treatments.

Method:

The role of long non-coding RNAs (lncRNA) in cancer progression is recognized, yet
the importance of specific lncRNAs in BC, despite potential alterations, remains inadequately
explored. We utilized bioinformatics tools to identify novel lncRNAs dysregulated in metastasis.
To achieve this objective, the gene expression profile of GSE102484, encompassing metastatic
and non-metastatic BC tissue samples, was analyzed using the limma package in R with cut-off
criteria set at an adjusted p-value < 0.005 and |fold change (FC)| ≥ 0.5. We used WGCNA analysis
to find co-expression genes for lncRNAs. Then, we identified hub genes and performed pathway
enrichment to better understand the results. Considering the defined criteria, eight novels of
dysregulated lncRNAs and top 10 miRNAs were identified.

Result:

Dysregulated lncRNAs are found in yellow, green, brown, purple, and turquoise co-expression
modules from WGCNA analysis. Enrichment analysis of these co-expressed modules
revealed relevant pathways to metastasis, such as epithelial-to-mesenchymal transition and integrin
cell-surface interactions, as well as regulation of HIF1-alpha. In addition, SDPR, TGFB1I1,
ILF3, KIF4A, and COL5A1 were identified as hub genes. Based on DElncRNA-miRNADEmRNA
connections and co-expression, we ultimately constructed lncRNA-associated ceRNA
axes.

Conclusion:

The current study may identify novel lncRNAs implicated in BC metastasis; still,
additional research is required to determine the potential functions of these lncRNAs in BC metastasis.
Jamming Attacks Detection Based on IGWO for Optimization of Fast Correlation-Based Feature extraction in Wireless Communication
Authors: Ameen Zinah Jaffar Mohammed , Naman Hala A. , Mahmood Alza Abduljabbar
Published: October 2024

Background:

Wireless networks are essential communication technologies that prevent
cable installation prices and burdens. Because of this technology's pervasive usage, wireless
network safety is a significant problem. Owing to distributed and open wireless medium
aspects, attackers might use different jamming methods to exploit physical and MAC layer
protocol vulnerabilities. In addition, jamming attacks require to be accurately grouped so that
suitable countermeasures can be considered. Given the potential severity of such attacks, precisely
identifying and classifying them is critical for implementing effective responses. The
motivation for this paper is the need to improve the detection and categorization of jamming
signals using modern machine learning algorithms, consequently enhancing wireless network
security and reliability.

Objective:

In this paper, we compare some machine learning models' efficiency for diagnosing
jamming signals.

Methods:

Such algorithms refer to support vector machine (SVM) and k-nearest neighbors
(KNN). We checked the signal features that recognize jamming signals. After the jamming attack
model, the developed grey wolf optimizer version known as IGWO (improved grey wolf
optimizer) has been discussed for feature extraction of software usability. Four separate metrics
were employed as features to detect jamming attacks in order to evaluate the machine learning
models. This novel feature extraction method is crucial for improving the accuracy of jamming
detection.

Results:

The measurements of these parameters were gathered through a simulation of a real
setting. And generated a large dataset using these parameters.

Conclusion:

The simulation results illustrate that the KNN algorithm based on jamming detection
could diagnose jammers having a minimal likelihood of false alarms and a high level of
accuracy.
Cardiotoxicity Caused by Doxorubicin and Trastuzumab: Current Understanding for Future Preventive Strategies
Authors: Siri-Angkul Natthaphat , Chattipakorn Siriporn C. , Chattipakorn Nipon
Published: October 2024

Cancers and cardiovascular disease continue to be leading causes of
mortality worldwide despite unrelenting efforts to improve therapeutic strategies in
both. Ironically, cardiac adverse effects of anticancer drugs result in an ever-increasing
proportion of deaths in cancer survivors. Doxorubicin, one of the earliest anthracycline
chemotherapeutic agents which has been in clinical use since the 1970s, is notorious
for causing cumulative dose-dependent irreversible cardiac damage, traditionally
termed “type I” cancer therapy-related cardiotoxicity. In the late 1990s, the approval of
trastuzumab, a monoclonal antibody against the human epidermal growth factor
receptor 2 (HER2), initiated an era of targeted anticancer therapy with the hope of
eradicating off-target adverse effects. Unfortunately, trastuzumab treatment leads to the
distinctive “type II” cancer therapy-related cardiotoxicity. As an acknowledged type I
and type II cardiotoxic anticancer agents, doxorubicin and trastuzumab have been
intensely investigated with regard to the complex mechanisms of their effects on the
heart, yet complete understanding remains elusive. This chapter comprehensively
summarizes current mechanistic insights regarding the cardiotoxicity of doxorubicin
and trastuzumab, encompassing the pathophysiology of contractile dysfunction
(cardiomyopathy) and electrical disturbance (arrhythmia). Gaps in knowledge and
recommendations for future advances are also discussed to encourage further
investigation in the field, with the ultimate goal being the effective prevention and/or
treatment of cancer therapy-induced cardiac complications.
Application of Nanotechnology in Nutraceuticals and Functional Foods
Authors: Tripathy Divya Bajpai , Chhabra Priyanka , Gupta Anjali , Singh Anurag
Published: October 2024

In today’s world, nanoscience and nanotechnology are persuading almost
every field of research and development including computer science, electrical,
electronics, communications, energy generation, pharmaceuticals, and many more.
Similarly, the food sector is also affected by the impact of nanotechnology.
Nanotechnology has a very significant role in nutraceuticals and functional foods and
covers a wide range of such applications, including the development of nanoparticulate
food materials like liposomes, micelles, nanosuspensions, nanoemulsions and
biopolymeric nanomaterials. Similarly, while considering the production sector,
nanosensors are found to be very helpful in maintaining a prime level of quality
foodstuff. Some fascinating applications of nanotechnology in food industries include
the protection of products due to oxidation, controlled nutrient release, taste screening,
nano-encapsulated nutraceuticals delivery, detection of pathogens detection in food,
food safety, better-quality packaging, an extension of shelf-life, and many more. The
current chapter is based on the role of nanotechnology in nutraceutical and functional
food. Various issues concerned with nanomaterial toxicity toward humans and the
environment are discussed.
Advanced Graphene-Based Supercapacitors for Energy Storage Applications
Authors: Kumari Isha , Kumar Sushant , Dhapola Pawan Singh , Singh Pramod K.
Published: October 2024

Graphene-based supercapacitors (SC) are rising as the most efficient and
smart energy storage systems. Nonpareil physiochemical properties of graphene offer
immense potential for their use in developing next-generation energy storage and
portable devices. Since the rise of graphene, this material has been seen as the best
alternative to activated carbon in SC applications. Being a 2D material, its high surface
area enables it to store electrostatic charge even after high cycling. Since the first
graphene-based SC was fabricated in 2008, this material has been explored beyond the
boundaries of pristine graphene. The recent invention paved the way for ultrafast
charging devices with excellent efficiency. However, the widespread use of these
devices in daily life seems far-fetched, but recent results in graphene-based
architectures are fetching these possibilities to life. In the last decade, various
revamped and manipulated graphene derivatives have also been investigated and found
to have great potential in SC applications. These derivatives have shown tremendous
specific capacitance with enhanced cyclability. Graphene derivatives can even exhibit
capacitance retention of almost 100% after 20,000 cycles. This book chapter discusses
the current state of affairs in various graphene-based SC devices, such as crumpled
graphene, graphene-metal oxide composites, graphene-based aerogels, graphene
nanoparticle systems, graphene-based fibers, graphene/carbon-based hybrid composites
for their potential application in the fabrication of efficient energy devices. This
comprehensive study aims to analyze current trends and the opportunities and
challenges offered by graphene and its derivatives in the development of nextgeneration SCs.
Toxicity of Graphene Family and Remediation Approaches
Authors: Bhatt Shalini , Pathak Rakshit , Faridi Neha
Published: October 2024

Graphene family nanomaterials (GFNs) appeared to be extensively exploited
in numerous diverse fields predominantly in the biomedical sector, owing to
distinctive physical, chemical as well as biological/biocompatible characteristics. With
the expanding uses, individuals are now exposed to GFNs more often and through a
variety of different routes. Upon exposure, these materials exhibit varying amounts of
toxicity in biological systems used for toxicological examinations. Administration by
various routes leads to penetration by breaching physical barriers and eventually gets
disseminated in various tissues or may accumulate in the cells, and subsequently may
get eliminated from the body. The present chapter provides information about the toxic
effect of the GFNs in several organs encompassing studies in various animals and cell
lines. Different factors including lateral size, functionality, concentration as well as
protein corona formation, etc. influencing the toxicity status of the GFNs have been
elaborated. Furthermore, some representative toxicity mechanisms include
mitochondrial as well as DNA impairment, and oxidative damage to name a few. At
last, we have provided toxicity remediation approaches for GFNs.
Silicene - A Novel 2D Material with Potential for Nanoelectronics and Photonics
Authors: Pathak Rakshit , Bhatt Shalini , Kumar Rajesh
Published: October 2024

Due to its distinct physicochemical properties, silicene, a silicon allotrope
with a 2-D honeycomb assembly, has attracted considerable interest from the entire
research community. The mixed sp2
/sp3
hybridization of silicon atoms increases surface
chemical activity and enables a range of mechanical and electronic characteristics. A
new topology of silicon-based nanoparticles known as 2D silicene has recently been
developed. It has a distinctive planar structure with a considerable surface, unusual
physiochemical characteristics, and favorable biological effects. In theoretical
observation, it exhibits remarkable characteristics and has many advantages over
graphene as a 2D material, which makes it a more exciting component and a matter of
deep study. So, the present chapter provides a complete overview of this 2D material
covering its wide applications in different sectors. The chapter mainly provides insights
into the synthesis approach and its characteristics, including its mechanical, electrical,
and spintronic attributes. Then, to shed light on the various phases of silicene seen on
the metal surfaces on its electrical structures, we describe the experimental
characterization of silicene. The chapter also covers the most current uses of silicene
outlined in the context of nanoelectronics.
Leer- en ontwikkelingsproblemen in de kindertijd: a question of time
Authors: Van Dijk Marijn
Published: October 2024

Niet alle kinderen ontwikkelen zich ‘volgens het boekje’. Sommige kinderen lopen voor, andere lopen achter, of gedragen zich anders dan andere kinderen. Dit kan problemen veroorzaken bij het leren en risico's met zich meebrengen voor de verdere ontwikkeling. Marijn van Dijk pleit in haar oratie voor een procesgerichte aanpak, waarbij gekeken wordt naar patronen in leren en gedrag. Kinderen, leerkrachten en ouders zijn onlosmakelijk met elkaar verbonden en beïnvloeden elkaar voortdurend. Je moet dus naar het systeem als geheel kijken. 'We denken ook nog te vaak in termen van gemiddelde groeicurves. Hierdoor vergeten we dat leren meestal met horten en stoten gaat', betoogt ze. Met haar aanpak wil Van Dijk een alternatief bieden voor het efficiëntie-denken in het onderwijs. 'Iedereen wil het beste voor het kind, maar dit leidt tot een druk op vroegsignalering en classificerende diagnoses zoals ADHD', aldus Van Dijk. Problemen worden meestal veroorzaakt door een complex samenspel van factoren en kunnen bijna nooit worden toegeschreven aan één enkele oorzaak. Er is misschien een bepaalde aanleg, er zijn aangeboren of vroeg verworven voorkeuren of beperkingen, maar de problemen ontstaan telkens in het hier-en-nu, in specifieke situaties. Volgens haar is het daarom belangrijk om eerst te begrijpen hoe de dynamiek in elkaar zit. Op basis hiervan kunnen vervolgens interventies worden ontwikkeld die hierop ingrijpen.
ARCHITECTURAL DESIGN AND OPTIMIZATION OF DISTRIBUTED ARITHMETIC BASED 2-D DISCRETE COSINE TRANSFORM
Authors:   SDM College of Engineering and Technology, India, Shirakol Shrikanth , S S Kerur ,   SDM College of Engineering and Technology, India
Published: October 2024

DCT is immensely used in Multimedia applications because it provides high energy compaction. The proposed architectural design of 1D-DCT employs an efficient computational technique, Distributed arithmetic and is synthesized using front end VLSI technique. The motive of utilising Distributed arithmetic is to have multiplier-less architecture that reduces the Area-delay product in comparison to the multiplier-based design by retaining the same structural regularities. The symmetric property of the DCT kernel matrix is applied to develop the proposed architecture which reduces the requirements of a number of multiplications by almost 50%. The 1D-DCT architecture is extended to 2D-DCT using only N 1D-DCT modules, while the conventional row-column decomposition method requires 2N 1D-DCT modules. The proposed 2D-DCT architecture is designed and implemented on a 65nm LX110T device of Vertex-5 FPGA and its performance evaluation is carried out. The debug and verification process has been carried out using the Virtual input-output technique. The results show improvement in delay and area consumption in contrast with existing models.
CONTROLLING AMBIPOLAR CURRENT AND ENHANCEMENT OF ON-STATE CURRENT WITH HIGH AND LOW CONCENTRATION SOURCE POCKETS
Authors:   National Institute of Technology, Goa, India, Kumar Sudhan , Rajput Dheeraj Singh ,   Indira Gandhi Engineering College, India, Gupta Ritu ,   Indira Gandhi Engineering College, India, Shukla Sankalp ,   Indira Gandhi Engineering College, India, Parsediya Deep Kishore ,   Madhav Institute of Technology and Science, India, Sharma Vikash ,   Rewa Engineering College, India
Published: October 2024

This paper proposes and investigates a new architecture of PNPN TFET by using 2-D ATLAS Device Simulation Software TCAD Tool. The source pocket plays a crucial role in increasing the ON-state current through Tunnel FET. In order to enhance the source pocket characteristics, the source pocket is divided laterally into high and low concentration source pockets without modulating its width (LP). This modification results in reduction of tunnelling width (?), thereby increasing the significant amount of lateral electric field and suppressing the horizontal electric field, which in turn enhance the tunnelling probability as well as tunnelling rate. This proposed structure is a promising candidate for getting higher ON-state current (Ion), higher current ratio (Ion/Ioff), lower threshold voltage (VT) and reduced steepness sub-threshold slope (SS). These parameters are calculated with respect to conventional PNPN TFET and hetero-dielectric BOX (HDB) PNPN TFET, which enable the device to be operated efficiently with lower power consumption.
THE CONSTRUCTION AND PERFORMANCE ENHANCEMENTS OF ULTRA HIGH-FREQUENCY RANGE FOR THE INDOOR AND OUTDOOR ANTENNAS IN BUILT-IN COMMUNICATION NETWORKS
Authors:   Park College of Engineering and Technology, India, J Gowri , Nair Indu ,   Park College of Engineering and Technology, India, Priyadharshini Hari Hara ,   Dell Technologies, India
Published: October 2024

Digital terrestrial television is a good budget alternative to satellite TV. Its main advantages are excellent image and sound quality and no subscription fees. There is a set-top box (receiver) and antenna for digital TV in smart homes, and a great picture on any TV is guaranteed. The accurate picture is achieved due to a different quality signal transmission system than analogue TV. The digital signal is not subject to interference and interference and depends very little on the distance to the transmitter. The proposed antenna design provides enhancements to the Ultra High Frequency (UHF) range for indoor and outdoor antennas. There are indoor and outdoor antennas for the home and Dutch, respectively, where the antenna is installed. They are divided into active and passive depending on the presence of a built-in signal amplifier operating from the network. If the cable is long, the signal attenuation will be strong, which is very important for receiving meter waves unlike UHF, for which the length of the wire is also important, but not so much.
DEEP CNN WITH SVM - HYBRID MODEL FOR SENTENCE-BASED DOCUMENT LEVEL SENTIMENT ANALYSIS USING SUBJECTIVITY DETECTION
Authors:   Presidency College, India, K Raviya , S Mary Vennila ,   Presidency College, India
Published: October 2024

With the growth of e-commerce reporting, online customer reviews have evolved rapidly, voicing the sentiment or opinion of customers about goods. The analysis of belief could provide useful data for us. Sentiment analysis on social media like Twitter or Facebook, is now the comprehensive way of understanding about the views of customers and has extensive variety of applications. In the context of NLP, automated text classification can be a fundamental activity and it can help people to pick essential information from vast text resources. Sentiment analysis may be a computational technique that plays a key role in automating the retrieval of subjective knowledge, i.e. customer’s sentiment from online text reviews or opinion from social network like Twitter and Facebook. Lexicon-based and machine learning-based methods are two main approaches widely used in sentiment analysis activities. In machine learning based framework, Sentiment analysis is a text recognition task. The outcome depends not only from the soundness of the algorithm for machine learning, but also with the appropriate features. In recent years, the most recent technological advancements, like deep-learning techniques, have resolved a number of standard challenges caused by the lack of lexical tools in the region. It has been exhibited that deep-learning models are auspicious and potential tool to NLP challenges. In this work, the fusion of deep CNN with SVM will automatically detect and extract subjective sentence-level features to perform sentiment analysis of online product review dataset with highest accuracy and less computation time.
NOVEL MULTI-LEVEL ASPECT BASED SENTIMENT ANALYSIS FOR IMPROVED ROOT-CAUSE ANALYSIS
Authors:   Bharathiar University, India, Seerangan Naveenkumar , Shanmugam Vijayaragavan ,   Muthayammal Engineering College, India
Published: October 2024

Aspect extraction and sentiment identification are the two important tasks to provide effective root cause analysis. This work presents a Multi-Level Aspect based Sentiment Analysis (MLASA) model that integrates the aspect extraction and sentiment identification modules to provide effective root cause analysis. The aspect extraction module performs token filtration, followed by rule based aspect identification. The heterogeneous multi-level sentiment identification phase performs aspect based sentiment identification. First level performs magnitude along and polarity identification of text, while the second level performs polarity identification using multiple machine learning models. The results are aggregated and ranked based on aspect significance and sentiment magnitude. Experiments and comparisons show effective performance of the MLASA model.
GENERIC APPROACH OF MEASURING TEXT SEMANTIC SIMILARITY
Authors:   Bhilai Institute of Technology, India, Dhagat Richa , Rawal Arpana ,   Bhilai Institute of Technology, India, Soni Sunita ,   Bhilai Institute of Technology, India
Published: October 2024

Text Semantic Similarity can be viewed as one of the challenging tasks as evident from current profound interest in NLP research community that has created achievable milestones through active participation in SemEval task series of the recent decade. Amidst these developments, it was realized that exploring text to compare its semantics largely depends on valid grammatical structures of sentences and sentence formulation types. In this paper, the computation of text semantic similarity is addressed by devising a novel set of generic similarity metrics based on both, word-sense of the phrases constituting the text as well as the grammatical layout and sequencing of these word-phrases forming text with sensible meaning. We have used the combination of word-sense and grammatical similarity metrics over benchmark sentential datasets. Having obtained highest value of Pearson’s correlation coefficient (0.89) with mean human similarity scores, when compared against equivalent scores obtained through closely competent structured approach models, plagiarism-detection classification task was revisited on well-known paragraph-phrased Rewrite corpus articulated by Clough and Stevenson (2011) using our model to provide generic utility perspective to these novel devised similarity metrics. Here also, nearly competent classification model performance (with accuracy 76.8%) encouraged authors to work in directions that are more promising where the performance can be enhanced by improving upon dependency (grammatical relations) component in order to raise the count of true-positives and false-negatives.
A FUZZY BASED DEEP LEARNING MODEL TO IDENTIFY THE PATTERN RECOGNITION FOR LICENSED PLATES IN SMART VEHICLE MANAGEMENT SYSTEM
Authors:   Karpagam Institute of Technology, India, B Chellapraba , D Manohari ,   St. Joseph’s Institute of Technology, India, M S Kavitha ,   SNS College of Engineering, India, K Periyakaruppan ,   SNS College of Engineering, India
Published: October 2024

In general, vehicle management is based on the proper maintenance and safety of a vehicle. Based on this the quality of the vehicle is calculated. Most of the older vehicles are currently of poor quality and are producing high levels of pollution. Thus, it is necessary to find information about those vehicles. The number plate is helpful to find the information about the vehicle. In this paper, the number blood detection method is proposed. It is based on the fuzzy model and developed in the way of deep learning. Its main purpose is to provide accurate vehicle details from a given set of data. It has also been upgraded to provide its safety measures to its owner based on the vehicle data. Thus, this proposed model prevents major accidents. These functions can also be very helpful in recovering vehicles based on data from stolen vehicles.
AUTOMATED SUMMARIZATION OF RESTAURANT REVIEWS USING HYBRID APPROACHES
Authors:   Avinashilingam Institute for Home Science and Higher Education for Women, India, George Shini , V Srividhya ,   Avinashilingam Institute for Home Science and Higher Education for Women, India
Published: October 2024

The arena of automatic text summarization incorporates the paramount and relevant information from a large document. This research paper attempts at representing two hybrid models for automatic text summarization. Extractive summarization followed by an abstractive summarization, is the strategy which is adopted in this paper to produce an informative and concise summary. The LexRank algorithm is used for extractive summarization, while BART (Bidirectional and Auto Regressive Transformers) and T5 (Text-ToText Transfer Transformer) are used for abstractive summarization. BART and T5 are advanced per-trained models based on Transformer. The Transformer-based Per-trained models are causing a stir in the deep learning world. The first hybrid model is constructed using LexRank with BART (LRB) and the second hybrid model is constructed with LexRank with T5 (LRT). This specific approach will result in the generation of the extractive summary using the LexRank algorithm. The resulted output of LexRank is used as the input for BART and T5. The efficiency of two hybrid models is analyzed using qualitative and quantitative methods. The human-generated summary is used to evaluate the quality of the models, while the ROUGE score provides a quantitative assessment of their performance. Thus, this work may be concluded in the precision that, the LRT hybrid model is more effective than LRB hybrid model.
BOOSTING THE ACCURACY OF OPTIMISATION CHATBOT BY RANDOM FOREST WITH HALVING GRID SEARCH HYPERPARAMETER TUNING
Authors:   Dayanand Sagar University, India, Nagaraj Bedre , B. Malagi Kiran ,   Dayanand Sagar University, India
Published: October 2024

Computer science, engineering and technologies are witnessing a vital role in providing challenging demands of users. Artificial intelligence, machine learning and robotic process automation strive to improve the intelligent behavior of computers. Fast human like responses of text chatbot can perform better if and only if it is optimized. Hyper parameter optimization methods are popular for successfully boosting up the overall performance of model. In this paper we focus on creating chatbot using random forest and optimizing its performance by hyper parameter tuning halving grid search. We propose chatbot model 1 without optimization, chatbot model 2 with optimization and chatbot model3 with optimization and best values of key performance indicators. Computations are performed before optimization and after optimization for measurement factors including accuracy, precision, recall and f1-scores. Three different models proposed, and performance are compared for each model with respect to precision, recall, f1-scores and accuracy.
MORPHIC COMPUTING WITH MACHINE LEARNING FOR ENHANCED FRAUD DETECTION IN FINANCIAL APPLICATIONS
Authors:   R.M.K. College of Engineering and Technology, India, N.P. Ponnuviji , M Vigilson Prem ,   R.M.K. College of Engineering and Technology, India
Published: October 2024

As financial fraud becomes increasingly complex, traditional detection methods struggle to keep pace, resulting in substantial financial losses globally. Morphic computing—a paradigm that emphasizes adaptable, context-aware processing—offers promising advancements for fraud detection in dynamic environments. Integrating morphic computing with machine learning models creates a responsive framework capable of discerning subtle and evolving fraud patterns. The proposed system utilizes a Convolutional Neural Network (CNN) enhanced with Morphic Layering, where layers adaptively morph in response to new data patterns. The dataset, sourced from real-time financial transactions, consists of 500,000 records, including 2,000 flagged fraudulent cases. The system was tested on a simulated environment over a six-month period, yielding an accuracy of 98.5% in fraud detection and reducing false positives by 40% compared to traditional machine learning models. Latency for real-time detection was minimized to 200 milliseconds, proving feasible for immediate application in transaction monitoring systems. By offering a flexible structure, this method surpasses existing approaches, as it continuously evolves to detect emerging fraud patterns, thus enhancing financial security.
DEEP Q-NETWORK (DQN) PARTIALOCCLUSION SEGMENTATION AND BACKTRACKING SEARCH OPTIMIZATION ALGORITHM (BSOA) WITH OPTICAL FLOW RECONSTRUCTION FOR FACIAL EXPRESSION EMOTION RECOGNITION
Authors:   PSG College of Technology, India, S.S. Sudha , S.S. Suganya ,   Dr. SNS Rajalakshmi College of Arts and Science, India
Published: October 2024

Video facial expression recognition (FER) has garnered a lot of attention recently and is helpful for several applications. Although many algorithms demonstrate impressive performance in a controlled environment without occlusion, identification in the presence of partial facial occlusion remains a challenging issue. Solutions based on reconstructing the obscured area of the face have been suggested as a way to deal with occlusions. These options mostly rely on the face’s shape or texture. Nonetheless, the resemblance in facial expressions among individuals appears to be a valuable advantage for the reconstruction. For semantic segmentation based on occlusions, Reinforcement Learning (RL) is introduced as the initial stage. From a pool of unlabeled data, an agent learns a policy to choose a subset of tiny informative image patches to be tagged instead of full images. In the second stage, a trained Backtracking Search Algorithm (BSA) is used to rebuild optical flows that have been distorted by the occlusion. On obtaining optical flows estimated from occluded facial frames, AEs restore optical flows of occluded regions. These recovered optical flows become inputs to anticipate classes f expressions. Optical flux reconstructions then classify stages. This study evaluates classification model’s performances for face expression identification based on Very Deep Convolution Networks (VGGNet). Furthermore, it produces more accurate confusion matrices and proposes approaches for the KMU-FED and CK+ databases, respectively. The results are evaluated using metrics including recall, f-measure, accuracy, and precision.
User Perceptions of Wearability of Knitted Sensor Garments for Long-Term Monitoring of Breathing Health: Thematic Analysis of Focus Groups and a Questionnaire Survey
Authors: Fobelets Kristel , Mohanty Nikita , Thielemans Mara , Thielemans Lieze , Lake-Thompson Gillian , Liu Meijing , Jopling Kate , Yang Kai
Published: October 2024

Background

Long-term unobtrusive monitoring of breathing patterns can potentially give a more realistic insight into the respiratory health of people with asthma or chronic obstructive pulmonary disease than brief tests performed in medical environments. However, it is uncertain whether users would be willing to wear these sensor garments long term.

Objective

Our objective was to explore whether users would wear ordinary looking knitted garments with unobtrusive knitted-in breathing sensors long term to monitor their lung health and under what conditions.

Methods

Multiple knitted breathing sensor garments, developed and fabricated by the research team, were presented during a demonstration. Participants were encouraged to touch and feel the garments and ask questions. This was followed by two semistructured, independently led focus groups with a total of 16 adults, of whom 4 had asthma. The focus group conversations were recorded and transcribed. Thematic analysis was carried out by three independent researchers in 3 phases consisting of familiarization with the data, independent coding, and overarching theme definition. Participants also completed a web-based questionnaire to probe opinion about wearability and functionality of the garments. Quantitative analysis of the sensors’ performance was mapped to participants’ garment preference to support the feasibility of the technology for long-term wear.

Results

Key points extracted from the qualitative data were (1) garments are more likely to be worn if medically prescribed, (2) a cotton vest worn as underwear was preferred, and (3) a breathing crisis warning system was seen as a promising application. The qualitative analysis showed a preference for a loose-fitting garment style with short sleeves (13/16 participants), 11 out of 16 would also wear snug fitting garments and none of the participants would wear tight-fitting garments over a long period of time. In total, 10 out of 16 participants would wear the snug fitting knitted garment for the whole day and 13 out of 16 would be happy to wear it only during the night if not too hot. The sensitivity demands on the knitted wearable sensors can be aligned with most users’ garment preferences (snug fit).

Conclusions

There is an overall positive opinion about wearing a knitted sensor garment over a long period of time for monitoring respiratory health. The knit cannot be tight but a snugly fitted vest as underwear in a breathable material is acceptable for most participants. These requirements can be fulfilled with the proposed garments. Participants with asthma supported using it as a sensor garment connected to an asthma attack alert system.
Predictive Models for Sustained, Uncontrolled Hypertension and Hypertensive Crisis Based on Electronic Health Record Data: Algorithm Development and Validation
Authors: Nguyen Hieu Minh , Anderson William , Chou Shih-Hsiung , McWilliams Andrew , Zhao Jing , Pajewski Nicholas , Taylor Yhenneko
Published: October 2024

Background

Assessing disease progression among patients with uncontrolled hypertension is important for identifying opportunities for intervention.

Objective

We aim to develop and validate 2 models, one to predict sustained, uncontrolled hypertension (≥2 blood pressure [BP] readings ≥140/90 mm Hg or ≥1 BP reading ≥180/120 mm Hg) and one to predict hypertensive crisis (≥1 BP reading ≥180/120 mm Hg) within 1 year of an index visit (outpatient or ambulatory encounter in which an uncontrolled BP reading was recorded).

Methods

Data from 142,897 patients with uncontrolled hypertension within Atrium Health Greater Charlotte in 2018 were used. Electronic health record–based predictors were based on the 1-year period before a patient’s index visit. The dataset was randomly split (80:20) into a training set and a validation set. In total, 4 machine learning frameworks were considered: L2-regularized logistic regression, multilayer perceptron, gradient boosting machines, and random forest. Model selection was performed with 10-fold cross-validation. The final models were assessed on discrimination (C-statistic), calibration (eg, integrated calibration index), and net benefit (with decision curve analysis). Additionally, internal-external cross-validation was performed at the county level to assess performance with new populations and summarized using random-effect meta-analyses.

Results

In internal validation, the C-statistic and integrated calibration index were 0.72 (95% CI 0.71‐0.72) and 0.015 (95% CI 0.012‐0.020) for the sustained, uncontrolled hypertension model, and 0.81 (95% CI 0.79‐0.82) and 0.009 (95% CI 0.007‐0.011) for the hypertensive crisis model. The models had higher net benefit than the default policies (ie, treat-all and treat-none) across different decision thresholds. In internal-external cross-validation, the pooled performance was consistent with internal validation results; in particular, the pooled C-statistics were 0.70 (95% CI 0.69‐0.71) and 0.79 (95% CI 0.78‐0.81) for the sustained, uncontrolled hypertension model and hypertensive crisis model, respectively.

Conclusions

An electronic health record–based model predicted hypertensive crisis reasonably well in internal and internal-external validations. The model can potentially be used to support population health surveillance and hypertension management. Further studies are needed to improve the ability to predict sustained, uncontrolled hypertension.
Promoting Digital Health Data Literacy: The Datum Project
Authors: Powell Daniel , Asad Laiba , Zavaglia Elissa , Ferrari Manuela
Published: October 2024

With the increased use of digital health innovations in Canadian health care, educating health care users, professionals, and researchers on the ethical challenges and privacy implications of these tools is essential. The Datum project, funded by the Fondation Barreau du Quebec, was created to help these actors better understand legal and ethical issues regarding the collection, use, and disclosure of digital health data for the purposes of scientific research, thereby enhancing literacy around data privacy. The project consists of a multimedia website divided into legislation and policy documents and narrative-based video content. Users can access the core legislation and policies governing the collection and use of health care data geared toward researchers and health practitioners. Users can also view the narrative-based video content explaining key concepts related to digital health data. The Datum project makes an original contribution to the field of law and ethics in health science research by using novel approaches, such as learning health systems and data banks, to improve equity in health care delivery and by generating multimedia content aimed at encouraging health care users to become better consumers and supporting the collective use of their data. The Datum project also promotes digital literacy as a digital communication tool, which has the significant potential to improve health outcomes, bridge the digital divide, and reduce health inequities.
Pathway for Meaningful Use of Claim Big Data in China: An Exploratory Study of Supporting Commercial Insurance Service with Claim Big Data (Preprint)
Authors: Huang Shanlingzi , Guan Meng , Yuan Fenghua , Dong Xuegeng , Wang Yao , Nan Shan , Li Wenxiu
Published: October 2024

BACKGROUND

With the development of medical information technology, China has built a national unified health insurance data information platform and formed large-scale health insurance big data. The informatization level of China's healthcare industry and the value of healthcare big data are increasing. However, the development and utilization of these data are constrained by conditions such as security compliance, business models, and mission boundaries, resulting in under-exploitation and utilization of data resources.

OBJECTIVE

Considering that there is a research lacuna in this area, we take the lead in exploring the path of health insurance data sharing.

METHODS

We clarified the objects involved and their rights and responsibilities in each link of the health insurance data service based on the basis of sorting out the existing laws and regulations, organizing the relevant participants to formulate the specification of the process of health insurance big data sharing based on the Delphi method, and formulating the pricing and revenue distribution method of health insurance data service through the cost analysis method, and taking the data service of underwriting and claim settlement for the commercial insurance company as a case study to validate this study.

RESULTS

A case study is conducted to verify the feasibility of the proposed method in this study and to provide a reference for future health insurance data sharing and utilization applications.

CONCLUSIONS

By applying the proposed approach, it is both feasible and convenient to use claim big data to support commercial insurance service.