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Healthcraft Frontiers
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Healthcraft Frontiers (HF)
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ISSN (print): 3005-7981
ISSN (online): 3005-799X
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2023: Vol. 1
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Healthcraft Frontiers (HF) is dedicated to advancing multidisciplinary research in health sciences. It focuses on publishing innovative and comprehensive findings that push the boundaries of current knowledge in health and well-being. The journal emphasizes an integrative approach, blending traditional practices with groundbreaking research, to drive advancements in health care. It seeks scholarly contributions that challenge established theories and provide practical solutions and insights with global public health and policy implications. The journal typically releases its four issues in March, June, September, and December each year.

  • Professional Service - Every article submitted undergoes an intensive yet swift peer review and editing process, adhering to the highest publication standards.

  • Prompt Publication - Thanks to our proficiency in orchestrating the peer-review, editing, and production processes, all accepted articles see rapid publication.

  • Open Access - Every published article is instantly accessible to a global readership, allowing for uninhibited sharing across various platforms at any time.

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Aims & Scope

Aims

Healthcraft Frontiers (HF) seeks to advance the multidisciplinary dialogue in health sciences by showcasing research that challenges and extends current knowledge boundaries. The journal's aim is to publish comprehensive and innovative findings in the health domain, supporting a broadened understanding of health and well-being. Emphasis is placed on integrative approaches that combine traditional practices with cutting-edge research to foster breakthroughs in health care. Scholarly contributions are expected to not only question established theories but also offer tangible solutions and insights that have the potential to influence public health and policy on a global scale.

Furthermore, HF highlights the following features:

  • Every publication benefits from prominent indexing, ensuring widespread recognition.

  • A distinguished editorial team upholds unparalleled quality and broad appeal.

  • Seamless online discoverability of each article maximizes its global reach.

  • An author-centric and transparent publication process enhances submission experience.

Scope

HF's expansive scope encompasses, but is not limited to:

  • Advanced biomedical research that pushes the frontiers of genetic, molecular, and cellular understandings of health and disease.

  • Public health studies that go beyond traditional epidemiology to include global health security, health economics, and the impact of health policies on disease prevention and management.

  • Behavioral and mental health research that explores new therapeutic paradigms, including the integration of technology in treatment and the role of digital health in modern healthcare.

  • Environmental health research that considers the complex interactions between humans and their environments, including studies on climate change, pollution, and urban health.

  • Nutrition and lifestyle studies that examine the influence of diet, exercise, and lifestyle choices on health and chronic disease management.

  • Health systems and policy research focused on the analysis and design of healthcare delivery systems, aiming to improve quality, efficiency, and equity in healthcare.

  • Translational research that includes the development of new diagnostic tools, vaccines, and therapeutics, emphasizing rapid translation of research into practice.

  • Innovations in healthcare technology, including telemedicine, health informatics, and the use of artificial intelligence in healthcare settings.

  • Integrative and complementary medicine studies that evaluate the efficacy and integration of alternative healing practices into conventional medicine.

  • Patient-centered research that emphasizes patient engagement, experience, and outcomes in the design and evaluation of healthcare interventions.

    Healthcraft Frontiers encourages submissions that not only contribute to their respective fields but also cross-pollinate ideas among various health disciplines, ultimately aiming to catalyze interdisciplinary research and innovation for a healthier global society.

Articles
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Open Access
Research article
Segmentation and Classification of Skin Cancer in Dermoscopy Images Using SAM-Based Deep Belief Networks
syed ziaur rahman ,
tejesh reddy singasani ,
khaja shareef shaik
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Available online: 11-30-2023

Abstract

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In the field of computer-aided diagnostics, the segmentation and classification of biomedical images play a pivotal role. This study introduces a novel approach employing a Self-Augmented Multistage Deep Learning Network (SAMNetwork) and Deep Belief Networks (DBNs) optimized by Coot Optimization Algorithms (COAs) for the analysis of dermoscopy images. The unique challenges posed by dermoscopy images, including complex detection backgrounds and lesion characteristics, necessitate advanced techniques for accurate lesion recognition. Traditional methods have predominantly focused on utilizing larger, more complex models to increase detection accuracy, yet have often neglected the significant intraclass variability and inter-class similarity of lesion traits. This oversight has led to challenges in algorithmic application to larger models. The current research addresses these limitations by leveraging SAM, which, although not yielding immediate high-quality segmentation for medical image data, provides valuable masks, features, and stability scores for developing and training enhanced medical images. Subsequently, DBNs, aided by COAs to fine-tune their hyper-parameters, perform the classification task. The effectiveness of this methodology was assessed through comprehensive experimental comparisons and feature visualization analyses. The results demonstrated the superiority of the proposed approach over the current state-of-the-art deep learning-based methods across three datasets: ISBI 2017, ISBI 2018, and the PH2 dataset. In the experimental evaluations, the Multi-class Dilated D-Net (MD2N) model achieved a Matthew’s Correlation Coefficient (MCC) of 0.86201, the Deep convolutional neural networks (DCNN) model 0.84111, the standalone DBN 0.91157, the autoencoder (AE) model 0.88662, and the DBN-COA model 0.93291, respectively. These findings highlight the enhanced performance and potential of integrating SAM with optimized DBNs in the detection and classification of skin cancer in dermoscopy images, marking a significant advancement in the field of medical image analysis.
Open Access
Research article
A CNN Approach for Enhanced Epileptic Seizure Detection Through EEG Analysis
nadide yucel ,
hursit burak mutlu ,
fatih durmaz ,
emine cengil ,
muhammed yildirim
|
Available online: 11-30-2023

Abstract

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Epilepsy, the most prevalent neurological disorder, is marked by spontaneous, recurrent seizures due to widespread neuronal discharges in the brain. This condition afflicts approximately 1% of the global population, with only two-thirds responding to antiepileptic drugs and a smaller fraction benefiting from surgical interventions. The social stigma and emotional distress associated with epilepsy underscore the importance of timely and accurate seizure detection, which can significantly enhance patient outcomes and quality of life. This research introduces a novel convolutional neural network (CNN) architecture for epileptic seizure detection, leveraging electroencephalogram (EEG) signals. Contrasted with traditional machine-learning methodologies, this innovative approach demonstrates superior performance in seizure prediction. The accuracy of the proposed CNN model is established at 97.52%, outperforming the highest accuracy of 93.65% achieved by the Discriminant Analysis classifier among the various classifiers evaluated. The findings of this study not only present a groundbreaking method in the realm of epileptic seizure recognition but also reinforce the potential of deep learning techniques in medical diagnostics.
Open Access
Research article
Racism and Hate Speech Detection Using QAHA Based Hybrid Deep Learning Model: LSTM-CNN
praveen kumar jayapal ,
kumar raja depa ramachandraiah ,
kranthi kumar lella
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Available online: 11-29-2023

Abstract

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Twitter, a predominant platform for instantaneous communication and idea dissemination, is often exploited by cybercriminals for victim harassment through sexism, racism, hate speech, and trolling using pseudonymous accounts. The propagation of racially charged online discourse poses significant threats to the social, political, and cultural fabric of many societies. Monitoring and prompt eradication of such content from social media, a breeding ground for racist ideologies, are imperative. This study introduces an advanced hybrid forecasting model, utilizing convolutional neural networks (CNNs) and long-short-term memory (LSTM) neural networks, for the efficient and accurate detection of racist and hate speech in English on Twitter. Unlabelled tweets, collated via the Twitter API, formed the basis of the initial investigation. Feature vectors were extracted from these tweets using the TF-IDF (Term Frequency-Inverse Document Frequency) feature extraction technique. This research contrasts the proposed model with existing intelligent classification algorithms in supervised learning. The HateMotiv corpus, a publicly available dataset annotated with types of hate crimes and ideological motivations, was employed, emphasizing Twitter as the primary social media context. A novel aspect of this study is the introduction of a revised artificial hummingbird algorithm (AHA), supplemented by quantum-based optimization (QBO). This quantum-based artificial hummingbird algorithm (QAHA) aims to augment exploration capabilities and reveal potential solution spaces. Employing QAHA resulted in a detection accuracy of approximately 98%, compared to 95.97% without its application. The study's principal contribution lies in the significant advancements achieved in the field of racism and hate speech detection in English through the application of hybrid deep learning methodologies.

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