Enhancing Wireless Sensor Network Lifetime through Energy-Efficient Data Clustering and Compressed Forwarding in video Processing

Jayasudha A. R.; Ramya S.; Vairaprakash S.; N. Kannaiya Raja

doi:https://doi.org/10.54216/IJWAC.080201

Fusion: Practice and Applications | 2024

Fusion of Topic Modeling and RoBERTa for Detecting Signs of Depression from Social Media

Depression, or Major Depressive Disorder, is a serious and common medical condition that affects people worldwide. It negatively affects the person's feelings, thoughts, and actions. Depression causes a loss of interest in activities he enjoyed in the past.  It can lead to physical and emotional problems that hamper the daily activities at work and home. In recent years, much research has been done to identify Depression through various modalities of image, speech, and text through artificial intelligence. Social media is an important medium where many discussions and mentions happen about Depression. The current study proposes a novel approach to understand how the depressed and non-depressed communicate differently with the help of Topic Modeling with latent-Dirichlet allocation (LDA) and also detect depression with the help of Robustly Optimized BERT Pretraining Approach (RoBERTa). The current study achieved an accuracy of 66.4% for the depression detection model, which outperformed the previous approaches with similar methodology. The current study is helpful for self-diagnosis of signs of Depression at very early stages.

groups

Madhu Sudhan H. V. mail -

S. Saravana Kumar mail

link https://doi.org/10.54216/FPA.150115

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2024

An Intelligent Fusion Framework for Risk Assessment of Notarial Activities in the Digital Era: Balancing Speed and Legal Security with ICT

The integration of Information and Communication Technologies (ICT) into notarial activities has revolutionized the way procedures are processed by significantly enhancing speed and legal security, which are key aspects for user satisfaction. This shift responds to the growing demand for fast and secure notarial services, where efficiency and legal protection are priorities. Through the analysis conducted with the neutrosophic RAFSI method, risks derived from digitalization have been identified and classified, proposing effective solutions for their mitigation. Among these, the need to update regulations to adapt them to the digital context and the importance of training notaries in digital competencies and cyber security stand out. These measures are focused not only on streamlining notarial procedures but also on reinforcing trust in notarial services, marking a significant advance toward the modernization of notarial practice in the digital era. In conclusion, the fusion of ICT with notarial activities, supported by risk control and supervision, has effectively balanced service speed with legal security, meeting the current expectations of users.

groups

Luis F. Piñas Piñas mail -

Luis R. Miranda Chávez mail -

Carlos J. Lizcano Chapeta mail -

Marina Abdurashidova mail

link https://doi.org/10.54216/FPA.150116

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

International Journal of BIM and Engineering Science | 2024

A New Approach to Enhance Smart City Applications Through Integrating GIS & BIM Technology

The research deals with the study and analysis of smart cities, the requirements for their establishment, and the role of Building Information Modeling (BIM) in building sustainable, energy-efficient smart cities. The primary objective is to put a smart city transformation methodology and apply it on Lattakia city as a case study, using Building Information Modeling (BIM). We proposed new tools in an integrated methodology to transform from ordinary to smart city, and implemented basics of its objects, including GIS/BIM technologies integration, insuring both sustainable and energy saving vision at general planning designs. Implementing the chosen methodology steps at an organized area in Syria – Lattakia City, starting with building an accurate geodatabase, then, obtaining Sun Shadow Frequency analysis, which shows that December contains the highest shade values, with cumulative coverage rate 79.84%, while June instead contain the lowest shade value, reaching only 33.84%. But in general, most of the area has shade coverage that does not exceed 15% most of the day, therefore no need to make an adjustment to the distribution of building blocks. After that, in order to secure the energy needed for the study Area, analyzing the Land Surface Temperature (LST) done using the ArcGIS Pro® software, and locating a renewable energy station site (solar plant). On the other hand, analyzing a residential building energy through annual PV analysis has been done, by using Revit® software through Insight™ extension, with two cases: first one, building without changing building material, while second one done after changing building material to save solar energy inside and rising roof slope up to 10%, and we achieve valuable energy savings by applying this modification to building material. And finally, introducing the infrastructure underground network, that serving transformation to smart city.

groups

Bushra Kheirbek mail -

Rana Maya mail

link https://doi.org/10.54216/IJBES.080103

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2024

Optimizing Loop Tiling in Computing Systems through Ensemble Machine Learning Techniques

This work investigates the use of ensemble machine-learning algorithms to optimize loop-tiling in computing systems, with the goal of improving performance by predicting optimal tile sizes. It compares two approaches: independent training and averaging (soft voting) and an ensemble technique (hard voting) that employs models such as linear regression, ridge regression, and random forests. Experiments on an Intel Core i7-8565U CPU with several benchmark programs revealed that the hard voting Ensemble Approach beat the soft voting technique, providing more dependable and accurate predictions across a range of computing environments. The hard voting technique reduced execution time by around 87.5% for dynamic features and 89.89% for static features, whereas the soft voting approach showed an average drop of 75.45% for dynamic features and 78.13% for static characteristics. This work demonstrates the effectiveness of hard voting ensemble machine learning approaches in improving cache efficiency and total execution time, opening the way for future advances in high-performance computing settings.

groups

NoorUlhuda S. Ahmed mail -

Esraa H. Alwan mail -

Ahmed B. M. Fanfakh mail

link https://doi.org/10.54216/FPA.150117

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2024

Using Fusion Data Analysis and Compensatory Fuzzy Logic to Analyze the Socioeconomic Impact of Venezuelan Migration

The surge of Venezuelan migration has left indelible marks on various regions, notably within Babahoyo Canton, presenting both challenges and opportunities for local communities. This study delves into the socio-economic impacts of Venezuelan migration on Babahoyo throughout 2023, employing a sophisticated blend of compensatory fuzzy logic and information fusion techniques. These methodologies offer a nuanced exploration of the migration's effects, capturing the complex interplay between local perceptions, labor market fluctuations, and broader economic dynamics. The findings underscore the critical need for comprehensive integration strategies that not only facilitate the socio-cultural adaptation of migrants but also leverage local public policies to mitigate adverse impacts while maximizing potential benefits. Ultimately, this research aims to illuminate pathways for informed decision-making and policy development, ensuring that responses to Venezuelan migration in Babahoyo are both effective and empathetic, thus fostering a more integrated and resilient community.

groups

Ignacio Barcos Arias mail -

Danna Villacres Castro mail -

Angelica Neira Toledo mail -

Fayzieva Muyassarzoda mail

link https://doi.org/10.54216/FPA.150118

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2023

A Neutrosophic Analysis of Ecuador's Traffic Injury Legislation

This study offers a comprehensive analysis of the perceptions regarding the gaps in traffic injury laws and the level of judicial security in Ecuador. By employing a neutrosophic approach, which incorporates degrees of truth, indeterminacy, and falsity, the study synthesizes data from legal professionals to assess the need for legislative reform. The use of the Indeterminate Likert Scale and the Triple Refined Indeterminate Neutrosophic Sets (TRINS) enriches the evaluation of nuanced opinions on traffic injuries that result in minimal disability. The resulting insights reveal a significant recognition of the inadequacies within the current legal framework and highlight a societal demand for enhanced legal measures and improved trust in judicial processes. This study underscores the urgency for legal reforms that would bridge the current legislative voids, ensuring justice and protection for traffic injury victims, thereby strengthening the legal system’s accountability and public confidence.

groups

Acurıo H. Germán Fabrıcıo mail -

Vıctor Lucero Salcedo mail -

Vıllacres D. Oscar Fabıan mail

link https://doi.org/10.54216/IJNS.200116

Volume & Issue

Vol. Volume 20 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2023

Information Fusion Analysis for Evaluating Educational Equity

The present study aims to analyze the situation of educational inequality in Ecuadorian rural students during the study period, by identifying the barriers and factors that affect their access to equitable and quality education. The results showed a marked digital educational gap between rural and urban students, aggravated by the lack of access to technological resources and connectivity in rural areas. The transition to virtual education during the pandemic exacerbated these inequalities, making it difficult for rural students to learn and limiting their integration into the education system. In conclusion, the need to implement comprehensive public policies to address these inequalities and promote educational equity is highlighted, considering the evaluations and weightings carried out through the MULTIMOORA method. For this, an investment in educational infrastructure, the provision of devices, and teacher training in educational technologies are required to improve access to quality education in rural areas. With this, the path towards a fairer and more inclusive future for all Ecuadorians is sought.

groups

Byron Javier C. Lema mail -

Iyo Alexis Cruz Piza mail -

María Elena I. Miranda mail -

Bekimbetova Gulnora mail

link https://doi.org/10.54216/FPA.120106

Volume & Issue

Vol. Volume 12 / Iss. Issue 1

Details open_in_new

Financial Technology and Innovation | 2024

The Fraud-Protection Paradox in Consumer Payments

Consumer-payment risk is hard to measure because instruments that have the highest incidence of reported fraud are often still in high usage and trusted. The key debate is when to break down the line between weak security and a protection ecosystem that will minimise the impact consumers suffer following an incident, through monitoring, liability allocation, dispute handling and recovery. This paper constructs a framework for risk alignment between instrument-level fraud incidence, perceived security, and realized payment use, and breaks down the difference between persistent instrument-level differences and short-run changes within instruments. Official U.S. estimates for cash, checks, debit cards, and credit cards are arranged in a balanced panel for the years 2015-2020 and an extended descriptive sample for 2022. Combined results of pooled regression, two-way fixed effects, first differences, and leave-one-year-out ridge validation to ascertain if fraud exposure can be predicted after accounting for stable instrument characteristics and common annual shocks. Regulators and payment providers need to address this measurement challenge since a narrow focus on incident counts can divert investment focus from detection, liability limits, recovery, and reimbursement capabilities that build consumer confidence and payment-system robustness. Credit-card payment share rose from 18.3% in 2015 to 31.3% in 2022 while 10.3% of adopters reported loss, theft, or fraud in 2022; the pooled fraud coefficient of 3.86 (p < .001) fell to 2.20 (p = .185) under instrument and year fixed effects, and lagged fraud did not predict annual share change (−0.27, p = .401). The validation using leave-one-year-out results in an R2 = .873 and MAE of 2.90 percentage points, whereas the instrument-history validation produces an MAE of 2.71.

groups

Taif Khalid Shakir mail -

Ahmed Al Masri mail

link https://doi.org/10.54216/FinTech-I.040201

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

Details open_in_new

Financial Technology and Innovation | 2024

Regime-Aware Digital-Asset Allocation: Balancing Participation and Downside Risk

The exposure of cryptocurrencies has been so easy to make a part of the digital-asset platforms, but the risk engines that come with these products are frequently based on static allocations or unconditional correlation estimates. The problem is that the states that allow participation in upside potential are different from the states that allow protection against sudden drawdowns; solving both optimization problems as a single problem can lead to a portfolio that retains too much crypto risk or to a portfolio with a zero digital asset exposure. In this paper, we introduce a participation constrained, regime aware allocation framework which allocates Bitcoin and Ether alongside gold and the S&P 500. A pre-specified volatility signal separates out the normal period from the stress period, and normal period minimum variance optimization enforces a significant crypto allocation during the normal period, whereas the stress period has an explicit ceiling on the amount of crypto invested. The design is tested without look-ahead bias over 2020–2023, assuming that there is a 10 basis points per unit of turnover charge for the design. Importance of the framework for FinTech investment platforms is that it converts a qualitative risk setting into allocation rules that are easily explainable, auditable, and repeatable. The volatility of the static crypto portfolio was 64.7%, the maximum drawdown was 72.1%, and the conditional loss at 5% monthly was 34.6% for the portfolio. A crypto-only strategy had an annualized return of 12.0% and reduced volatility to 16.3% and the 5% conditional loss to 20.1%, while a regime-aware strategy had an annualized return of 14.9% and reduced volatility to 25.9% and the 5% conditional loss to 14.5%.

groups

Andino Maseleno mail -

Aa Hubur mail

link https://doi.org/10.54216/FinTech-I.040202

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

Details open_in_new

International Journal of Wireless and Ad Hoc Communication | 2024

Enhancing Wireless Sensor Network Lifetime through Energy-Efficient Data Clustering and Compressed Forwarding in video Processing

Wireless Sensor Networks (WSN) play a crucial role in diverse data gathering applications, but face a significant challenge in the form of limited energy reserves within sensor nodes. Enhancing the network's Quality of Service, particularly its lifetime, is paramount. Prolonging the network's operational span hinges on mitigating energy consumption, with communication accounting for a substantial portion of nodal power usage. By reducing data transmission, not only can energy consumption be curtailed, but also bandwidth requirements and network congestion can be minimized. In the context of Wireless Sensor Networks, the Distributed Similarity-based Clustering and Compressed Forwarding (DSCCF) approach strives to construct data-similar iso-clusters with minimal communication overhead. This technique involves extracting trend and magnitude components from lengthy data series using an LMS filter, resulting in what is termed "data projection." Data similarity between nodes is assessed by measuring the Euclidean distance between these data projections, thereby facilitating efficient and low-overhead iso-cluster formation. To further economize intra-cluster communication, an adaptive-nLMS-based dual prediction framework is employed. During each data collection round, the cluster head holds instantaneous data for each cluster member, using either prediction or direct data communication. Furthermore, inter-cluster data is reduced via a multi-level lossless compressive forwarding technique. Impressively, this proposed approach has achieved an 80% reduction in data while maintaining optimal data accuracy for the collected information. The transmission of inter-cluster data exclusively occurs through a network backbone comprised solely of cluster heads. Initially, the cluster heads establish this network backbone. Each cluster head dispatches a link request query towards the sink through the backbone, receiving a link reply message containing path length and the weakest link of the path. The cluster head repeats this process for each available path, subsequently selecting the most optimal path based on the acquired information and its reliability in terms of link quality

groups

Jayasudha A. R. mail -

Ramya S. mail -

Vairaprakash S. mail -

N. Kannaiya Raja mail

link https://doi.org/10.54216/IJWAC.080201

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new