Modelling an Improved Swarm Optimizer and Boosted Quantile Estimator For Malicious Flow Monitoring And Prediction In Network

U. Harita; Moulana Mohammed

doi:https://doi.org/10.54216/JCIM.130210

American Journal of Business and Operations Research | 2027

When Tariff Averages Misrank Markets: A Business Intelligence Audit of WTO 2025 Profiles

Procurement teams often rank destination markets using a single published tariff average. Whether that ordering survives a change of measurement is a practical question for business intelligence. We audit the 2025 World Tariff Profiles, retaining 101 economies with matching 2024 most-favoured-nation (MFN) applied tariffs, reported trade-weighted tariffs, and 2023 import totals. A reproducible screen compares simple and trade-weighted averages, ranking changes, duty-free subheading shares, and the share of subheadings facing duties above 15%. The median simple average is 7.5% and the median trade-weighted average is 6.9%; the within-economy weighted-minus-simple median is −0.6 percentage points. Thirty-five economies differ by at least two points. With ties retained at each top-25 cutoff, 28 economies qualify under the simple rate and 26 under the weighted rate, but only 20 qualify under both. Türkiye falls from 17.3% to 7.0%, whereas Dominica rises from 10.7% to 15.9%. Disagreement persists among larger import markets: 13 of 31 economies with at least US$50 billion in 2023 imports have a gap of two points or more. A layered dashboard should therefore trigger product-level verification where aggregate measures disagree. Neither average identifies the duty paid by a particular firm or the effect of changing trade policy.

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Seyfettin Artan mail

link https://doi.org/10.54216/AJBOR.150101

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

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American Journal of Business and Operations Research | 2027

Purpose-Specific Financial Analytics for Entrepreneurial Credit Market Screening

A lender choosing markets for small-enterprise finance may use account ownership as a shortcut for credit demand. This article asks whether that shortcut identifies places where adults report borrowing to start or operate a business. We analyze the 2024 country estimates in the World Bank’s Global Findex Database 2025: 98 economies with complete business borrowing, account, formal-channel borrowing and formal saving measures. Account ownership and business borrowing are negatively associated across those economies (Spearman ρ = −0.220). An account-only model has a leave-one-economy-out mean absolute error of 5.529 percentage points, close to a constant forecast (5.580); adding formal-channel borrowing barely changes it. Adding formal saving reduces error to 4.700 points, although individual country errors remain substantial. Fifteen economies fall simultaneously above the sample’s upper quartile of business borrowing and below its median account ownership. In 40 economies with paired gender estimates, men have higher business borrowing in 19 and women in 21; account gender gaps do not reproduce a uniform enterprise-credit gap. These are country-level diagnostics of reported borrowing, not loan applications, unmet credit demand or evidence that account access causes enterprise borrowing. A staged market screen should therefore use purpose-specific borrowing and validate borrower quality and local lending rules before capital allocation.

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Samandarboy Sulaymanov mail -

Olimjonov Abbosjon Olimjonovich mail

link https://doi.org/10.54216/AJBOR.150102

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2024

On the Development of Fuzzy Estimators for Life Time Distributions based on Censored Fuzzy Life Times

Lifetime analyses comprise the techniques dealing with observations obtained from the occurrence of a specified event(s). In most of the situations dealing with lifetime observations, some units are recorded as censored observations. Dealing with censored observations makes these techniques unique. Countless standard statistical tools are available for inference based on censored lifetime observations. These classical techniques consider lifetime observations as precise numbers and ignore the uncertainty of single observations. Whereas in practical applications it is not possible to measure life times as precise numbers, they are always more or less nonprecise. The imprecision in measurements can be covered by neutrosophic set. Fuzzy estimators for life time distributions potentially use neutrosophic system to model and analyze the inherent uncertainties and neutalities present in the data and the parameter estimates. This study aimed to obtain estimators for the Weibull parameters and two exponential parameters based on the up-to-date fuzzy number approach, a special case for neutrosophic set. The suggested estimators incorporate fuzziness in addition to random variation, which makes these estimators more realistic. The same techniques need to be extended to fuzzy and neutrosophic sets.

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Mohammad Abiad mail -

Muhammad Shafiq mail -

Syed Habib Shah mail -

Muhammad Atif mail

link https://doi.org/10.54216/IJNS.240217

Volume & Issue

Vol. Volume 24 / Iss. Issue 2

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Journal of Neutrosophic and Fuzzy Systems | 2024

On Some Novel Generalizations of Weak Fuzzy Complex Numbers

The ring of weak fuzzy complex numbers is an extension of real numbers ring by using an algebraic element with fuzzy property. In this paper, we present two novel generalizations of weak fuzzy complex numbers, where the concepts of strong fuzzy complex numbers and split-complex weak fuzzy complex numbers will be defined for the first time with a general study of their elementary properties and special elements. On the other hand, we provide an algorithm to compute the dempotent elements in the ring of split-complex weak fuzzy complex numbers with many related examples that clarify the validity of our work.

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Zahraa Hasan mail -

Rama Asad Nadweh mail

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

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new

Journal of Neutrosophic and Fuzzy Systems | 2024

On Some Novel Results About Weak Fuzzy Complex Integers

This paper is dedicated to studying for the first time the concept of weak fuzzy complex integers division and units, where we present a full classification of weak fuzzy complex integer units with necessary and sufficient conditions for division in the set of weak fuzzy complex integers. On the other hand, we provide an algorithm for solving weak fuzzy complex linear congruencies with many related examples that explain how algorithms work.

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Mohammad Abobala mail

link https://doi.org/10.54216/JNFS.080202

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2024

Turiyam Based Four Way Unknown Profile Characterization on Social Networks

Recently Turiyam set and its algebra is introduced for dealing with the data containing human quantum cognition. This paper tries to extend the applications of Turiyam set while profile characterization at Social Network. To achieve this goal, a method is proposed for characterization of social network profiles in true regions (t), false region (f), Neutro region (i) and Liberal or Turiyam region (l), independently. The proposed method also provides implementation to understand the concept of Turiyam logic. In addition, many real life examples for data with Turiyam attributes is given with its four way characterization.

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Prem Kumar Singh mail -

Naveen Surathu mail -

Ghattamaneni Surya Prakash mail

link https://doi.org/10.54216/GJMSA.0100203

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2024

Fused and Cascaded Squeeze Excitation Network for Pneumonia Detection

Pneumonia is a medical condition affecting 100 million people globally, and rates are predicted to reach epidemic levels within the next several decades. As a result of the air sacs in both or even one lung becoming inflamed, the patient may experience fever, chills, and trouble breathing. Coughs with pus may also occur. Various organisms can cause pneumonia, including bacteria, viruses, and fungi. Early detection of pneumonia can allow the severity of the purulent material to be reduced. The ability of computer-aided detection techniques to reliably diagnose pneumonia has made them popular among scientists. We used a pre-trained Inception V3Net, Squeeze Excitation-based deep Convolutional Neural Network (SE-CNN) that was trained on the Kermany dataset and the RSNA Pneumonia Detection Challenge dataset in this study. In early-stage detection, the suggested technique beat previous state-of-the-art networks, achieving 91% precision in severity rating. Furthermore, our network's accuracy, recall, f1-score, as well as quadratic weighted kappa were reported to be 91.56%, 91%, and 90%, respectively. In terms of processing time and space, our suggested framework is simple, precise, and effective.

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Ramitha M. A. mail -

N. Mohanasundaram mail -

R. Santhosh mail

link https://doi.org/10.54216/FPA.160110

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

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Journal of Cybersecurity and Information Management | 2024

Link-Based Xcorr Normalization and Attention Mechanism for Predicting the Threats over the Network Model

Sensor Networks (SNs) play an essential role in upcoming technologies like the Internet of Things (IoT), where technical services are highly prone to crucial vulnerability due to attacks. This research motivates to provide a mechanism to identify the link reliability of connected sensor nodes. The privacy-preserving keys are distributed among the corresponding network nodes. When the nodes suffer from an attack, it damages the linking nodes' community. It has the nature of healing itself when the attacks are identified over the network. The self-healing nature is not so complex, and it is termed a lightweight process. A novel link-based intrusion prediction mechanism uses attention-based Deep Neural Networks (-DNN) for lightweight linkage identification and labelling. This model helps predict basic network patterns using topological analysis with better generalization. The simulation is done with Python where the proposed -DNN model outperforms the five different conventional approaches with the adoption of a benchmark dataset (network traffic) for extensive analysis. The AUC is improved in an average manner with the adoption of -DNN. This model enhances the linkage connectivity to make different connectivity processes more efficient and reach the target non-convincing. It is sensed that the proposed -DNN outperforms the existing approaches by improving the network resilience by maintaining higher energy efficiency.

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V. Jemmy Joyce mail -

K. Rebecca Jebaseeli Edna mail -

P. Sherubha mail -

Arivazhagi mail

link https://doi.org/10.54216/JCIM.130208

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

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Journal of Cybersecurity and Information Management | 2024

Hybridization of Deep Sequential Network for Emotion Recognition Using Unconstraint Video Analysis

The reliable way to discern human emotions in various circumstances has been proven to be through facial expressions. Facial expression recognition (FER) has emerged as a research topic to identify various essential emotions in the present exponential rise in research for emotion detection. Happiness is one of these basic emotions everyone may experience, and facial expressions are better at detecting it than other emotion-measuring methods. Most techniques have been designed to recognize various emotions to achieve the highest level of general precision. Maximizing the recognition accuracy for a particular emotion is challenging for researchers. Some techniques exist to identify a single happy mood recorded in unrestricted video. Still, they are all limited by the processing of extreme head posture fluctuations that they need to consider, and their accuracy still needs to be improved. This research proposes a novel hybrid facial emotion recognition using unconstraint video to improve accuracy. Here, a Deep Belief Network (DBN) with long short-term memory (LSTM) is employed to extract dynamic data from the video frames. The experiments conducted uses decision-level and feature-level fusion techniques are applied unconstrained video dataset. The outcomes show that the proposed hybrid approach may be more precise than some existing facial expression models.

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P. Naga Bhushanam mail -

Selva Kumar S. mail

link https://doi.org/10.54216/JCIM.130209

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2024

Modelling an Improved Swarm Optimizer and Boosted Quantile Estimator For Malicious Flow Monitoring And Prediction In Network

For a long time, malware has posed a significant risk to computer system security. The effectiveness of conventional detection techniques based on static and dynamic analysis is restricted due to the quick advancement of anti-detection technologies. In recent years, AI-based malware detection has increasingly been employed to combat malware due to its improved predictive ability. Unfortunately, because malware may be so diverse, it can be challenging to extract features from it, which makes using AI for malware detection ineffective. A malware classifier based on an Improved Salp Swarm optimization for feature selection and a Boosted tree with Conditional Quantile Estimation (ISSO-BCQE) is developed to adapt different malware properties to solve the problem. Specifically, the malware code is extracted, and the feature sequence is generated into a boosting tree where the feature map of the node is extracted using BCQE, where a boosting network is used to design a classifier and the method's performance is finally analyzed and compared. The results show that our model works better than other approaches regarding FPR and accuracy. It also shows that the method beats current methods with the highest accuracy of 99.6% in most detecting circumstances. It is also stable in handling malware growth and evolution.

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U. Harita mail -

Moulana Mohammed mail

link https://doi.org/10.54216/JCIM.130210

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new