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Journal for Technology and Science
Published by Antis Publisher
ISSN : -     EISSN : 30474337     DOI : https://doi.org/10.61796/ipteks.v1i1
Core Subject : Engineering,
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering
The Journal for Technology and Science published by Antis Publisher eISSN 3047-4337 is a scholarly journal that focuses on original research articles in natural science and technology relevant to industries and communities in developing countries. Released annually in March, August, and November, it is inclusive of scientists, researchers, educators, and scholars. The journals scope encompasses various topics addressing current challenges encountered by industries, governments, and communities in developing nations. With a commitment to advancing knowledge and fostering innovation, The Journal for Technology and Science welcomes submissions that contribute to the advancement of science, technology, and their applications in addressing societal needs and promoting sustainable development in emerging economies
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 82 Documents
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PREPARATION AND PHYSICOCHEMICAL ANALYSIS OF POLYMER COMPOSITES FROM RECYCLED PET WASTE Norbutayev, S. Q.; Tavashov, Sh. Kh.
Journal for Technology and Science Vol. 3 No. 2 (2026): Journal for Technology and Science
Publisher : PT ANTIS INTERNATIONAL PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/ipteks.v3i2.476

Abstract

Objective: This study investigates the physicochemical properties of polymer composite materials based on recycled PET and CaCO3. Method: The thickness of the samples was selected in compliance with the standard requirements: 3,49 mm for sample 1, 3,43 mm for sample 2, and 3,91 mm for sample 3.The experiment was initiated at an ambient temperature of 22,5°C, with a controlled heating rate of 50°C per hour. Under an applied load of 10 N, the penetration of the needle to a depth of 1 mm for the first sample was achieved after 118,68 minutes, corresponding to a temperature of 152,4°C. Results: The results indicate that optimal performance is achieved at 5–15% CaCO3, demonstrating the potential of recycled PET composites for cost-effective industrial applications. Novelty: The results indicate that optimal performance is achieved at 5–15% CaCO3, demonstrating the potential of recycled PET composites for cost-effective industrial applications.
INVESTIGATION OF ZINC HYDROXIDE CARBONATE FORMATION FROM ZINC NITRATE SOLUTION BY SODIUM CARBONATE PRECIPITATION Hamdamov, A. A.; Ochilov , J.M.; Tavashov , Sh.Kh.
Journal for Technology and Science Vol. 3 No. 2 (2026): Journal for Technology and Science
Publisher : PT ANTIS INTERNATIONAL PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/ipteks.v3i2.477

Abstract

Objective: This study presents the results of zinc hydroxide carbonate production through precipitation from a zinc nitrate solution using an 18% sodium carbonate solution. Method: The effects of key process parameters on the precipitation efficiency were systematically examined, and optimal operating conditions were determined. Results: The initial zinc nitrate solution contained 13,16% ZnO. The highest precipitation efficiency of zinc hydroxide carbonate was achieved within a pH range of 7,9–8,3 and at a temperature of 65–70°C. Additionally, extending the process duration from 40 to 45 minutes led to an increase in the precipitation degree from 98,79% to 99,96%. Novelty: The effects of key process parameters on the precipitation efficiency were systematically examined, and optimal operating conditions were determined.
ARTIFICIAL INTELLIGENCE DRIVEN INFRASTRUCTURE SECURITY ENHANCING CYBERSECURITY AND PROTECTING NATIONAL SECURITY SYSTEMS Chowdhury Amin Abdullah; Md Jahidul Islam Ridoy
Journal for Technology and Science Vol. 1 No. 3 (2024): Journal for Technology and Science
Publisher : PT ANTIS INTERNATIONAL PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/ipteks.v1i3.498

Abstract

Objective: Artificial Intelligence (AI) functions as a fundamental technological solution which defends national security systems through improved cybersecurity infrastructure against sophisticated cyber threats. The fast growth of digital technology has made vital infrastructure systems vulnerable to cyber threats which include phishing attacks and ransomware and malware infections and data breach incidents. Method: The research used a quantitative survey-based design which collected data from 175 participants worked as cybersecurity professionals and IT experts and government officials and academic researchers. Descriptive statistics to analyze data through frequency and percentage and mean and standard deviation and ranking analysis and Pearson correlation which studied the connection between AI adoption and cybersecurity performance indicators. Results: The study found that 81.1% of participants selected phishing attacks as their primary security risk while ransomware attacks received 77.7% and data breaches obtained 73.7% of the votes. Correlation data revealed that countries which adopt AI technology tend to experience better national security results with r = 0.77 and improved threat detection with r = 0.74 and stronger data protection with r = 0.69. The system provides multiple benefits to users but users encounter three major obstacles which include privacy concerns at 74.3% and insufficient qualified staff at 71.4% and costly setup expenses at 69.1%. Novelty: AI functions as a fundamental element which enhances both cybersecurity systems and protects national security interests.
ADOPTION OF DIGITAL TECHNOLOGIES IN THE HIGHER EDUCATION Abdukadirova Xalida Abduxamedovna; Mukumova Nargis Nuriddinovna
Journal for Technology and Science Vol. 3 No. 3 (2026): Journal for Technology and Science
Publisher : PT ANTIS INTERNATIONAL PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/ipteks.v3i3.502

Abstract

Objective: This article examines the digital transformation of higher education institutions and their impact on the development of distance education. It analyzes modern approaches to defining the concepts of "university digitalization" and "distance learning technologies," identifying key drivers and barriers to the implementation of digital solutions in the educational process. Method: Based on an analysis of domestic and international experience, the author identifies key areas of digitalization: the implementation of electronic educational platforms, automation of educational process management, and the use of artificial intelligence and big data technologies. Results: Particular attention is paid to the changing roles of teachers and students in the context of the transition to hybrid and fully online learning formats. Novelty: The article identifies key areas of digitalization: the implementation of electronic educational platforms, automation of educational process management, and the use of artificial intelligence and big data technologies.
REVOLUTIONIZING CARDIOVASCULAR CARE: AN AI-DRIVEN APPROACH TO EARLY INTERVENTION Hussein Ali Al-jashamy; Hashim Adnan; Hussein Majid
Journal for Technology and Science Vol. 3 No. 3 (2026): Journal for Technology and Science
Publisher : PT ANTIS INTERNATIONAL PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/ipteks.v3i3.505

Abstract

Objective: Cardiovascular diseases (CVDs) continue to be a primary cause of early death globally, with both their prevalence and the costs associated with healthcare consistently increasing. Epidemiological Researches has pinpointed a range of risk factors, including high cholesterol levels, elevated blood pressure, diabetes, obesity, smoking, and lack of physical activity, which together account for more than 90% of the risk linked to CVDs. The integration of artificial intelligence (AI) into healthcare has revolutionized medical diagnosis and treatment, particularly in the field of cardiology. Natural Language Processing (NLP) algorithms further enhance this by converting unstructured clinical notes into structured data, thus supporting clinical decision-making processes. This study explores the implementation of both traditional machine learning methods—such as Decision Trees (DT), Multilayer Perceptron (MLP)and advanced deep learning techniques in conjunction with NLP to diagnose heart conditions requiring catheter intervention. Method: This study explores the implementation of both traditional machine learning methods—such as Decision Trees (DT), Multilayer Perceptron (MLP)and advanced deep learning techniques in conjunction with NLP to diagnose heart conditions requiring catheter intervention. Results: Our findings suggest that the hybrid model employing deep learning methods outperforms traditional models, demonstrating the potential of AI in advancing cardiovascular healthcare. Novelty: Our findings suggest that the hybrid model employing deep learning methods outperforms traditional models, demonstrating the potential of AI in advancing cardiovascular healthcare.
A STUDY ON THE DIAGNOSIS OF CERVICAL MAGNETIC RESONANCE IMAGING USING VARIOUS TECHNIQUES Tahseen Falih Mahdi
Journal for Technology and Science Vol. 3 No. 3 (2026): Journal for Technology and Science
Publisher : PT ANTIS INTERNATIONAL PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/ipteks.v3i3.508

Abstract

Objective: In this study, magnetic resonance images (MRIs) of the neck were examined and enhanced to help doctors clearly interpret and analyse the condition visible in the MRI scans. Method: Various techniques were employed to improve image quality and clarity, ultimately producing an excellent image with significantly clearer information compared to the original scan, These techniques lend a degree of clarity to the information provided by the image. Results: The results of the enhancements applied were significantly more positive compared to previous studies and the application of such techniques. Novelty: Furthermore, the success of these enhancements represents an important field and a focus of interest in many areas, particularly medicine, which is the most significant field.
EXPERIMENTAL CHARACTERISATION OF 802.11A/G INDOOR PROPAGATION PATTERNS Hasanain A. H. Al-Behadili
Journal for Technology and Science Vol. 3 No. 3 (2026): Journal for Technology and Science
Publisher : PT ANTIS INTERNATIONAL PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/ipteks.v3i3.510

Abstract

Objective: The technology of wireless local area network (WLAN) has developed rapidly, and it has become an interesting research topic. The knowledge of the radio propagation characteristics and the environmental information, including the layout of the rooms, the construction materials, and furniture arrangement, is a prerequisite for designing and developing the indoor wireless communication system. So, an exhaustive measurement is essential for studying the radio propagation characteristics. This thesis centres on the path loss for 2.4GHz and 5GHz frequency bands used in wireless Local area network (WLAN), 802.11a and 802.11g standards. Method: Measurements under LOS and OBS environments were conducted, and path loss was calculated. The best fit plots to the measured results are presented. The results from simulations by various models are also examined, and a comparison concerning the accuracy of the models is made. The penetrating loss for some typical partitions is also measured. Results: Some findings are concluded. It is found that the 802.11a 5 GHz frequency band actually suffers more path loss than the 802.11g 2.4 GHz frequency band. In a complex indoor environment, the difference is diminished since various reflections, deflections, and scatter all contribute to signal propagation. Novelty: The results from simulations by various models are also examined, and a comparison concerning the accuracy of the models is made. The penetrating loss for some typical partitions is also measured.
SPECTRAL DECOMPOSITION STRATEGIES FOR ANOMALOUS DIFFUSION AND IMPULSIVE BOUNDARY VALUE SYSTEMS Nada Abdul-Hassan Atiyah
Journal for Technology and Science Vol. 3 No. 3 (2026): Journal for Technology and Science
Publisher : PT ANTIS INTERNATIONAL PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/ipteks.v3i3.512

Abstract

Objective: Physical models with memory or impulsive effects of non-local nature present complications mathematically. Method: To circumvent such computational challenges, a frequency domain approach is explored herein. The new algorithm allows one to deal easily with fractional derivatives and strong singularities by reducing the problem of solving the integro-differential equation to the solution of an algebraic problem. Results: Numerical analysis suggests that the transition to the frequency domain is indeed valid but exhibits a noticeable reduction in rate of convergence, which goes from O(N⁻²) in normal situations to O(N⁻ᵅ) for the anomalous case. We further consider sustained oscillations induced by impulse-like forcing. Novelty: The traditional approach using finite difference method is known to have stability issues when dealing with anomalous diffusion as well as impulses like Dirac delta functions.
EXPLAINABLE MACHINE LEARNING-BASED MALWARE DETECTION USING PORTABLE EXECUTABLE STRUCTURAL FEATURES AND HYBRID VOTING ENSEMBLE Haider Ali Muften; Murtadha Flieh Hassan
Journal for Technology and Science Vol. 3 No. 3 (2026): Journal for Technology and Science
Publisher : PT ANTIS INTERNATIONAL PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/ipteks.v3i3.513

Abstract

Objective: While evolving technologies have introduced advanced threat intelligence, traditional threats such as malware attacks continue to be a potential risk for contemporary computer systems and cybersecurity infrastructures and thus, it is of utmost importance that intelligent methods are developed in order to detect malware. Detecting malware variants is very difficult for traditional signature-based detection methods, especially for new and advanced malware. Thus, in this research work involves an explainable hybrid ensemble framework on machine learning approach with utilizing of Portable Executables (PE) file features for malware detection. Method: Our dataset is composed of 62,485 executable samples on the pre-processed and feature cleaning PE structures reduced to a domain matrix into 16 numerical features. We have implemented and evaluated multiple machine learning algorithms such as, Logistic Regression, Decision Tree, Random Forests, Support Vector Machine and Extreme Gradient Boosting. Voting Classifier was created for a robustness against classification and detection in addition to the hybrid ensemble model. Furthermore, we utilized SHAP analysis to understand model predictions and detect the most important features that contribute to malware classification. Results: Output from our practical experiments demonstrated some superior scores on all types of metrics measured. The Random Forest classifier has the best accuracy (99.64%) and the proposed Hybrid Voting model achieved an accuracy of 99.53%, precision: 99.68, recall: 99.24 and F1-score of 99.46 which confirms that our algorithm yielded very strong rumors and results are strongly stable across all compared datasets as well as high stability between individual classifiers (Fig From this analysis, we found that Dll Characteristics, Debug Size and Debug RVA stood out as the most significant features overall for our malware detection task, which showed how informative variations in executable structural characteristics can be to classification. Novelty: The findings demonstrate the potential of using ensemble learning in conjunction with explainable artificial intelligence techniques to enhance malware detection systems and cybersecurity applications.
ANALYSIS ON THE USES OF GEO-SYNTHETICS TO STABILIZE SANDY SOIL SUB GRADE Ali A. Al-Zubaidi; Rand Otbah Farqad
Journal for Technology and Science Vol. 3 No. 3 (2026): Journal for Technology and Science
Publisher : PT ANTIS INTERNATIONAL PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/ipteks.v3i3.515

Abstract

Objective: The main goal of this study is to compare how well synthetic and natural geo-textiles work to stabilise sub-grade soil. Method: This study looks at what happens when geo-textiles are used to strengthen the soil under a building. In the lab, the load-penetration abilities of granular soils that had been strengthened with geo-textiles were tested using California bearing ratio (CBR) tests. Results: By using geotextiles, the soil's ability to hold water can be fixed. Geosynthetics are often used to improve road pavement, among other things. Novelty: Soil stabilisation methods include stabilising the soil with cement, lime, hydrocarbons, chemicals, and the new and promising method of stabilising the soil with Geo textiles and Geo artificial fibres.

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