Innovative: International Multidisciplinary Journal of Applied Technology (2995-486X)
Innovative: International Multidisciplinary Journal of Applied Technology (IMJAT) with U.S ISSN 2995-486X is a peer-reviewed, open-access scientific journal dedicated to publishing original research and innovative developments across a broad spectrum of applied and technical sciences. With its growing reputation, IMJAT continues to be a valuable platform for academics, researchers, engineers, and industry professionals seeking to publish impactful research that advances applied science and technology on a global scale. We are pleased to announce that IMJAT has been officially recognized by the Higher Attestation Commission (OAK) of the Republic of Uzbekistan for meeting the criteria required for technical sciences publications. As per OAK Decision No. 368/4 dated March 6, 2025, the journal is now listed as a recommended scientific publication for “05.00.00 – Technical Sciences, making it a valid outlet for PhD and DSc researchers to publish their dissertation-related scientific results.
APPLYING PRINCIPLES OF GENERAL PHYSICS TO DESIGN EFFECTIVE STRATEGIES FOR PLANT DISEASE PREVENTION AND ENHANCEMENT OF AGRICULTURAL PRODUCTION
The significant impact of ionizing radiation and physical factors on vegetative growth and pest resistance of various plant species underlies the development of their ability to exist and grow in contaminated areas, space conditions and unfavourable agricultural environment. Exposure to radiation from cosmic rays, nuclear accidents, or radioactive substrates causes the morphological, physiological, and biochemical changes that shapes species-specific tolerance windows, but previous studies failed to reliably extrapolate radiations and physical conditions such as temperature, humidity, and light. While radiation effects are well documented, the combined influence of multiple physical factors with γ- and X-ray exposures, and their role in growth–defense trade-offs across species with different architectural and physiological traits have not been well characterized. This research examines vegetative development, morphological and anatomical responses, photosynthetic performance and pests naturally occurring at the most susceptible entities of the targeted plant species under controlled doses of ionizing radiation coupled with modulated physical factors, such as mechanical stress and soil differences. Results show variable effects on biomass accumulation or suppression depending on the tested species, as well as structural alterations of stems and leaves, induced efficiency of chlorophyll, radiation-triggered defensive pathways leading to secondary metabolites production and pest pressure reduction. Evidence of growth defense trade-offs, with some species increasing resistance but at the cost of reduced vegetative growth. This study systematically integrates the effects of radiation and other abiotic components of the environment–showing how combined effects of abiotic inputs alter growth trajectories, resistance traits, as well as patterns of physiological allocation. These findings provide a basis for enhancing crop resilience, pest-management strategies, and the potentially relevant agricultural and ecological practices in radiation-affected or stress-microbe intensive systems.
Copyright (c) 2025 Innovative: International Multidisciplinary Journal of Applied Technology (2995-486X)
Artificial intelligence (AI) is a collection of various technologies and algorithms built to minimize human intervention and maximize results, saving time for businesses and customers. It is transforming ecommerce by enhancing customer experiences and optimizing business operations through data analysis, automation, and personalization. It is making an enormous impact on ecommerce businesses. It personalizes the shopping experience through dynamic recommendations and AI-powered chatbots, automates operations like customer service and supply chain management, and improves marketing with advanced customer segmentation and content generation. AI in ecommerce has revolutionized the industry, providing powerful tools to enhance customer experiences, streamline operations, and tackle various challenges. The significance of AI integration with ecommerce sites cannot be understated. This paper explores some of the essential AI tools transforming ecommerce.
RESEARCH ON THE MANUFACTURING TECHNOLOGY OF SPECIAL FOOTWEAR
In sectors such as medical, military, and industrial where the working environment is dangerous, special footwear is crucial to maintaining safety and comfort. With evolving industries, the need for specialized footwear to protect from mechanical, thermal, chemical, and electrical risks are on the rise. Integration of modern technologies in production methods such as CAD, 3D modeling, and smart Quality Control has become imperative. While considerable knowledge exists regarding special footwear technologies in developed economies, less attention has been drawn to the upgrading of production methods in transitional areas such as Central Asia. The purpose of this oeuvre is to study the technological processes that are used in the manufacturing of special shoes, and estimating the affect that modern interfacing, guidelines of ergonomic frameworks and the producing process have on their restoration. Use of materials such as polyurethane and thermoplastic polyurethane with improved ergonomic designs for better comfort and reduced fatigue among users are identified. The other highlights automation technologies for manufacturing, as well as conservation practices, integration of ag products and production technologies. This research expands the existing literature on sustainable and ergonomic manufacturing technology for special footwear, and the data generated would be valuable for Uzbekistan, located in Central Asia, and one of the countries in the region, having its textile and light industry sector to benefit from it. These results have implications on improving the international competitiveness of the special footwear sector through innovation in safety, comfort, and environmental sustainability, while serving as a model for the technological modernization in developing areas.
Artificial intelligence (AI) refers to the use of computers and technology to simulate intelligent behavior and critical thinking comparable to a human being. It is an interdisciplinary field that integrates computer science, mathematics, and related disciplines to create algorithms that can perform tasks conventionally restricted to human intelligence. It has the potential to transform healthcare and disrupt the field of medicine in significant ways. AI technologies such as machine learning, natural language processing, and robotics can help improve healthcare by enhancing medical services and research efficiency. Artificial intelligence techniques have the potential to be applied in almost every field of medicine. They have the potential to transform medicine. In this paper, you will explore the transformative impact of artificial intelligence on medicine.
A Unique Method for Concealing Information
The subject of steganography is the means and methods that ensure the concealment of the fact of message transmission. The historical roots of this discipline are very deep. With the development of technology, the methods have also changed. If previously invisible ink was used between the lines of ordinary text, then modern digital steganography consists of embedding data in digital objects with minimal distortion. This is possible due to the use of the redundancy of the container data or the peculiarities of human perception, which is unable to notice these changes. In light of the widespread use of text documents in organizational activities, methods of hiding data in a text container, in particular, in MS Word documents, are becoming especially relevant. Based on this, an MS Word document is considered as an information carrier here. MS Word documents have different parameters, by changing these parameters or properties, it is possible to achieve data embedding. In this article, we present steganography using invisible Unicode characters of the Space type, but with different encoding. /2
Integration of Material Physics and Optics in the Development of Modern Laser Technologies for Diagnostic and Therapeutic Medical Devices
Accurate medical diagnostics and effective therapeutic interventions that maximize safety and minimize discomfort potentially offer enormous human and financial benefits. By integrating the physical science of materials with the art of optics and their engineering, a new generation of laser-based diagnostic-imaging and therapeutic systems, targeting biomedically relevant anatomical and functional tissue features, is possible. Material-property characterizations that predict the extent of anatomical scattering or determine whether tissue is likely to fluoresce are needed for designing laser-based medical diagnostic systems. Related material-property characterizations enable similar laser-based therapeutic procedures. Targeted coupling between material physics, optics and biomedically relevant tissue enables technological advances towards both earlier-stage and more effective medical diagnostic and therapeutic procedures. Diagnostics involving laser-based imaging techniques, light-tissue interactions, and tissue responses are described, followed by corresponding therapeutic procedures and desired material-property characterizations.
ANALYSIS OF A DENSITY-DEPENDENT MULTIDIMENSIONAL CROSS-DIFFUSION MODEL WITH NONLINEAR BOUNDARY CONDITIONS
Recently, the cross-diffusion problem has gained importance in solving problems of mathematical modelling of complex processes such as population dynamics, diffusion in multiphase media and reaction-diffusion systems and therefore has attracted considerable attention. Studies have shown that cross-diffusion elements in models can significantly change the qualitative and quantitative properties of solutions. However, many aspects of solutions, especially nonlinear boundary value problems, have not been sufficiently studied, which requires further and more in-depth study of these issues and creates the need for a more in-depth theoretical analysis. Based on the above considerations, the objective of this study is to formulate and analyse the scientific problem associated with the dynamic behaviour of the cross-diffusion problem based on the conditions of existence and non-existence of a global solution and the influence of boundary and parametric conditions on solutions based on self-similar analysis. The research methodology is based on the use of a self-similar approach, which allows simplifying the system of differential equations by introducing variables that are invariant with respect to changes in measurements. The main conclusion of the work is that the use of self-similar variables not only significantly simplifies the study of complex cross-diffusion models but also allows us to obtain important information about the stability limits of solutions.
self-similar analysis nonlinear parabolic system
critical exponents of the Fujita type, blow-up, existence, non-existence
DESIGN AND IMPLEMENTATION OF AN INTELLIGENT SYSTEM FOR TEMPERATURE AND VENTILATION CONTROL IN INFANT INCUBATOR
An infant incubator is a piece of equipment common to pediatric hospitals, birthing centers and neonatal intensive care units. While the unit may serve several specific functions, it is generally used to provide a safe and stable environment for newborn infants, often those who were born prematurely or with an illness or disability that makes them especially vulnerable for the first several months of life. Perhaps the most obvious function of an infant incubator is to protect infants during the earliest stage of life, when they're most vulnerable. As fully enclosed and controllable environments, incubators can be used to protect babies from a wide range of possible dangers. Incubators are fully temperature controlled, shielding infants from harmful cold, and they provide insulation from outside noise, making it easier for them to get plenty of comfortable rest. Incubator environments can be kept sterile, protecting infants from germs and minimizing the risk of infection. The enclosure also keeps out all airborne irritants like dust and other allergens. The cradle of the incubator is a roomy and comfortable surface, so it's possible to leave the infant in place while many examinations and even simple medical procedures are administered. This protects infants from too much handling, which can be a concern in the case of some premature births.
DESIGN AND IMPLEMENTATION OF A MEDICAL DEVICE BASED ON ELECTRICAL STIMULATION TO ACCELERATE WOUND HEALING
This research project focuses on designing and implementing a device that utilizes electrical stimulation to accelerate wound healing. By applying mild and continuous electrical currents, the device stimulates cellular growth, enhances blood circulation, and promotes faster tissue regeneration. The results demonstrated a high response speed and an accuracy rate of approximately 90%, making the device effective for therapeutic use with low cost and ease of operation. Despite minor challenges such as sensitivity to rapid movements and glove fitting issues, the prototype proved reliable and efficient. This project opens promising prospects for further development, such as expanding the range of recognized signals, integrating wireless connectivity, and adding voice output features, thereby contributing significantly to improving the quality of life for patients with chronic wounds.
Genetic Algorithm–Based Hyperparameter Optimization for 1D-CNN NIDS
This paper investigates Genetic Algorithm (GA)–driven hyperparameter optimization for one-dimensional Convolutional Neural Networks (1D-CNNs) in network intrusion detection systems (NIDS). Building on prior evidence that evolutionary search markedly improves deep models for NIDS, we optimize nine key hyperparameters (filters, kernel size, pooling size, dense depth/width, dropout, learning rate, batch size, and epochs) and train/evaluate on UNSW-NB15, CIC-IDS2017, and NSL-KDD using standard metrics: accuracy, loss, precision, recall, and F1-score. Our GA framework encodes hyperparameters as chromosomes and evolves candidates via selection, crossover, and mutation to maximize validation performance. Experiments show that GA-tuned 1D-CNNs consistently outperform non-optimized baselines across datasets; on UNSW-NB15, for example, GA attains ~99.31% accuracy, aligning with the best reported performance for GA-optimized 1D-CNNs. Results highlight that GA delivers robust gains with practical compute budgets while preserving strong precision-recall trade-offs, and that effectiveness can vary by dataset characteristics. Overall, GA-based hyperparameter optimization offers a simple, reproducible path to higher accuracy and reliability in deep learning–based NIDS, advancing the development of adaptable intrusion-detection solutions for evolving cyber threats.
network intrusion detection system (NIDS)
one-dimensional convolutional neural network (1D-CNN); hyperparameter optimization
LIGHTWEIGHT YET PRECISE: INCEPTION-RESNET-A BACKBONE FOR YOLO-BASED WCE POLYP DETECTION
Wireless capsule endoscopy (WCE) produces long, variable-quality video streams in which early and reliable polyp detection is critical. We present YOLO-InceptionResNet-A, a lightweight object detector that replaces the standard YOLOv4-tiny backbone with an Inception-ResNet-A block to enrich multi-scale feature representation while preserving real-time efficiency. The proposed pipeline operates in two stages: (i) a frame-level screening classifier to filter normal/abnormal images, and (ii) the detector for precise polyp localization. To respect clinical color sensitivity, we adopt conservative, clinically aware augmentation (brightness and mild hue jitter), alongside standard normalization. We evaluate on the Kvasir family of WCE images using patient-level splits and report object-detection metrics (mAP@0.5, mAP@[.5:.95], precision/recall/F1, and IoU), frame-level classification metrics (AUROC, sensitivity, specificity), and throughput on a single RTX 3090 GPU. Across benchmarks, our backbone swap consistently improves detection mAP and recall over YOLOv3, YOLOv4, and YOLOv4-tiny baselines, while maintaining low latency suitable for real-time review. Ablation studies isolate the contributions of the Inception-ResNet-A backbone and the augmentation policy, demonstrating that richer multi-scale features are the primary driver of the gains. We discuss limitations related to dataset size and domain shift, and outline external validation on additional WCE datasets as future work. These results indicate that targeted backbone re-architecture can deliver lightweight yet precise WCE polyp detection without sacrificing speed—an attractive trade-off for clinical deployment.
Wireless Capsule Endoscopy (WCE), Polyp Detection, Object Detection, YOLO/YOLOv4-tiny, Inception-ResNet-A, Lightweight Backbone, Real-Time Detection, Medical Image Analysis, Gastrointestinal Endoscopy, Clinical AI.
ANALYSIS OF THE THERMAL EFFECTS OF LOW-LEVEL LASER THERAPY ON BIOLOGICAL TISSUES USING ADVANCED DIGITAL MEASUREMENT SYSTEMS
The application of Low Level Laser Therapy (LLLT) has been under scientific scrutiny on various fronts, including the need for more knowledge about its thermal effects on biological tissues. Two advanced digital temperature measurement systems are implemented to evaluate temperature temporal and spatial profiles in biological tissues thus enabling an approximate assessment of thermal effects. Temperature profiles obtained with a Laser Doppler Vibrometer (LDV) for a tissue model subjected to continuous wave (cw) laser irradiation at 810 and 980 nm wavelengths, measured during both exposure and free time, showed a linear dependence of the maximum temperature rise as well as the heating and cooling times on the spatial average irradiance. The corresponding temperature rise values were also found to be independent of the tissue model (in vitro/in vivo). Moreover, pointwise measurements using an infrared thermometer positioned at different distances from the irradiated site on painted versus unpainted tissue showed significant contrasts, corroborating that a pig skin model (in vivo) and an apple model (in vitro) painted with white gouache, can be regarded as suitable tissue analogs for LLLT.
DESIGN AND IMPLEMENTATION OF A HIGH-PRECISION, CONSTANT-CURRENT ELECTRICAL MUSCLE STIMULATOR WITH INTEGRATED SAFETY FEATURES
This study presents the development and experimental validation of a high-precision constant-current Electrical Muscle Stimulator (EMS) with integrated safety mechanisms for transcutaneous neurostimulation. To address the main drawback of traditional voltage-source designs—output current variation resulting from changes in skin-electrode impedance—an updated Howland pump configuration is used as a precision voltage-controlled current source (VCCS). The circuit incorporates precisely matched 100Ω resistors and a TLO72 operational amplifier, ensuring output current stability within ±5% for load impedances typical of human tissue (500Ω–2 kΩ). An NE555 timer configured as an astable multivibrator generates adjustable biphasic pulses with a frequency range of 1–6.35 Hz, encompassing the therapeutic bands commonly employed in rehabilitation and pain relief. A key aspect of the design is the incorporation of robust patient safety features—often absent from research-grade stimulators—such as galvanic isolation via a medical-grade output transformer and a series 10µF DC-blocking capacitor. Collectively, these provisions guarantee compliance with the IEC 60601-1 standard for medical electrical equipment. Tests demonstrate reliable delivery of a stable balanced biphasic output current across a range of load conditions. The prototype thus confirms that clinical performance in terms of accuracy and safety can be achieved with inexpensive components, offering a reliable and versatile platform for neuromuscular exploration and future electrotherapeutic applications.
NANOPARTICLE-BASED DRUG MONITORING: A CLINICAL CHEMISTRY APPROACH TO REAL-TIME PHARMACOKINETICS IN CRITICAL CARE
Different types of nanomaterials have been used extensively in intensive-care units (ICUs) to deliver drug for dying cells. The pore could accommodate the transported drug and serve as a transporter to the drug and delivery vehicle. Three classes of nanoparticles are frequently employed for such applications: Metallic particles: (gold and silver particle) * Polymers particles Liposomes and nanospheres Metallic and polymeric types generally have diameters of from about 10 to 200 nm, while liposomes typically range in size from about 100 to 5000 nm. The preference of nanoparticle type is based on desired characteristics of drug loading capacity, biocompatibility and in vivo duration. Liposomes and polymers possess limitations with respect to reduced biocompatibility for the former in which a greater drug-carrying capacity can be achieved. One of the great advantages of nanoparticles is selective transport and delivering drugs, preventing such as broad delivery throughout the body. They are small, improving biochemical interaction and allowing faster delivery. In addition, nanoparticles contribute to maintaining drug susceptibility and guiding transport. Applications include antibiotics, chemotherapeutics, and anesthetics where dose alterations are based on pharmacokinetic monitoring. Nanoparticle quantification can be achieved by spectroscopic, chromatographic and microscopic measurements to produce clinical-chemistry data important for dosage-calibration and the definition of therapeutic toxicities. Finally, incorporation of nanoparticles improves drug monitoring in patients supporting intensive care who are treated with very powerful drugs.
Main Issues of Information Security and Their Solutions
This paper examines the situation on informational security in Uzbekistan by drawing attention to importance of CYBERSECURITY as it is gaining significance at breakneck speed and a growing number of cyber threat incidents are being detected. Amid the India’s digital push, having strong cybersecurity and information security systems has increasingly become crucial to protect sensitive personal data, national infrastructure and businesses. The study examines the political and technical progress in cybersecurity regulation made by Uzbekistan, with an emphasis on legislation that has been developed (including data protection laws) and national agencies established for cybersecurity such as the National Cyber security Center. The research adopts a mixed methods approach, applying qualitative interviews with IT professionals and quantitative surveys with respect to perceived appropriateness of security technologies involved, including the biometric authentication system. The results indicate progress in complying with fundamental cybersecurity measures like login and password policies, as well as increased use of biometric systems. But the study also illustrates persistent challenges, such as the growth of sophisticated cyberattacks like ransomware and phishing. The report ends with suggestions on how Uzbekistan can further develop its security infrastructure to safeguard against cyber warfare.Laying the Groundwork for the FutureOf course, this is just the beginning. These initiatives should guarantee the ongoing safety and viability of Uzbekistan's digital economy.
Cross Section and Yield Calculations of Copper- 67 used in Medicine
Radiopharmaceuticals used in positron emission tomography (PET) are among the most suitable isotopes for radioimmunotherapy. Copper-67 is a suitable radionuclide for labeling a wide range of tumors due to its long enough half-life to allow good biodistribution within the tumor. Among the possible methods for producing Copper-67 using a cyclotron, we investigate deuteron irradiation of natural zinc and copper targets. This paper examines alpha particle, proton beam, and deuteron irradiation of zinc isotopes, and proton irradiation of gallium-71. Key published and verified experimental findings for excitation functions served as the basis for calculating the cross-section and overall integral yield.
APPLICATION OF MULTIPHYSICS MODELING TO COMBINE ELECTROMAGNETIC THEORY, TISSUE BIOMECHANICS, AND CLINICAL IMAGING FOR NEXT-GENERATION MEDICAL DIAGNOSTIC AND THERAPEUTIC DEVICES
Multiphysics modeling combines electromagnetic theory and tissue biomechanics for applications in medical diagnostics and therapeutic device design. Future medical devices will incorporate embedded sensors and actuators that measurably interact with the patient, thereby requiring anticipation of coupled interactions between electromagnetic fields and biological structures. Understandings of sensing and therapeutic implementations support new medical device designs, allowing guidance, control, and landmarking to facilitate next-generation therapies. Diagnostic applications include virtually all clinically established imaging techniques, and validation methods for targeted therapies rapidly improve patient outcomes. The development of therapeutic techniques explores low-level direct applicator-to-tissue couplings that hold promise for tissue regeneration and enhancement.
INTEGRATION OF GENERAL PHYSICS, APPLIED MEDICAL PHYSICS, AND MEDICAL DEVICES TECHNOLOGY ENGINEERING IN THE DEVELOPMENT AND APPLICATION OF X-RAY TECHNIQUES FOR ADVANCED MEDICAL DIAGNOSIS
X-rays, discovered 130 years ago, deeply influence contemporary clinical diagnosis through radiography, computed tomography (CT), fluoroscopy, mammography, and nuclear medicine. Capitalizing on the intersection of applied medical physics and medical devices technology engineering, researchers have introduced new digital radiography technologies at institutions such as the University of New South Wales. Multidisciplinary development encompasses physics concepts, radiation characteristics, CT and X-ray imaging principles, image enhancement, simulation, detection, and computational tomography. An experimental design of an 8 × 8 lateral detector array system continues this unified strategy of applied physics and engineering.
Optimized PTS-SLM Techniques for Reducing Signal Power Fluctuations in OFDM Systems Using BPSK and QPSK Modulation under AWGN and Fading Channels
Orthogonal Frequency Division Multiplexing (OFDM) has emerged as a prominent modulation scheme for wireless communications due to its efficiency in combating multipath fading and supporting high data rates. A major drawback of OFDM is the high variation between peak and average signal power, which leads to nonlinear distortion when passed through power amplifiers. This paper presents improved Partial Transmit Sequence (PTS) and Selected Mapping (SLM) techniques aimed at reducing signal power fluctuations while minimizing computational complexity. The proposed method achieves effective suppression of signal peaks without compromising system efficiency. Simulations conducted in MATLAB R2012 under various modulation schemes (BPSK, QPSK) and channel models (AWGN, fading) demonstrate that the proposed PTS technique offers superior performance in terms of peak power control compared to conventional SLM approaches, as verified by Complementary Cumulative Distribution Function (CCDF) analysis.
Copyright (c) 2023 Innovative: International Multidisciplinary Journal of Applied Technology (2995-486X)
LANDFILLS AND THE IMPORTANCE OF GIS TECHNOLOGY
This article explores the environmental challenges posed by landfills and the role of Geographic Information System (GIS) technologies in addressing these issues. As waste volumes increase due to urbanization and industrialization, the overcrowding and mismanagement of landfills become significant environmental threats. GIS offers a robust solution for efficient landfill management by facilitating precise monitoring and analysis of landfill locations, surrounding risk zones, and waste characteristics. The study presents the various types of landfills, their environmental risks, and how GIS can optimize waste tracking, hazard zone identification, and future planning. Through a case study in Uzbekistan, the article highlights the application of GIS in monitoring landfills, with examples from regions like Tashkent and Karakalpakstan. International experiences from countries like the USA, Germany, and Japan illustrate the global utility of GIS in waste management. Despite challenges such as the shortage of GIS professionals and limited technical resources, the integration of GIS with remote sensing, drones, and artificial intelligence presents a promising avenue for sustainable landfill management. The findings suggest that GIS technology can significantly improve waste sorting, minimize environmental risks, and support long-term waste management strategies. The study concludes with recommendations for expanding GIS-based systems in Uzbekistan, including enhancing regulatory frameworks, training professionals, and establishing dedicated GIS centers.
PHYSICAL ANALYSIS OF IONIZING RADIATION AND ITS APPLICATIONS IN MEDICAL RADIOTHERAPY AND ITS RELATIONSHIP TO THE ACCURACY OF CLINICAL BIOMARKERS
Ionizing radiation is extensively employed in medical procedures, particularly for cancer therapy and diagnostic imaging. Although the biological effects of medium- and high-dose radiation on human health have been established over the past century, the consequences of exposures below 0.5 Gy remain ambiguous. Damage to DNA arises through both direct and indirect mechanisms, resulting in single- and double-strand breaks. Clustered double-strand breaks are especially deleterious due to their complexity and the considerable difficulty involved in repair. The DNA damage response is predominantly mediated via non-homologous end-joining, an error-prone repair pathway that can engender chromosome aberrations and genomic instability, thereby contributing to carcinogenesis. Dose rate critically influences the extent of cellular damage: high dose rates provoke rapid energy deposition, overwhelming repair processes, whereas low dose rates afford temporal windows for repair, mitigating adverse effects. A sophisticated understanding of these underlying biological consequences is imperative for optimizing clinical application of ionizing radiation.
The Internet of things (IoT) is the networking of devices such as embedded sensors, actuators, and various other devices that have the capability to collect and transmit data over a network. IoT revolution represents a paradigm shift in the way devices and objects are connected and communicate with each other over the Internet. IoT technologies offer governments the ability to monitor and manage infrastructure, healthcare, transportation, and public safety in real-time, leading to more responsive and effective public administration. As governments embrace the potential of IoT, they have the opportunity to create smart cities and deliver innovative services that address the evolving needs of their constituents. The paper explores the integration of Internet of things (IoT) technologies within government institutions.
Wearable technologies, also known as “wearables,” are electronic devices designed to be worn on the user’s body. Common types of wearable technologies include smartwatches, fitness trackers, and smartglasses. These devices form a major part of the Internet of things (IoT), and are expected to have a far reaching influence on the fields of fitness, medicine, education, transportation, gaming, logistics, and entertainment. A wearable device is any device that is worn comfortably on the body and enables user interaction. It is typically integrated into the clothing or attached to the body of a person to enhance human performance. Wearable devices contain sensors, processors, and connectivity features to collect and process data, provide feedback, and communicate with other devices. This paper provides an overview of wearable technologies.
LUNG CANCER DETECTION AND CLASSIFICATION USING MACHINE LEARNING ALGORITHM
Lung cancer is a widespread and life threatening disease that has international concerns, and requires innovative methods of identifying it at an early stage and classifying it. In the present paper, a new solution, utilizing the newest machine learning algorithms to enhance the effectiveness and accuracy of lung cancer diagnostics, is presented. The proposed solution will integrate the most recent image-processing algorithms and an effective classification model to analyze the medical imaging data as a whole. The motivation behind this study is that the literature review is done in a critical fashion and gaps that exist in the current literature are determined by this approach and opportunities that machine learning offers to medical imaging. It is this set of gaps that we are targeting, and this approach is a mixture of image preprocessing techniques, feature extraction techniques, along with a state of the art machine learning model. The implementation model is described with step-by-step processes, tools and technologies applied to train and validate on a mixed dataset. Experimental results verify our technique is effective, and the sensitivity, specificity, and the overall accuracy of the technique are better than the existing procedures. These discoveries can be significant due to the fact that they will present a useful and timely diagnostic instrument to the health professionals in order to attain better patient outcomes. In conclusion, the paper can be said to contribute to the field of lung cancer detection as well as predetermine the subsequent improvements and extensions of its application in medical imaging.
The Role of Digital Transformation Frameworks in Aligning IT Innovations with Strategic Business Growth
Digital transformation has become a strategic imperative for enterprises seeking to maintain competitiveness, enhance agility, and drive sustainable business growth in the global marketplace. Recent industry reports indicate that over 91% of organizations have adopted or are planning digital transformation initiatives, yet only 30% achieve the expected value, largely due to the absence of structured frameworks that align IT innovations with core business strategies. This article examines the critical role of digital transformation frameworks as enablers of strategic alignment between technological advancements—such as cloud computing, artificial intelligence (AI), big data analytics, and the Internet of Things (IoT)—and enterprise-level growth objectives. Drawing on real-world benchmarks, including the finding that companies effectively aligning IT and business strategies report 20–30% higher revenue growth and 25% faster market responsiveness, the discussion highlights how structured transformation frameworks ensure scalability, governance, and cross-functional integration. The paper further explores challenges organizations face—ranging from fragmented IT investments to talent gaps—and emphasizes best practices for adopting holistic transformation models that balance innovation with operational resilience. Ultimately, the study underscores that a well-designed digital transformation framework is not merely a technological roadmap but a strategic growth enabler, empowering enterprises to convert IT innovations into measurable business value and long-term competitive advantage.
DESIGN OF A LOWCOST INTELLIGENT DEVICE FOR CHILD PROTECTION USING ARDUINO AND SENSORS
The study focuses on the design and implementation of a smart child protection device using IoT technologies and the Arduino platform. The project aims to monitor the environmental and health status of children in real time and alert parents when any risks such as falling, suffocation, or exposure to inappropriate temperatures appear. The device relies on a set of sensors (such as DHT11 to measure temperature and humidity, motion sensor, heart rate sensor, rain and fire sensors) to collect data, which is processed by Arduino while NodeMCU connects the system to a Wi-Fi network to send alerts to the mobile phone application. The research presents an integrated methodology that combines engineering design and programming, with a focus on reducing cost, achieving high efficiency and ease of use. It also reviews the challenges and results obtained, while providing future recommendations to enhance the device's performance and expand its scope of application to include other areas such as monitoring the elderly and patients.
The Convergence of DevOps, MLOps, and AIOps for Continuous Software Delivery
The accelerating pace of digital transformation has driven organizations to adopt DevOps, MLOps, and AIOps as critical paradigms for agile software delivery, intelligent operations, and data-driven decision-making. While each framework addresses distinct challenges—DevOps streamlining continuous integration and deployment (CI/CD), MLOps operationalizing machine learning pipelines, and AIOps enabling AI-driven monitoring and incident response—their convergence represents a paradigm shift in continuous software delivery. This article explores how the integration of these practices creates a unified ecosystem that supports end-to-end automation, adaptive learning, and intelligent orchestration across the software development lifecycle. By combining DevOps’ agility with MLOps’ governance of machine learning workflows and AIOps’ real-time analytics capabilities, organizations can achieve faster release cycles, enhanced reliability, and proactive system resilience. The convergence also enables closed feedback loops where operational data informs development and ML models, while AI-driven insights automate anomaly detection, resource optimization, and predictive maintenance. Case studies from industries such as finance, healthcare, and cloud-native platforms highlight the practical applications of this triad, including intelligent CI/CD pipelines, automated model retraining, and self-healing infrastructure. However, realizing this vision entails challenges such as toolchain fragmentation, skill gaps, model governance, and integration complexity. Addressing these barriers requires standardization, cross-disciplinary collaboration, and investment in AI-driven automation frameworks. The article concludes that the convergence of DevOps, MLOps, and AIOps is not merely an operational trend but a strategic necessity for organizations seeking to deliver secure, scalable, and adaptive software in an era of rapid innovation and growing complexity. By embracing this unified approach, enterprises can transform continuous delivery pipelines into intelligent, autonomous, and future-ready systems capable of sustaining competitive advantage in dynamic digital ecosystems.
Copyright (c) 2024 Innovative: International Multidisciplinary Journal of Applied Technology (2995-486X)
S/4HANA FINANCE CLOUD: A COMPREHENSIVE ANALYSIS OF DEPLOYMENT, EXTENSIBILITY, AND APPLICATIONS IN MODERN FINANCIAL MANAGEMENT
This article provides a comprehensive analysis of SAP S/4HANA Finance Cloud, exploring its architecture, deployment, extensibility, and applications in modern financial management. It examines the system's in-memory database and simplified data model, which enables real-time processing and streamlined financial operations. The cloud deployment options are discussed, highlighting the benefits of scalability, rapid implementation, and built-in compliance features. The article also delves into the extensibility capabilities of S/4HANA Finance, addressing customization options and the balance between flexibility and standardization. Through case studies, the article illustrates successful implementations and common challenges faced during adoption. Finally, it explores future trends in financial management systems and potential developments in S/4HANA Finance, including enhanced AI-driven automation and improved predictive capabilities. This article offers valuable insights for organizations considering the adoption of advanced financial management solutions in the digital era.
PLASMA PHYSICS APPLICATIONS IN STERILIZATION AND CANCER TREATMENT
Advances in plasma physics enable widespread applications of plasma technology, notably in sterilization and cancer treatment. Sterilization necessitates effective microbial elimination without high temperatures, and corona discharge plasma achieves bacterial and viral inactivation at temperatures below 80 °C. Cold atmospheric plasma (CAP) developments facilitate sterilization of heat-resistant medical devices at room temperature and atmospheric pressure. Plasma's suitability extends to cancer treatment since it generates reactive oxygen species and charged particles inducing various forms of cancer cell death. Plasma constitutes an ionized gas comprising positive and negative charged particles and neutrals, exhibiting collective behavior due to electromagnetic interactions. At sufficiently low gas temperatures, plasma attains thermodynamic equilibrium and behaves as a neutral fluid. Common plasma types include thermal plasma, glow discharge plasma, and corona discharge plasma. Various techniques can generate plasma from neutral gases, such as DC discharge, glow discharge, and corona discharge methods.
AI-Driven Dosimetry Using Real-Time Particle Physics Simulations in Radiation Therapy
Dosimetry, the estimation and assessment of radiation dose received by individuals or materials, is pivotal across many contexts where exposure to ionizing radiation occurs—including medical applications, nuclear industry, and environmental monitoring. Within medical radiation therapy, dosimetry becomes especially critical: accurate calculation of the dose absorbed by tumors ensures effective therapeutic outcomes, while risk to neighboring healthy tissue and medical personnel necessitates stringent control of administered dosage. Complementing dosimetry, particle physics contributes an essential framework for understanding the fundamental constituents of matter and energy, and guides applications that span an extraordinary breadth of contexts—ranging from clinical practice and cancer treatment to space exploration and improving health on Earth. By extending the concepts and principles of particle physics to monitor radiation therapy treatments in real time, automated dosimetry models can furnish on-the-fly measurements of the dose radiated within the patient. These efforts empower medical practitioners to validate the planned dose during the procedure, provide dynamic feedback for modulating temporal delivery characteristics, and underpin precision diagnoses that correlate the dose and biological response.