London Journal of Research In Computer Science and Technology
London Journal of Research in Computer Science and Technology London Journal of Research in Computer Science and Technology: A: Software Engineering & Computing Algorithms, is focused on publishing groundbreaking ideas in domains of computing and advances in technology. It provides practical, theoretical, and managerial implications useful for further research development. As a flagship scholarly journal, It is a peer-reviewed, international, indexed journal. The journal publishes high-quality research papers that are double-blind refereed. Authors and readers include academic researchers and educators, as well as engineering professionals. SUBMIT MANUSCRIPT
Effect of Google Meet & AMP; Instagram App on Speaking Accuracy in Intermediate Level of EFL Learners in L2
Foreign language acquisition is a complex process that requires extensive practice, particularly in developing speaking skills. The aims of this study aims to analyze the effect of utilizing digital platforms such as Google Meet and Instagram on improving foreign language speaking abilities. Four key areas will be discussed: greater access to native speakers, interactive and immersive speaking opportunities, increased cultural exposure, and personalized digital language learning tools. The findings suggest that incorporating Google Meet and Instagram into foreign language learning significantly enhances students’ speaking proficiency, motivation, and overall language acquisition. In this study, for completion of this article used from qualitative methods and for data collection is used from interview from the learners the integration of Google Meet and Instagram into foreign language learning offers a multitude of benefits in terms of native speaker interaction, immersive speaking opportunities, cultural exposure, and personalized language learning tools. By utilizing these platforms, language learners can significantly enhance their speaking skills, build confidence, broaden cultural awareness, and establish connections with individuals from various linguistic backgrounds.
Copyright (c) 2023 Authors and Global Journals Private Limited http://creativecommons.org/licenses/by/4.0/
A Comprehensive Survey on Mobile-based Resource Sharing Platforms for Sustainable Neighbourhoods using Artificial Intelligence
Effective sharing of local resources, tools, everyday goods, and skills can change daily life. However, current peer-to-peer (P2P) systems, the majority of the time, struggle with user engagement, supply-demand mismatch, and a lack of trust. This paper surveys these models and introduces a mobile-centric sharing app using Flutter and Firebase. The design features user authentication, location-based discovery, and an in-app negotiation module with a rating system for reliability. Prototype tests show responsive interactions averaging under two seconds. Role-based access control and encrypted data exchanges address security concerns. This survey demonstrates how intelligent local platforms can optimize assets, minimize waste, and enhance community resilience.
Copyright (c) 2026 Authors and Global Journals Private Limited http://creativecommons.org/licenses/by/4.0/
A Dynamic Framework for a GeoAI-Driven Updatable Master Planning System
Traditional master planning often relies on decadal, static documents that fail to account for the rapid spatio-temporal dynamics of modern urban environments. This divergence between planned and actual land use creates systemic inefficiencies in urban governance. This research proposes a GeoAI-driven framework—a formal systems approach that integrates Geomatics, Artificial Intelligence (AI), and Deep Learning (DL) into a live, updatable “City Engine.” The framework utilises Convolutional Neural Networks (CNN) for automated change detection and GIS-based heuristic rules for instant plan versioning. By shifting the master plan from a static atlas to a dynamic “body of knowledge,” the proposed system enables real-time monitoring, evaluation, and publishing of urban development strategies. The results demonstrate that such a system can significantly bridge the implementation gap, offering a scalable model for smart city governance and sustainable regional development.
Monitoring and Diagnosis of Faults in Three-Phase Induction Motors using a Narx Artificial Neural Network
Three-phase induction motors constitute the backbone of modern industrial drive systems due to their structural simplicity, reliability, and cost efficiency. However, mechanical and electrical faults significantly reduce operational reliability and may lead to unplanned downtime, energy losses, and safety risks. This study proposes an integrated intelligent monitoring and diagnostic framework based on current, temperature, and vibration signal analysis combined with a Nonlinear Auto Regressive model with eXogenous inputs (NARX) artificial neural network. Experimental investigations were conducted using a laboratory-scale test bench under controlled fault conditions including stator unbalance, bearing damage, and shaft misalignment. Multi-sensor data acquisition enabled time-domain and frequency-domain feature extraction for dynamic fault characterization. The collected dataset was used to train and optimize a NARX neural network capable of modeling nonlinear temporal dependencies inherent in induction motor behavior. The developed model demonstrated high classification performance with accuracy rates of 94.2% for general faulty motor detection, 95% for shaft misalignment, 98% for bearing defects, and 95% for stator-related faults. The proposed methodology provides a robust and scalable solution for early fault detection and predictive maintenance in industrial applications.
Privacy after Puttaswamy: Constitutional Boundaries of State Data Collection under the Digital Personal Data Protection Act, 2023
The recognition of privacy as a fundamental right by the Supreme Court of India in Justice K.S. Puttaswamy (Retd.) v. Union of India marked a decisive shift in Indian constitutional jurisprudence, particularly in the context of an increasingly data-driven State. In the aftermath of this landmark judgment, the enactment of the Digital Personal Data Protection Act, 2023 represents India’s first comprehensive statutory framework governing personal data processing. However, the Act raises significant constitutional questions, especially concerning the breadth of exemptions granted to the State for purposes such as sovereignty, public order, and national security. This paper examines whether the regime of State data collection under the Digital Personal Data Protection Act, 2023 conforms to the constitutional standards articulated in Puttaswamy, particularly the doctrines of proportionality, necessity, and procedural safeguards. The research problem centres on the apparent tension between the constitutional right to privacy and the statutory discretion accorded to the executive. Adopting a doctrinal and comparative methodology, the study analyses constitutional jurisprudence, statutory provisions, and comparative data protection frameworks, notably those in the European Union and other common law jurisdictions. The paper finds that while the Act strengthens data protection vis-à-vis private actors, it falls short in adequately constraining State power. It concludes that without clearer statutory limits and robust oversight mechanisms, the constitutional promise of privacy risks being diluted in practice.
A Critical Analysis of Governance Failures, Fiduciary Responsibilities, and the Path Forward
The rapid integration of artificial intelligence into organizational decision-making has fundamentally altered how value is created, risks are managed, and authority is exercised within modern enterprises. Yet, while AI systems increasingly influence high-stakes outcomes, the governance mechanisms that oversee them have not evolved at the same pace. A critical vulnerability has emerged: executives and board members are frequently tasked with governing AI systems they do not sufficiently understand. This article argues that AI governance without executive AI literacy represents a structural governance failure rather than a technical shortcoming. The article examines how low levels of AI literacy among executives undermine strategic alignment, weaken risk oversight, and expose organizations to ethical, regulatory, and reputational harm. It demonstrates why traditional corporate governance and enterprise risk management frameworks are ill-suited to address AI-specific risks, including algorithmic bias, data misuse, opacity, and cascading system failures. Rather than positioning AI literacy as optional or advisory, the article reframes it as a fiduciary and governance imperative essential to informed oversight and responsible decision-making. To address this challenge, the article presents an integrated AI governance approach centered on executive literacy and structured around technical understanding, strategic oversight, ethical accountability, and regulatory compliance, supported by continuous learning and adaptive governance. The article concludes that organizations that embed AI literacy at the executive level are better positioned to realize AI’s benefits while mitigating its risks, whereas those that fail to do so face growing governance, performance, and legitimacy deficits in the AI era.
Reverse Cognitive Pathways: A Vijñaptimātra Account of the Ontological Limits of Artificial Intelligence and its Governance
This paper proposes an inverse trajectory thesis for understanding human-level Artificial Intelligence (AI) by distinguishing computational data processing from authentic information processing. Drawing upon the Vijñaptimātra (consciousness-only) framework, we argue that contemporary AI follows a vijñāna-first developmental path—achieving functional discrimination without lived appropriation (manas) or karmic continuity (citta)—whereas human cognition develops from citta through manas to vijñāna. This ontological asymmetry explains recurrent AI failure modes including specification gaming, simulated empathy, and brittle generalisation. We introduce Prime Knowledge Elements (PKEs) and an Architecture of All Knowledge (AOAK) as computational frameworks for representing authentic information. We propose governance mechanisms integrating Buddhist ethical principles into AI architecture, treating alignment as continuous ethical reflexivity. Testable predictions regarding anthropomorphism gradients, corrigibility dividends, and embodiment limitations are presented. Our analysis concludes that the absence of cetanā (genuine intentionality) represents a fundamental ontological boundary preventing AI from achieving genuine moral agency.
Copyright (c) 2025 Authors and Global Journals Private Limited http://creativecommons.org/licenses/by/4.0/
A Machine Learning Assisted MRI Approach for Early Detection of Pelvic Bone Cancer
Pelvic bone cancer is a serious medical condition that often remains undetected in its early stages due to non-specific symptoms and the complex anatomical structure of the pelvic region. Common clinical symptoms include persistent pelvic pain, swelling, restricted movement, unexplained weight loss, and fatigue, which are frequently mistaken for musculoskeletal disorders. Factors such as genetic predisposition, previous radiation exposure, bone disorders, and long-term inflammation are considered significant contributors to the development of pelvic bone malignancies. Delayed diagnosis increases disease severity, highlighting the importance of early detection and public awareness. Magnetic Resonance Imaging (MRI) plays a vital role in visualising pelvic bone abnormalities due to its superior soft-tissue contrast. This study proposes an automated framework for pelvic bone cancer detection that integrates image filtering, region extraction, and clustering-based segmentation. During preprocessing, median filtering and Gaussian filtering are applied to MRI images to suppress noise, smooth intensity variations, and enhance structural visibility. This filtering stage improves image quality and supports accurate identification of abnormal tissue regions. A Region of Interest (ROI) extraction step then isolates tumour-suspected pelvic areas, reducing interference from surrounding tissues. The extracted ROIs are segmented using K-means clustering and Fuzzy C-Means (FCM) algorithms based on intensity and spatial characteristics. While K-means performs hard clustering, FCM enables soft classification through membership values, resulting in improved tumour boundary delineation in complex pelvic structures. Experimental results show that FCM outperforms K-means in handling overlapping tissue intensities. This automated, filtering-assisted approach can serve as a supportive diagnostic tool for radiologists. Moreover, the study emphasises the importance of early symptom recognition and timely medical consultation to reduce fear, increase awareness, and improve survival outcomes among individuals at risk of pelvic bone cancer.
Association between Cyberbullying Crude Comments and the Number of User Subscribers and Uploads on Youtube
This article entails a quantitative analysis of the association between cyberbullying crude and number of user subscribers and uploads on YouTube. Of the several numbers of variables listed in the said dataset from Kaggle.com, the variables of comments, subscribers and uploads were found to be continuous. The data included 3464 YouTube user ids, and were analyzed using Descriptives, Normality Tests and Spearman’s Rho on SPSS. The findings included non-linear, positively skewed, non-normal distribution. The correlation analysis depicted a slight positive association between User subscribers and rude comments, whereas no connection between User uploads and crude comments. Future work to focus on other factors influencing hurtful commenting on YouTube.
VGG16TLCCNN: Hybrid VGG16-Transfer Learning and Custom CNN Model for Sugarcane Disease Classification
Efficient crop management and yield optimization rely on accurate identification of sugarcane diseases. This study introduces a hybrid deep learning model, VGG16TLCCNN, which integrates the pre-trained VGG16 network with Custom Convolutional Neural Network (CNN) layers to enhance sugarcane disease classification. The proposed model employs transferlearning to leverage VGG16’s robust feature extraction while fine-tuning custom layers tailored to the unique visual patterns of sugarcane diseases. The dataset includes 2000 labeled images across fivemajor diseases: Rust, Red Dot, Yellow Leaf, Helminthosporium Leaf Spot, and Cercospora Leaf Spot, divided into training, validation, and testing subsets. Experimental results demonstrate that the hybrid model improves accuracy by 10–15% compared to conventional CNN and standalone VGG16 architectures, achieving superior generalization and reduced overfitting. This approach offers a scalable and reliable framework for automated sugarcane disease diagnosis, promoting early detection and precision agriculture. Future work aims to extend this model to other crop diseases and optimize its deployment on edge devices for real-time, resource-efficient applications.
Empirical Evaluation of BATMAN-adv for Carrier-Class Resilience in a Resource-Constrained Campus Wireless Mesh Network
The proliferation of digital educational resources necessitates robust and affordable campus networking solutions, particularly in emerging economies where infrastructural and budgetary constraints are pronounced. Wireless Mesh Networks (WMNs) present a viable alternative to traditional wired backhauls; however, their efficacy is critically dependent on the underlying routing protocol’s ability to provide resilient connectivity without incurring prohibitive costs. This study presents an empirical evaluation of the BATMAN-adv (Better Approach to Mobile Ad-hoc Networking – advanced) routing protocol, deployed within the production network of the University of Cape Coast (UCC), Ghana. Employing a mixed-methods sequential explanatory design, we integrated quantitative data from NS3 simulations and physical network deployments with qualitative insights from interviews with IT administrators. Our findings demonstrate that a multi-path redundancy architecture, facilitated by BATMAN-adv, achieved 99.9% operational uptime with a mean failover time of 1.5 seconds during simulated link failures. This performance substantially surpassed dual-gateway (99.8% uptime, 3.2s failover) and non-redundant (92.5% uptime) architectures. Crucially, this carrier-class resilience was delivered at an estimated 90% reduction in capital expenditure compared to proprietary alternatives, leveraging commodity hardware and the protocol’s decentralized design. The study concludes that BATMAN-adv is a pragmatically superior routing solution for institutions where fiscal constraint and operational reliability are paramount, effectively bridging the gap between theoretical network models and tangible, sustainable deployment.
A Hybrid Deterministic–Machine Learning Framework for Bandwidth-Efficient Ddos Detection in Iot Networks
The spread of Internet of Things (IoT) devices has significantly expanded the attack surface for Distributed Denial-of-Service (DDoS) threats, exposing resource-constrained gateways to bandwidth exhaustion and service disruption. While machine learning (ML)–based detection systems achieve strong accuracy, their computational cost renders them impractical for IoT environments. Conversely, lightweight deterministic filters provide efficiency but lack adaptability to evolving attack strategies. This study presents a Hybrid Deterministic–Machine Learning (HD-ML) framework that integrates deterministic packet verification with lightweight supervised classifiers to achieve both scalability and adaptability. The framework filters trivially malicious traffic at the gateway and forwards only residual ambiguous flows for ML-based classification. Using NS-3 simulations, we generated a dataset of over 100,000 packets, extracted flow-level features, and evaluated multiple classifiers including Decision Tree, Naïve Bayes, Logistic Regression, Random Forest, and Support Vector Machine (SVM). Results demonstrate that the HD-ML framework achieves an overall detection accuracy of 98.8% with a false positive rate as low as 0.8%, significantly outperforming standalone deterministic or ML-based approaches. Among the classifiers, SVM exhibited the highest performance with a perfect ROC-AUC score of 1.0 and an F1-Score of 0.926, confirming its suitability for residual traffic analysis. The proposed framework therefore offers a bandwidth-efficient, computationally lightweight, and adaptive defense mechanism for real-time DDoS mitigation in IoT networks.
Self-Service Analytics 2.0: AI-Powered Dashboard Generation with Human-in-the Loop Feedback Architecture
This paper presents the architectural foundation and implementation results of Self-Service Analytics 2.0, an AI-powered system that automatically generates business dashboards from raw data while incorporating continuous human feedback loops. Our architecture integrates automated schema detection, intelligent KPI discovery, and adaptive visualization generation through a multi-layered feedback mechanism that learns from user interactions. The system demonstrates a 47% reduction in dashboard creation time and achieves 78% user satisfaction scores through iterative refinement. We detail the comprehensive architecture including feedback collection pipelines, model adaptation mechanisms, and human-in-the-loop quality assurance workflows that ensure generated insights remain aligned with business objectives.
The Weakest Link in Internet Privacy: Security and Compliance Risks in Third-Party Vendor Data Handling
The new internet economy relies on third-party sellers, such as cloud computing service providers, SaaS and services, payment processing services, and marketing services. On the one hand, such sellers make scaling and innovativeness possible, and, on the other hand, such sellers endanger the safety of personal data and the sanctity of the law. This paper discusses the vulnerabilities inherent to vendor ecosystems using case studies of the Target and SolarWinds breaches to provide examples of the weaknesses present in systems. It also talks about the regulatory frameworks such as GDPR, CCPA, HIPAA, and PCI DSS, and outlines the impediments to implementation and lapses in responsibility. This empirical study proposal of the best internet company practices on vendor risk is provided to contribute to benchmarking in this under-researched field. Lastly, there are technical safeguards, organizational measures and policy recommendations, and finally a call to a global Vendor Privacy Assurance Standard. The results show that vendors are the least strong link in privacy protection, and that there is a need for concerted efforts across the industry, regulators, and academia.
The Relationship between Consciousness and Linguistic Data to Formalize
This study seeks to formalize the relationship between consciousness and linguistic data through a symmetry-based approach. It proceeds from the assumption that consciousness as a biological fact and linguistic structures—especially their formally describable patterns—are mirror images of one another. The structures of conscious linguistic composition are reflected in the organization of linguistic data, while formal linguistic patterns can be traced back to the inner, rhythmic, and symmetrical architecture of consciousness. To map this relationship, the study integrates the methodological toolkits of three coequal fields: computational linguistic encoding, rhythm-based phonology, and the historical investigation of linguistic melody. The computational linguistic encoding relies on a custom-developed system that classifies syllables within words by typology and position. At the core of the model stands a center–periphery principle: vowels constitute the center of the structure, encircled by concentrically arranged consonants. This symmetry-based approach enables precise formal annotation of the internal structure of syllables and sheds light on recurring patterns in phonological organization. The classification introduces 55 syllable types grounded in five basic structures (e.g., open, closed, reduced, etc.), augmented by positional labelling that records whether a syllable is word-initial, word-medial, or word-final.
Design of a Real-Time, Multilingual, Emotion-Aware Cyberbullying Detection System using Multi-Teacher Knowledge Distillation and Explainable AI
Social media cyberbullying has propagated rapidly and is being experienced by individuals worldwide. It tends to be expressed using sarcasm, emotional language, and multiple languages, making it difficult to determine the identity of the perpetrator. Although automated detection systems are becoming increasingly prevalent, the majority of existing systems suffer from language issues, function only in offline batch mode, and are black-box models that cannot be interpreted. These constraints make it more difficult to intervene with speed and transparency. This paper offers a real-time, multilingual system for detecting cyberbullying, using explainable AI, emotion and sarcasm detection, and Multi-Teacher Knowledge Distillation (MTKD) to address shortcomings. The system leverages an ensemble of transformer-based teacher models, like mBERT, XLM-R, and IndicBERT, to capture language-specific features. Then, the models collaborate to produce a lightweight XGBoost classifier. To assist with the interpretation of context, additional layers are incorporated to identify sarcasm and emotion. SHAP (SHapley Additive Explanations) is employed to provide each prediction token-level interpretability. Algorithmic and architectural design of a system that would form a transparent, efficient, and deployable solution to detect cyberbullying in different emotional and linguistic contexts is the focus of this study.
AI-based Sustainable Vehicle Monitoring System for Existing Internal Combustion Vehicles
The transportation industry is a major contributor to carbon emissions, with internal combustion engines responsible for over 25% of the total. Despite advances and regulations encouraging the shift to electric vehicles, the transition from diesel engines remains slow, as expected. Many countries have heavily relied on diesel engines, which makes the switch to electric vehicles more difficult due to the higher costs of buying and replacing internal combustion engines with electric ones. Therefore, this report suggests a solution: retrofitting existing ICE vehicles with AI-powered sustainable vehicle monitoring systems. This upgrade involves installing sensors that work with OBD-II diagnostics to monitor emissions, fuel use, and driving habits in real time. Gathering this data aims to develop personalized, eco-friendly driving recommendations that help reduce overall emissions. This method provides a cost-effective, sustainable, and environmentally friendly alternative to high carbon emissions. It is also scalable, even in regions with limited financial resources.
Federated Data Governance for Cross‑Institution Anti‑Money Laundering (AML) using Data Warehousing and AI
Federated data governance enables banking institutions to leverage collaborative capabilities, effectively combating money laundering activities while upholding compliance requirements and safeguarding information autonomy. The structural design merges secure information warehousing with cognitive systems, creating identification frameworks that maintain confidentiality across institutional boundaries. Utilizing federated learning principles, institutions uncover intricate laundering schemes typically concealed within segregated systems. The governance structure employs cryptographic safeguards, detailed permission hierarchies, and permanent verification records to protect information throughout collaborative engagements. Successful deployment addresses system interoperability, allocates processing capacity, and harmonizes data structures among participating organizations. Regulatory aspects include navigating jurisdictional requirements, transnational information exchange protocols, and coherence with global financial security standards. The article yields improvements in identification precision, surveillance capabilities, and notification accuracy when compared with conventional isolated approaches. Financial organizations implementing federated governance enhance compliance positions while sustaining operational autonomy. Through a balance between security imperatives and functional requirements, this architecture provides a comprehensive solution to coordinated anti-money laundering challenges within interconnected financial markets, laying the groundwork for productive collaboration against increasingly sophisticated financial offenses.
Sentiment Analysis of Computer Mediated Communication in Social using Natural Language Processing
The massive interactions on social mediaplat forms had created a luxury of computer-mediated communication (CMC) languages in recent times, especially on the X (formerly Twitter) Platform. Resources required in extracting and analyzing these enormous expressions whether for public perception, market trends, or social dynamics are incredibly huge and can also be complex to handle. The comparative investigation of the CMC based on the accuracy of the interpreted sentiments is expressed within the Google Natural Language Processing(NLP) API model. The results of experts’ analysis with that of the Google NLP model using sizable data of CMC from X were compared. The X comments on the declaration of the state of emergency by the Nigeria President-Bola Ahmed Tinubu- in Rivers State on the 18th of March 2025 as posted by its handlers were the subjects of analysis. Identification and categorization of sentiment polarity whether positive, negative, or neutral were carried out by the model. Indices such as linguistic variations, context-dependent sentiment, sarcasm, and irony were used in order to understand the influence on the accuracy and reliability of sentiment analysis results of the tool. The outcome of this paper reveals the remarkable strengths and weaknesses of an Google NLP model in analyzing sentiment present in the CMC in social media platforms.
Self‑Serving Data Marts Orchestrated by Auto ML-Governed Pipelines
Self-serving data marts orchestrated through AutoML-governed pipelines mark a significant advancement in enterprise analytics democratization. This architectural approach creates domain-specific information repositories with automated data preparation, feature engineering, and model development functions accessible to business users without deep technical knowledge. The governance framework applies automated quality controls, lineage tracking, and access management, ensuring data integrity throughout analytical processes. Integration with existing data warehouse systems maintains centralized governance while enabling distributed analytical capabilities, addressing specific business needs. Implementation factors include metadata standardization, processing resource allocation, and organizational change management supporting effective usage. Technical elements comprise automated data profiling, dynamic transformation creation, and continuous quality monitoring throughout pipeline operation. The orchestration layer manages complex workflows while implementing appropriate error handling and recovery mechanisms. Enterprises implementing these frameworks report significant enhancements in analytical responsiveness, resource utilization effectiveness, and business coordination compared to conventional centralized models. This balanced approach resolves conflicting requirements between governance standardization and analytical adaptability, establishing durable foundations for growing self-service functions while preserving appropriate supervision across increasingly intricate data landscapes.
Advanced Robotic Multimodal Imaging with Real-Time Motion Compensation for Dynamic Structural Health Monitoring
Modern composite materials promise superior performance and load-bearing capabilities, yet evaluating their structural integrity remains challenging. Current testing methods, such as visual, thermographic, ultrasonic, optical, electromagnetic, terahertz, shearography, X-ray, and neutron imaging, are hampered by long scan durations, limited field of view, suboptimal accuracy, and high costs, particularly when applied to large structures. This paper addresses these issues by introducing a novel robotic multimodal imaging system that overcomes the limitations of traditional methods. This system dynamically captures both static and dynamic properties of materials using advanced motion compensation techniques. By integrating multiple radiographic modalities into a coordinated robotic platform, it provides rapid, high-resolution imaging of composite materials of large structures without the need for disassembly. The system was validated through simulations of a four-robot radiograph setup, treated as two single-plane systems. The 3D position and orientation of a cube phantom were determined by generating computer-based digitally reconstructed radiographs from a computed tomography model and applying a 3D line intersection method based on known imaging geometries. Comparisons between marker-based and markerless kinematics tracking methods yielded differences of only 0.03 mm in translation and 0.06° in rotation. These findings demonstrate that the proposed system significantly reduces scan times and enhances accuracy, offering a robust, scalable solution for dynamic inspection in diverse fields such as aerospace and medical device manufacturing.
Internet of Things and Global System for Mobile Communications
Robotics-driven Imaging; Motion Correction; Multimodal; Porosity Analysis; Materials testing
Computer-based metrology; Non-Destructive Testing
Smart Safety Solutions: Real-Time SMS Alerts for Gas Leakage and Fire Detection using IoT
Security issues are becoming more and more important in today’s environment, necessitating increased attention to detail because of the rising dangers of infiltration and dangerous situations. Gas leaks and fires are among the most urgent hazards to residential and commercial safety as they may cause a devastating loss of life and property. Conventional alarm systems that just use sound signals frequently fail to guarantee prompt human reaction, particularly in situations where inhabitants are not present. Using Internet of Things (IoT) devices that are coupled with GSM (Global System for Mobile Communications) technology, this study suggests a real-time gas leak and fire detection system that sends timely SMS notifications. To identify anomalies, the system uses an Atmega644P microprocessor that is interfaced with gas and fire sensors, relays, and buzzers. Through the SIM548C GSM module, which notifies pre-designated emergency contacts via SMS in real time, the system instantly sounds an alarm in the event of a gas leak or fire. This rapid communication mechanism ensures faster response and mitigates potential damage. The increasing importance of intelligent, real-time safety systems in tackling contemporary security issues is highlighted by this review.
Digitizing Compliance: A Case Study in AMS-EDI Integration for Secure Global Shipping
The implementation of the Automated Manifest System (AMS) rule by the U.S. Customs and Border Protection agency following the September 11 attacks marked a pivotal transformation in global maritime security protocols. This transformative case documents the creation of an Electronic Data Interchange (EDI) compliance system that bridged regulatory requirements with operational efficiency. The initiative successfully deployed a middleware abstraction layer that processed thousands of daily cargo manifest submissions while validating critical data elements required by federal regulations. Developed by a geographically distributed team operating across multiple time zones, the system achieved exceptional first attempt acceptance rates while significantly reducing processing times and costs. The impementation pioneered human-centric design principles in regulatory technology, dramatically decreasing cognitive load and training requirements while increasing user satisfaction and adoption rates. Beyond meeting immediate compliance needs, the solution generated substantial recurring revenue, expanded market share, and catalyzed broader digital transformation initiatives across the maritime shipping industry. The case exemplifies how regulatory challenges, when approached through innovative architectural design and cross-border collaboration, can yield competitive advantages while establishing new standards for balancing security imperatives with commercial efficiency.
Enhancing Learning Through Game Flow: A Case Study for Epistemic Game “Working With Water”
Epistemic video games, interpreted as video games pursuing new knowledge through the mechanism that concatenates practices and cognition, grab numerous attention on account of their extensive implementation since COVID-19. The corresponding academic explorations into the players, especially indicating the students, have largely integrated in game enjoyment and the principals exerted. Understanding the essential factors that promote game enjoyment that aligns with educational purposes is crucially important for clarifying the mechanism of epistemic games. Consequently, the present study adopts the GameFlow model presented by Sweetser and Wyeth (2005), whose accuracy has been verified and yields to evaluate enjoyment of video games by examining specific criteria under eight elements: concentration, challenge, player skills, control, clear goals, feedback, immersion, and social interaction of the model. The highly acclaimed epistemic game “Working with Water” is elected to conduct a case study by implementing the Game Flow model. The result indicates that the overall values for Game Flow cohere with the performance, usability, and potentiality of the epistemic games. The model performs well in the evaluation of epistemic games to a large extent. but simultaneously limited to the intrinsic harshness of the specific genre that differs from others in the degree of enjoyment. As a result, the evaluating model based on GameFlow is worthy of inquiry in an educational context for further investigation.
Advancement in Autonomous Navigation in Space through Artificial Intelligence: A Systematic Review
Since the first trip to Mars, rovers have been used to conduct scientific experiments. The use of rovers continues to date due to Mars being uninhabitable for humans at this state and time. Despite the invention of Mars rovers, many limitations have been challenging space exploration, even with improvements in the Curiosity and Perseverance rovers. This study aimed to investigate the advancement in autonomous navigation in space through artificial intelligence. The study used qualitative, desktop, and systematic review research designs. The study’s objectives were to determine the types of autonomous navigation technologies in space and examine the role of artificial intelligence in autonomous navigation technologies. Data was collected from secondary sources, which were purely open-access online journals. A total of 61 journals were searched as the target population. The study then conducted judgmental sampling using 9 out of 61 journals. The Preferred Reporting Items for Systematic Reviews and meta-analysis guidelines guided the sampling process. The study used an extraction form to collect data. The study found that star tracker navigation reduces positioning errors and environmental disturbances from sand and dust storms. It was also established that LOS navigation and star trackers ensured highly accurate navigation in challenging conditions for path-planning accuracy. Selecting the Next Coordinate, Obtaining Coordinates of Target Points, Generating Additional Coordinates, and optimising planned paths reduce travel time and energy consumption. On-orbit servicing robotics can extend rover missions up to 10 years. It was also found that 3D OctoMaps improves rovers’ navigation accuracy. In addition, the study revealed that unsupervised homography networks, state recursive models, and inertial measurement models reduce localisation errors. Simultaneous Localization and Mapping can improve navigation accuracy and reduce mission delays. The use of RL navigation systems reduces mission delays. It was also found that Convolutional Neural network algorithms advance autonomous navigation accuracy in terms of terrain classification. The study also established that the Nvidia Jetson Nano AI controller and Rapidly-exploring Random Tree (RRT) in MATLAB reduce travel time and energy consumption. Finally, it was found that artificial neural networks advance the accuracy of terrain classification and lower navigation errors.
An Approach to Setting up Dataset for Winner-Domain Algorithm Selecting Teamers Participating in Vietnam National Competitions
The dataset of competitors’ performance in some recent years is the input of winner-domain algorithm selecting good student as member of a subject team participating in Vietnam national prize. The data of learning performance features may be retrieved from the competitors’ learning outcomes. Meanwhile, the collection of non-learning performance feature data of competitors meets challenges relating to the individual right and interviewees’ cooperation for answering questionnaire. This research analyzed competitors’ skills and lifestyle into very simple questions with very easy answer mode, without the deep relation to their individual lifestyle. The reply-matrix and reply-cube are formed to map simple answers of questionnaire reply into quantitative values. This approach may be flexibly applied to easily form the datasets of competitors’ performance for various subjects in different years and at different levels of competitions.
Classification of Acoustic Data with Transformer Model
Bees are essential to global ecosystems, particularly for pollinating crops, yet in recent years their populations have faced significant decline. One critical aspect of bee colony health is the ability to detect negative in-hive events such as a queen leaving the hive. Traditionally, beekeepers rely on manual inspections to assess hive conditions, a labor-intensive and time-consuming process. However, recent advances in machine learning offer new approaches to automating this task. Since 2016, there have been attempts to classify bee sounds using machine learning, employing the power of different machine learning methods, including deep learning architectures. In this research, we explore the use of acoustic labeled data for in-hive event classification, focusing specifically on detecting when a queen leaves the hive. We utilize 12-hour recordings from different locations, with the data preprocessed and transformed to be suitable for input into a transformer-based neural network. Our goal is to demonstrate that transformer models yield superior results in this task compared to previous approaches. The study is organized into several key sections: we first highlight the ecological importance of bees, followed by a literature review on the state of bee sound classification research. We then delve into the data preparation process, model design, and present our findings. Our results underscore the potential of transformer models in automating hive monitoring, offering a scalable solution for beekeepers to protect and preserve bee populations.
Artificial Intelligence and the Challenges of Copyright Protection Systems in Search of Common Norms
The integration of artificial intelligence (AI) into the production of creative works presents significant challenges to the existing frameworks of copyright law. With AI’s increasing autonomy in creative processes, traditional notions of authorship and originality are under scrutiny, necessitating a reevaluation of copyright protections. The paper proposes an evolved copyright framework that distinguishes between AI-assisted and AI-autonomous creations, suggesting clear guidelines for AI authorship and recommending the establishment of a specific copyright category for AI-generated works. This category would adjust protection levels and terms to reflect the unique nature of these works while ensuring that human creativity remains incentivized. The proposed framework aims to recalibrate copyright law to ensure the protection of human creators’ rights while acknowledging AI’s innovative contributions. This focused approach underscores the need for copyright law to adapt to technological advances, ensuring that it continues to fulfill its role in promoting creativity and innovation.
Four Universal Data Structures of 169 arrays
Copyright (c) 2024 Authors and Global Journals Private Limited http://creativecommons.org/licenses/by/4.0/
Practical Screening Method for Cancer Gene Diagnosis, How to Choose Cancer and Normal Patients by Four Principles
We developed a new theory of discriminant analysis (Theory1). Physicians can use it for practical medical diagnoses. Only Revised IP Optimal-LDF (RIP) obtains the minimum number of misclassification (MNM). RIP can discriminate linearly separable data (LSD) theoretically. It discriminated against 169 microarrays with two classes and found that 169 MNMs are zero and LSD. It can split high-dimensional arrays into many small LSD with less than n (patient’s number) genes that are the candidates of multivariate oncogenes. We completed a new theory of high-dimensional gene data analysis (Theory2). A 100-fold Cross-Validation (Method1) can rank all candidates for the importance of diagnosis. Thus, if physicians firstly use Theory2 as the screening method, they can start their medical studies with the correct small sizes of candidates. This paper analyzes four arrays in detail and proposes correctly choosing cancer and normal patients using four principles.
Smooth Attention: Improving Image Semantic Segmentation
This document shows the required format and appearance of a manuscript prepared for SPIE journals. It is prepared using LaTeX2e with the class file spieman.cls. Please note that the following journals require the use of structured abstracts in manuscript submissions: Neurophotonics, the Journal of Biomedical Optics, and the Journal of Medical Imaging. Structured abstracts are encouraged for the Journal of Micro/Nanolithography, MEMS, and MOEMS. Guidelines are available on the journal website. Whether structured or single-paragraph, the abstract should be a summary of the paper and not an introduction. Because the abstract may be used in abstracting and indexing databases, it should be self-contained (i.e., no numerical references) and substantive in nature, presenting concisely the objectives, methodology used, results obtained, and their significance. A list of up to six keywords should immediately follow.