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Ensuring Beneficiaries' Rights and Legal Protection Through Strengthening Downward Accountability in Waqf Management: A Shariah-Legal Analysis of the Stakeholder Theory
No full textThis research explores how stakeholder theory can enhance the protection of waqf beneficiaries’ rights and ensure their legal safeguards through strengthened downward accountability. It reconciles traditional Islamic practices with modern governance principles to improve transparency and beneficiary involvement in waqf governance. The study employs qualitative doctrinal methods, utilising Islamic jurisprudence and legal theories to analyse the rights of waqf beneficiaries, with an emphasis on fiduciary duties, stakeholder accountability, and the need for mutawalli’s legal obligations to implement downward accountability. Findings show that integrating stakeholder theory into waqf management can effectively balance the interests of multiple stakeholders, including waqf beneficiaries. Implementing this principle can enhance accountability, fostering better socio-economic development and protection for waqf beneficiaries. However, addressing power imbalances between the mutawallis and waqf beneficiaries, exacerbated by the lack of a legal requirement for downward accountability from the mutawallis, is essential to ensure the complete protection of beneficiaries’ rights and the efficient and transparent operation of the waqf system. Thus, this study proposes a beneficiary-centric model of waqf governance rooted in stakeholder theory and aligned with Islamic principles, emphasising the need to prioritise beneficiaries’ interests in waqf management and addressing potential challenges to their fulfilment. The research proposes the need to impose enforceable obligations on mutawallis to be accountable to waqf beneficiaries under their managemen
Influence of Religion on Modern Piracy: A Socio-Legal Analysis
No full textResearch suggests that piracy is linked to religion through politics, ocean security and trade. Pointedly, it appears that terrorism also links piracy to religion. However, studies analysing piracy have neglected to explore it. This article uses a doctrinal legal methodology to examine the linkages between piracy and religion. It shows that from the classical era through the golden age to the modern era, piracy and religion are intertwined, and terrorism appears evident in the process. This is revealed in piracy in Southeast Asia and the Gulf of Aden. The article argues that piracy and religion linkage presuppose a forceful and violent control over people’s lives for economic, security and political hegemony. Thus, terrorism, in some cases, becomes a conduit through which piracy and religion are joined. Given the importance of technology in criminal acts, especially in communicating, recruiting and locating targets etc, the use of social media becomes key in terrorising and hijacking vessels. Consequently, the article suggests a sociolegal strategy for curbing piracy by enforcing antipiracy legislation and using social media to reaffirm the significance of religion in crime reduction, including piracy
A Critical Discourse Analysis of Immigration Satire in Joe Wong’s Stand-Up Comedy
No full textPolitical satire has long been used as a means of critiquing societal structures and governmental policies, particularly in the realm of immigration, where humour can expose contradictions, stereotypes, and biases within dominant ideologies. This study analyses Joe Wong’s satirical performance on The Late Show with Stephen Colbert, focusing on how humour operates as a form of political commentary that challenges immigration policies and discourses associated with former U.S. President Donald Trump. Drawing on Van Dijk’s (2001) Critical Discourse Analysis (CDA) framework, the research examines Wong’s linguistic strategies, pragmatic flouting of conversational norms, narrative framing, and interaction with the live audience. Findings suggest that Wong’s satire disrupts discriminatory political narratives while fostering a sense of solidarity around immigrant identity, enabling audiences to critically reflect on exclusionary attitudes and power dynamics. By demonstrating how stand-up comedy can simultaneously entertain and resist political dominance, this study contributes to broader scholarship on political humour, discourse, and resistance, highlighting satire as a performative tool for challenging entrenched social prejudices and reimagining immigrant representation in the public spher
Enhancing Citrus Plant Health through the Application of Image Processing Techniques for Disease Detection
No full textThe foremost task in agriculture is the decisive identification of citrus plants and the timely identification of diseases in the plants with the aim of improving the quality of crops and the yield. In this work, a machine learning algorithm focuses on image processing of citrus to solve issues that are significant and cause concern in agriculture. This work focus on the machine learning models like VGG 19 and VGG 16. In addition, dataset curation, data augmentation and various other methods were employed. The dataset used in this research is a composed one which is recorded in a comprehensive manner including the data of both the affected and healthy pieces of citrus fruits. The ensemble model utilised here to ensure the improvement of trained datasets. Reviewing the research on machine learning models indicates a possibility for accurate classification of the fruits and disease detection models of the fruit. The three contenders performed admirably, with VGG 19 dominating with 95.5% accuracy. In second place was CNN with 93.4% and VGG 16 trailing at 91.2%. Such models are recognisable, because they perform well in agricultural environments, thanks to their precision, recall, and F1 scores, which are all balanced properly. The models’ capacity to lessen the number of false alarms and misses is further assessed with the use of confusion matrices, which are of utmost importance in disease control. New developments in early disease diagnosis and detection of citrus fruits in agriculture may greatly enhance the health and productivity of crops. This research can be critical in increasing agricultural productivity while ensuring the environmental sustainability and health of growers and citrus crops in the long run
A Comparative Study of Oracle ERP Netsuite and Microsoft Dynamics 365 Contributions to Contemporary Business Development in India
No full textERP systems centrally provide support to a company's business operations and are capable of providing solutions in the area of resource management, process streamlining, and making data-driven decisions. Acceptance of ERP systems is growing in India as companies begin to become more efficient and competitive. Among the most prominent players are Oracle ERP Netsuite and Microsoft Dynamics 365, both holding unique attributes which pose a challenge for businesses when choosing the right system. The aim of this paper of study is to compare between Oracle ERP Netsuite and Microsoft Dynamics 365 by exploring their contribution to business development in India. With respect to the implementation complexity, user satisfaction, ROI, and overall business impact, this research study weighs the merits and demerits of each platform according to India. Although high-end multinationals prefer it for its strength and integrated features, the SME prefers Microsoft Dynamics 365 because of its flexibility and smooth integration with other products of Microsoft. Basing the conclusion drawn from the afore findings, the study does provide recommendations to the Indian business in the choice of the most appropriate ERP system. Thus, valuable inputs into the understanding of the adoption of ERP in India are expected from this article
IoT-Based Nerve Stimulator for Women’s Safety
No full textWomen deserve a right to live free from intimidation, mistreatment, unfair treatment, and eliminating hurdles from a hazardous workplace can help them reach their maximum potential both personally and as contributions to economies, societies, and the workplace. Physical, emotional, and environmental safety have a variety of effects on wellbeing, including stress management, emotional stability, and physical health. The Women Safety System with Nerve Stimulator is a comprehensive system that integrates essential components for women's safety using an Arduino Uno microcontroller. It has an SOS button for emergency activation, a temperature sensor for environmental monitoring, a pulse oximeter for tracking health, a buzzer for auditory warnings, a relay for controlling other devices, a 5V DC vibration motor for tactile feedback, and a rechargeable battery for mobility. In an emergency, the Nerve Stimulator draws attention from nearby by producing controlled vibrations, which improves security. Ongoing global positioning system (GPS) monitoring guarantees accurate position awareness, and a buzzer warns users and anyone in the vicinity of possible hazards. The relay for controlling remote equipment adds to the system's versatility, while the SOS button triggers emergency actions like GPS location sharing. Rechargeable batteries provide continuous functioning, which in turn guarantees the dependability and efficacy of the system in protecting women's safety
A Hybrid Deep Learning VGG-16 Based SVM Model for Vehicle Type Classification
No full textCar classification is important in daily life because there are many distinct types of automobiles made by various manufacturers. Although there are numerous methods for classifying autos, machine learning technologies have not been widely utilized, resulting in low accuracy levels. The goal of this paper is to create a machine learning system that is especially made to categories models of two Pakistan's top automakers, Toyota, and Honda. Ten Toyota models such as Avalon, Land Cruiser, Camry, Corolla, C-HR, Highlander, Prius, Tundra, RAV4, and Yaris and a dataset of Honda automobiles, which also includes 10 models (Accord, Civic, CR-V, Fit, HR-V, Insight, Odyssey, Passport, Pilot, and Ridgeline), are used to evaluate the model's performance. A deep learning-based VGG integrated with support vector machine (SVM) is proposed, utilizing a dataset from Kaggle.com, providing high-definition images for multiple classes. Comparisons with other models such as VGG16, AlexNet, and Convolutional Neural Network (CNN) reveal that the suggested model (VGG16 + SVM) achieves superior accuracy. For the Toyota dataset, the proposed model achieves 99% accuracy, outperforming VGG16 (66%), AlexNet (52%), and CNN (65%). Similarly, for the Honda dataset, the suggested model achieves 98% accuracy, surpassing VGG16 (96%), AlexNet (71%), and CNN (82%). In conclusion, the proposed deep learning-based model demonstrates enhanced accuracy in classifying Toyota and Honda cars, highlighting its effectiveness for image-based classification tasks in the automotive domain.
Aspects-Based Sentiment Analysis of Extreme Weather on Twitter Using Long Short-Term Memory
No full textThis study presents an aspect-based sentiment analysis of tweets related to extreme weather events in Indonesia, utilizing the Long Short-Term Memory (LSTM) model. The dataset was obtained through a Twitter crawling process, followed by a series of preprocessing steps including data cleaning, stop word removal, normalization, tokenization, and stemming. The three primary areas of emphasis in the study were kinds of bad weather forecasts, and the government or society reactions. Using a lexicon-based technique, sentiment labelling generated three groups: positive, neutral, and negative. A random oversampling method was employed to address the data imbalance. The model using the LSTM algorithm was trained individually for aspect and sentiment classification tasks, so reaching high accuracies of 98.94% and 97.53%, respectively. The results indicate that the model effectively categorises talk on extreme weather and the opinions of the public. A word cloud visual representation was additionally created to show frequently occurring terms in the dataset, thereby offering insights into current themes and sentiment expressions. This work provides valuable input for government agencies and legislators in developing communication and disaster response plans, thereby serving to better understand the public's view on climate-related events. Future work could involve improving techniques for preprocessing and using larger, wider-ranging datasets for improving the model's robustness and generalisation
Sentiment Analysis and Topic Modelling on Twitter Related to Mobile Legends: Bang Bang Game Using Lexicon-Based, LDA, and SVM
No full textMobile Legends: Bang Bang (MLBB) has become a significant phenomenon within the global e-sports landscape, attracting millions of active players and fans. This study presents a comprehensive sentiment analysis and topic modelling of MLBB-related discussions on Platform Twitter, combining a lexicon-based approach, Latent Dirichlet Allocation (LDA), and Support Vector Machine (SVM) classification within a unified analytical pipeline. A dataset of 4,313 tweets was analysed, revealing that 70.8% expressed neutral sentiment, suggesting that much of the community's communication is informational rather than emotionally charged. Positive sentiments were associated with game content updates and rewards, while negative sentiments focused on technical and competitive issues. The SVM model achieved a sentiment classification accuracy of 90.57%, and cluster classification reached 85.13%. These findings offer valuable insights into how players engage with the game and reflect the underlying sentiments that influence the perception of gameplay and system updates. Furthermore, the predominance of neutral sentiment suggests opportunities for developers and content creators to enhance emotional resonance and community interaction through more engaging content and responsive design. The effectiveness of the combined methodology demonstrates the potential of integrating lexicon-based techniques with machine learning and topic modelling in analysing social media discourse within gaming communities. Future research is recommended to adopt advanced deep learning techniques, develop domain-specific sentiment lexicons, conduct multilingual sentiment analysis, and perform temporal tracking of community sentiment over time, enabling more dynamic and inclusive assessments of user experience and satisfaction
Real-time Read and Analysis of Air Pollution Produced from Private Electrical Generators in Mosul City using LoRaWAN
No full textThis study presents a novel, site-specific deployment of a Long Range Wide Area Network (LoRaWAN)-driven air pollution monitoring network specifically for the Iraqi city of Mosul, which is beset by widespread power outages and extensive utilization of decentralized diesel generators. While these generators mitigate electricity shortages, they are enormous contributors to urban air pollution, emitting high levels of CO2 and particulates. As opposed to previous studies, which concentrate on affluent urban areas, this research addresses a very deprived locale using an extensible low-power, low-cost LoRaWAN network and high-precision CO2 sensors (Sensirion SCD30 and MH-Z19) and The Things Network (TTN) for real-time data aggregation. With geo-referenced generator mapping integrated into the system, systematically distributed sensor nodes, and spatial interpolation via Geographic Information System (GIS), the system acquires seasonally varying emissions and identifies hotspots of pollution. Temperature and humidity data are incorporated to calibrate sensors so that the emission models are improved. Furthermore, the study conducts an operational evaluation of the LoRaWAN network over Mosul's urban densification, investigating link stability, RSSI, latency, and packet loss to verify network performance in actual conditions. The results highlight strong seasonal correlation between generator working and CO2 flux, reinforcing the climate-energy-emission nexus. Practically, LoRaWAN's infrastructure-independent and long-range design would be particularly apt to Mosul's connectivity-deficient terrain, serving as a robust platform for environmental monitoring and planning regulation. This research makes a significant contribution to the field by proposing an open, reproducible IoT-based framework for urban air quality control in energy-constrained regions and outlines future directions encompassing multi-pollutant sensing, mobile sensor nodes, and blockchain-secured data communication for enhanced trust and system reliability