Volume 20 Issue 9 2025

Image investigation systems maintain fundamental importance in digital information security. The correct and secure management of digital content depends on image forgery detection techniques. This research presents an effective method with explained functionality to detect forged images by using Local Binary Patterns (LBP) and Discrete Cosine Transform (DCT) features extracted from chrominance components. Local Binary Patterns (LBP) provide image forgery detection capability. The selected features for image forgery detection include LBP together with DCT obtained from image chrominance components. The proposed method segments the C-band Cr channels into blocks and applies LBP followed by DCT to capture textual and frequency-based artifacts indicative of tampering. These features are then classified using a Linear Support Vector Machine (SVM), achieving an accuracy of 92 percent in five-fold cross-validation on the combined CASIA 1.0 and CASIA 2.0 datasets. To enhance model transparency, we integrate the Local Interpretable Modelagnostic Explanations (LIME) framework, providing insight into the classifier’s decision-making process. This study proves the utility of the proposed method through measurement results, advancing the interpretability of the proposed approach and the reliability of image forensic systems.

A hospital is a healthcare facility that provides specialized medical and nursing care as well as medical supplies to patients. The most well-known form of the infirmary is the universal clinic, which usually carries an alternative subdivision to switch pressing strength issues such as fire and accident victims, as well as medical emergencies. A hospital is an organization of public health. It takes care of the health and diseases of people with the help of sophisticated equipment and instruments, by a group of specially trained persons. Areas of care discussions necessitate surgeons to efficiently interconnect complex data about a medicinal judgement and its scenario to the persistent and family, elicit information about patient preferences, provide support and make shared decisions, and ensure treatments and outcomes are aligned with the patient. The functions of hospitals are Patient care & education, diagnosis, treatment of illness, and rehabilitation. convalescent care. Clinic goals are frequently around educating persevering facility, quality of overhaul, staff maintenance and ability, hospice development, and monies. The goals whitethorn not altered much, but the way each goal is phrased, applied, and measured can have a huge impact on staff performance. Persevering experience is related to an affected role perception of care, although persistent approval is about the patient role expectations for care. Easy-going fulfilment is a significant and normally used indicator for mensuration the superiority of health care. Patient gratification disturbs experimental consequences, persistent retaining, and medicinal delinquency entitlements. The main intention of the study shows the imperative service excellence influences in healthcare and to determine the patients observing the service quality factors of healthcare.

Background: Cloud computing has become a vital infrastructure for modern data storage and sharing due to its scalability, cost-effectiveness, and accessibility. However, the untrusted nature of cloud environments raises significant concerns regarding data confidentiality and fine-grained access control. Attribute-Based Encryption (ABE) has emerged as a promising cryptographic technique, enabling secure data sharing by embedding access policies directly into ciphertext. While numerous ABE schemes exist, challenges remain in scalability, dynamic policy updates, and resistance to key compromise. Results: This paper surveys traditional access control models—Discretionary Access Control (DAC), Mandatory Access Control (MAC), Role-Based Access Control (RBAC), and Attribute-Based Access Control (ABAC)—and evaluates their applicability in cloud environments. A detailed classification of ABE schemes, including CP-ABE, KP-ABE, Hierarchical ABE, NonMonotonic ABE, and Outsourced/Distributed ABE, is presented with comparative analysis. Building on this, we propose an enhanced Temporal Attribute-Based Encryption (T-ABE) framework that integrates time-varying encryption with ABAC principles. The system decouples ciphertext from policies using an XML-based metadata file, ensuring dynamic and modular policy management. This design improves resilience against static key analysis, simplifies revocation, and enhances fine-grained access control in distributed cloud environments. Conclusion: The proposed T-ABE scheme addresses key limitations of conventional ABE models by introducing temporal constraints and metadata-driven access control. This makes it a more adaptive, secure, and scalable solution for real-world cloud applications. Future work will focus on implementing and benchmarking the scheme against existing ABE and ABAC models to evaluate its efficiency in policy updates, revocation handling, and access enforcement. “The key contribution of this work lies in proposing a novel Temporal Attribute-Based Encryption (T-ABE) framework that integrates time-varying encryption with metadata-driven policy management, thereby enabling secure, flexible, and fine-grained access control in cloud computing environments.”

The primary aim of this study is to examine the similarities between the expressions consider and allow for in American and British English and explore whether national variation influences their usage. In the Corpus of Contemporary American English (COCA), the genre-ranking similarity between the two expressions is 37.5% (matching rankings in 3 out of 8 genres), indicating limited overlap. In the British National Corpus (BNC), the similarity decreases to 14.28% across seven genres, further suggesting minimal genre-based alignment. Interestingly, genre-specific proximity varies: in COCA, consider is most dissimilar to allow for in academic texts and most similar in web genres; in the BNC, the greatest divergence appears in the miscellaneous genre, with the closest alignment in spoken genres. Both expressions show substantial frequency variation across genres in both corpora, with no significant national differences in their usage patterns. In COCA, consider appears between 7,519 and 20,137 times, while allow for ranges from 115 to 1,615. In the BNC, consider ranges from 334 to 2,938, and allow for from 8 to 230. Correlation and linear regression analyses further reveal strong positive relationships. In COCA, the correlation coefficient (R) is 0.8739, while in the BNC, it is 0.9186, with 84.39% of the variation in allow for explained by consider. The major finding of this study is that there is no stark difference between American and British English in seven different statistical analyses, including Chi-squared tests, which show significant differences in genre distribution across both corpora (p < 0.000001).

Student dropout is an extreme issue because it negatively affects the former institution, family, and society at large, not to mention the individual student who dropped out. To handle this problem, several efforts to use ML to forecast students dropping out have been made. The aim of the present research is to estimate the student dropout rate by testing the capabilities of several ML models to interact with multifaceted patterns and non-balanced data. LightGMB scored the largest at 86.14, which is above the rates of other models such as Gradient Boosting (79.40) and multi-layer perceptron (78.52). The good performance of LightGBM means that it is able to offer justifiable and fair findings on the other categories of students like dropout, enrolment, and graduation. This software could assist schools with detecting highrisk students at an earlier stage and spending resources more effectively to assist them and use advanced algorithms. The findings explain why machine learning has the potential to revolutionize student retention plans with data-driven insights that would enhance decision-making. In general, the article, as well, can be placed in the already-too-long queue of articles that permit the application of predictive analytics in schools in order to enhance student academic performance and dropout rates.

Cancer is second leading cause of morbidity and mortality in the present world. Early detection and prediction of uptake of cancer cells is important for patient care and can improve the chances of cure. Recent work has Integrated the IoT and Deep Learning models acts as a promising approach to diagnosis cancer early. According to this research a new model to predict early malignant cells diagnosis by using Internet of things (IoT) and an optimal deep learning approach. The proposed system combines the strengths of IoT in data collection and connectivity with the capabilities of deep learning for data analysis. The integration of these two technologies enables real-time monitoring and analysis of relevant physiological and biological data, facilitating early cancer detection. The key components of this system include sensor devices capable of monitoring vital signs, genetic markers, and other health-related data, which are connected to a central IoT platform. The collected data is then processed and analyzed by an optimized deep learning model. The Recurrent Neural Networks (RNN) model is fine-tuned and optimized to accurately identify early signs of cancer cells from the data, which can include anomalies, biomarkers, or other indicators. optimizing for feature selection, hyper-parameter optimization and model architecture design to improve cancer cell prediction accuracy. The algorithm is also based on adaptive learning to take into account new data and changing cancer profiles. In addition to early prediction, this system can provide personalized health recommendations and alerts to both patients and healthcare professionals. it holds promise to influence the cancer burden by facilitating early interventions and personalized therapies. The proposed model has good potential for early prediction of cancer cells and may result in better patient outcomes and lower healthcare costs. It highlights the synergy of IoT and deep learning as a promising duo in health care and disease management. This system still needs more research and development to prove its effectiveness and be included in clinical routine.

This paper provides empirical evidence explaining why the Korean pronoun ku ‘he’ cannot function as a bound variable in sentences with every-type quantificational phrases (QPs). We argue that the issue arises from number disagreement in Korean variable binding. Specifically, ku is unable to bind variables in these contexts because it does not agree in number or gender with the QP, leading to a mismatch. However, we show that plural pronouns like ku-tul ‘they’ can indeed yield bound variable readings, as they agree in number with plural every-type QPs. Moreover, ku can support bound variable readings in contexts involving singular indefinite QPs (e.g., nwukwunka ‘someone’) and wh-words (e.g., nwukwu ‘who’, nwukwu-uy ‘whose’), as these are semantically singular. In contrast, ku fails to yield bound variable readings with many-type QPs due to the number disagreement issue. Plural pronouns like ku-tul can support bound variable readings with many-type QPs, since they agree in number with plural antecedents. Additionally, ku-tul can be bound by the quantifier amwuto (‘nobody’), further confirming its plural nature. Importantly, ku is not the direct equivalent of the English pronoun he, which always supports bound variable readings with every-type QPs. Notably, ku cannot induce a bound variable reading with all-type QPs due to the number mismatch issue. This paper emphasizes the crucial role of number and gender agreement in variable binding, highlighting how these factors influence the availability of bound variable readings in Korean.

Introduction: Polycystic ovarian disease (PCOD) is a common endocrine-metabolic disorder in women of reproductive age, often presenting with irregular menstruation, hirsutism, obesity, and polycystic ovaries. Case summary: This case report describes a 23-year-old female with a two-year history of delayed, painful, and irregular menses, progressive weight gain, excessive hair growth, and moderate hair fall. Previous conventional therapy provided only temporary relief. Clinical evaluation revealed overweight status (BMI 27.43 kg/m²) with hirsutism, and ultrasonography confirmed bilateral polycystic ovaries. She was treated exclusively with individualized homoeopathic remedy Sepia officinalis in 200C and 1M potencies, prescribed according to the principles of posology outlined in the Organon of Medicine, based on constitutional analysis and repertorization using Synthesis Repertory. Over one year of follow-up, including six months of active medication, the patient achieved regular, painless cycles, 11 kg weight reduction, decreased hair growth, improved well-being, and ultrasonographic normalization of ovarian morphology. Conclusion: This case adds to the growing evidence supporting individualized homoeopathic management of PCOD and underscores the relevance of addressing both physical and emotional aspects of the disorder.The uniqueness of this case lies in the rapid and sustained clinical improvement supported withradiological investigation, achieved with individualized homoeopathy, emphasizing a holistic, non-invasive approach. Furthermore, the case was assessed using the MONARCH criteria, which demonstrated high therapeutic effectiveness.