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The field of Medicine and Healthcare has attained revolutionary advancements in the last forty years. Within this period, the actual reasons behind numerous diseases were unveiled, novel diagnostic methods were designed, and new medicines were developed. Even after all these achievements, diseases like cancer continue to haunt us since we are still vulnerable to them. Cancer is the second leading cause of death globally; about one in every six people die suffering from it. Among many types of cancers, the lung and colon variants are the most common and deadliest ones. Together, they account for more than 25% of all cancer cases. However, identifying the disease at an early stage significantly improves the chances of survival. Cancer diagnosis can be automated by using the potential of Artificial Intelligence (AI), which allows us to assess more cases in less time and cost. With the help of modern Deep Learning (DL) and Digital Image Processing (DIP) techniques, this paper inscribes a classification framework to differentiate among five types of lung and colon tissues (two benign and three malignant) by analyzing their histopathological images. The acquired results show that the proposed framework can identify cancer tissues with a maximum of 96.33% accuracy. Implementation of this model will help medical professionals to develop an automatic and reliable system capable of identifying various types of lung and colon cancers.

Due to the value and importance of patient health records (PHR), security is the most critical feature of encryption over the Internet. Users that perform keyword searches to gain access to the PHR stored in the database are more susceptible to security risks. Although a blockchain-based healthcare system can guarantee security, present schemes have several flaws. Existing techniques have concentrated exclusively on data storage and have utilized blockchain as a storage database. In this research, we developed a unique deep-learning-based secure search-able blockchain as a distributed database using homomorphic encryption to enable users to securely access data via search. Our suggested study will increasingly include secure key revocation and update policies. An IoT dataset was used in this research to evaluate our suggested access control strategies and compare them to benchmark models. The proposed algorithms are implemented using smart contracts in the hyperledger tool. The suggested strategy is evaluated in comparison to existing ones. Our suggested approach significantly improves security, anonymity, and monitoring of user behavior, resulting in a more efficient blockchain-based IoT system as compared to benchmark models.

An IoT-based wireless sensor network (WSN) comprises many small sensors to collect the data and share it with the central repositories. These sensors are battery-driven and resource-restrained devices that consume most of the energy in sensing or collecting the data and transmitting it. During data sharing, security is an important concern in such networks as they are prone to many threats, of which the deadliest is the wormhole attack. These attacks are launched without acquiring the vital information of the network and they highly compromise the communication, security, and performance of the network. In the IoT-based network environment, its mitigation becomes more challenging because of the low resource availability in the sensing devices. We have performed an extensive literature study of the existing techniques against the wormhole attack and categorised them according to their methodology. The analysis of literature has motivated our research. In this paper, we developed the ESWI technique for detecting the wormhole attack while improving the performance and security. This algorithm has been designed to be simple and less complicated to avoid the overheads and the drainage of energy in its operation. The simulation results of our technique show competitive results for the detection rate and packet delivery ratio. It also gives an increased throughput, a decreased end-to-end delay, and a much-reduced consumption of energy.

The proliferation of the internet of things (IoT) technology has led to numerous challenges in various life domains, such as healthcare, smart systems, and mission-critical applications. The most critical issue is the security of IoT nodes, networks, and infrastructures. IoT uses the routing protocol for low-power and lossy networks (RPL) for data communication among the devices. RPL comprises a lightweight core and thus does not support high computation and resource-consuming methods for security implementation. Therefore, both IoT and RPL are vulnerable to security attacks, which are broadly categorized into RPL-specific and sensor-network-inherited attacks. Among the most concerning protocol-specific attacks are rank attacks and wormhole attacks in sensor-network-inherited attack types. They target the RPL resources and components including control messages, repair mechanisms, routing topologies, and sensor network resources by consuming. This leads to the collapse of IoT infrastructure. In this paper, a lightweight multiclass classification-based RPL-specific and sensor-network-inherited attack detection model called MC-MLGBM is proposed. A novel dataset was generated through the construction of various network models to address the unavailability of the required dataset, optimal feature selection to improve model performance, and a light gradient boosting machine-based algorithm optimized for a multiclass classification-based attack detection. The results of extensive experiments are demonstrated through several metrics including confusion matrix, accuracy, precision, and recall. For further performance evaluation and to remove any bias, the multiclass-specific metrics were also used to evaluate the model, including cross-entropy, Cohn’s kappa, and Matthews correlation coefficient, and then compared with benchmark research.

Long-range radio (LoRa) communication is a widespread communication protocol that offers long range transmission and low data rates with minimum power consumption. In the context of solid waste management, only a low amount of data needs to be sent to the remote server. With this advantage, we proposed architecture for designing and developing a customized sensor node and gateway based on LoRa technology for realizing the filling level of the bins with minimal energy consumption. We evaluated the energy consumption of the proposed architecture by simulating it on the Framework for LoRa (FLoRa) simulation by varying distinct fundamental parameters of LoRa communication. This paper also provides the distinct evaluation metrics of the the long-range data rate, time on-air (ToA), LoRa sensitivity, link budget, and battery life of sensor node. Finally, the paper concludes with a real-time experimental setup, where we can receive the sensor data on the cloud server with a customized sensor node and gateway.

With continuous advancements in Internet technology and the increased use of cryptographic techniques, the cloud has become the obvious choice for data sharing. Generally, the data are outsourced to cloud storage servers in encrypted form. Access control methods can be used on encrypted outsourced data to facilitate and regulate access. Multi-authority attribute-based encryption is a propitious technique to control who can access encrypted data in inter-domain applications such as sharing data between organizations, sharing data in healthcare, etc. The data owner may require the flexibility to share the data with known and unknown users. The known or closed-domain users may be internal employees of the organization, and unknown or open-domain users may be outside agencies, third-party users, etc. In the case of closed-domain users, the data owner becomes the key issuing authority, and in the case of open-domain users, various established attribute authorities perform the task of key issuance. Privacy preservation is also a crucial requirement in cloud-based data-sharing systems. This work proposes the SP-MAACS scheme, a secure and privacy-preserving multi-authority access control system for cloud-based healthcare data sharing. Both open and closed domain users are considered, and policy privacy is ensured by only disclosing the names of policy attributes. The values of the attributes are kept hidden. Characteristic comparison with similar existing schemes shows that our scheme simultaneously provides features such as multi-authority setting, expressive and flexible access policy structure, privacy preservation, and scalability. The performance analysis carried out by us shows that the decryption cost is reasonable enough. Furthermore, the scheme is demonstrated to be adaptively secure under the standard model.

Saudi Arabic Sign Language (SArSL) recognition poses significant challenges due to its complex spatio-temporal structure and the scarcity of annotated datasets. This paper introduces a self-supervised learning framework built upon the Video Momentum Contrast (VideoMoCo) paradigm integrated with a 3D ResNet-50 backbone, designed to jointly capture spatial and temporal gesture dependencies. The proposed model is pretrained on 18,000 unlabeled gesture videos and subsequently fine-tuned on the KARSL-502 dataset containing 15,400 labeled samples covering 502 distinct classes. Experimental evaluation shows that the model attains an F1-score of 92.7%, outperforming CNN-LSTM (86.0%) and Two-Stream CNN (84.5%) baselines—an improvement of nearly 9% points. Beyond accuracy, the framework demonstrates strong robustness to class imbalance, motion variation, and visual noise, while maintaining efficient deployment performance with an inference latency of 12 ms per batch. The ablation study verifies the contribution of the momentum encoder and large negative sample queue in achieving stable and discriminative feature learning. Overall, the VideoMoCo–ResNet-50 framework establishes a scalable and inclusive foundation for real-time SArSL recognition, advancing accessibility for the Saudi Deaf community and supporting future multimodal extensions.

A plasmonic material-coated circular-shaped photonic crystal fiber (C-PCF) sensor based on surface plasmon resonance (SPR) is proposed to explore the optical guiding performance of the refractive index (RI) sensing at 1.7–3.7 μm. A twin resonance coupling profile is observed by selectively infiltrating liquid using finite element method (FEM). A nano-ring gold layer with a magnesium fluoride (MgF2) coating and fused silica are used as plasmonic and base material, respectively, that help to achieve maximum sensing performance. RI analytes are highly sensitive to SPR and are injected into the outmost air holes of the cladding. The highest sensitivity of 27,958.49 nm/RIU, birefringence of 3.9 × 10−4, resolution of 3.70094 × 10−5 RIU, and transmittance dip of −34 dB are achieved. The proposed work is a purely numerical simulation with proper optimization. The value of optimization has been referred to with an experimental tolerance value, but at the same time it has been ensured that it is not fabricated and tested. In summary, the explored C-PCF can widely be eligible for RI-based sensing applications for its excellent performance, which makes it a solid candidate for next generation biosensing applications.

A central limitation of existing temporal image analysis and video understanding models lies in their reliance on explicit motion cues, dense supervision, or auxiliary modalities, which constrains their ability to infer latent temporal structure, evolving semantic states, and long-range dependencies from silent image sequences. This limitation becomes critical in settings where temporal meaning emerges implicitly from stable visual representations rather than explicit frame-to-frame dynamics. In this work, we propose CATS (Context-Aware Temporal Synthesis), a mathematically grounded and interpretable framework for temporal reasoning that operates directly on silent image sequences and general temporal signals. CATS integrates curvature-aware temporal alignment, symmetry-enforced attention, slot-based nonlinear recurrence, and semantic memory fusion to model temporal coherence under noise, partial observability, and unordered inputs. Unlike conventional spatiotemporal architectures, CATS does not assume fixed temporal ordering or handcrafted motion representations, enabling robust temporal abstraction across heterogeneous domains. We validate the proposed framework primarily on silent egocentric video understanding tasks and further assess its robustness and generality through controlled cross-domain temporal stress tests, including stochastic diffusion modeling (ANDI), reinforcement-based temporal alignment, and cyber-physical time-series forecasting. In particular, we demonstrate that the same architecture trained on visual data transfers effectively to the Anomalous Diffusion (ANDI) benchmark, where CATS organizes particle trajectories in latent time and separates diffusion regimes without architectural modification. This cross-domain consistency confirms that CATS captures intrinsic temporal structure rather than dataset-specific cues. Across visual and non-visual tasks, CATS consistently outperforms competitive baselines, achieving up to 15% relative improvement in mAP and -score on egocentric video understanding, stable regime separation and accuracy gains on anomalous diffusion dynamics, and lower forecasting error in cyber-physical time-series prediction, while maintaining stable convergence under CPU-only constraints and providing interpretable attention and memory dynamics. By unifying temporal alignment, memory, and reasoning within a principled mathematical framework, CATS establishes a domain-agnostic approach to temporal understanding, advancing the state of the art in interpretable temporal reasoning for computer vision and beyond.

Medical Imaging Segmentation is an essential technique for modern medical applications. It is the foundation of many aspects of clinical diagnosis, oncology, and computer-integrated surgical intervention. Although significant successes have been achieved in the segmentation of medical images, DL (deep learning) approaches. Manual delineation of OARs (organs at risk) is vastly dominant but it is prone to errors given the complex irregularities in shape, low texture diversity between tissues and adjacent blood area, patient-wide location of organisms, and weak soft tissue contrast across adjacent organs in CT images. Till now several models have been implemented on multi organs segmentation but not caters to the problem of imbalanced classes some organs have relatively small pixels as compared to others. To segment OARs in thoracic CT images, we proposed the model based on the encoder-decoder approach using transfer learning with the efficientnetB7 DL model. We have built a fully connected CNN (Convolutional Neural network) having 5 layers of encoding and 5 layers of decoding with efficientnetB7 specifically to tackle imbalance class pixels in an accurate way for the segmentation of OARs. Proposed methodology achieves 0.93405 IOU score, 0.95138 F1 score and class-wise dice score for esophagus 0.92466, trachea 0.94257, heart 0.95038, aorta 0.9351 and background 0.99891. The results showed that our proposed framework can be segmented organs accurately.