Validating natural language security requirements are fully specified remains a persistent challenge in Security Requirements Engineering (SRE). Manual reviews are slow, error-prone, and rarely align systematically with international standards like the Common Criteria (CC). This paper evaluates whether the requirement completeness in smart-home IoT specifications can be audited deterministically by constraining transformer-based semantic embeddings within bounded vector spaces. To test this, an automated completeness checking approach is developed within the proposed framework. The methodology integrates text processing tools (nltk, python-docx, PyPDF2, Crypto.Cipher) with fine-tuned BERT-base-uncased embeddings built using PyTorch and Hugging Face Transformers. The framework evaluates requirement completeness on two levels. First, term-level completeness is measured via cosine semantic similarity against expert-validated CC keywords. Second, relationship-level completeness is tracked using a dual metric combining cosine similarity and Euclidean distance. Additionally, an all-MiniLM-L6-v2 topic modeling layer automatically categorizes requirements into primary objectives: authentication, communication, and system configuration. Grounded on a 174-sample expert corpus and a 680-sample commercial IoT dataset, human expert annotations achieved a Fleiss Kappa inter-rater agreement of 0.602. Data scarcity constraints were resolved using context-aware synonym data augmentation and Repeated Stratified 5-Fold Cross-Validation. The model achieved a classification accuracy of 77.8% on expert data and 89.7% on commercial IoT specifications. The proposed framework significantly outperformed traditional Term Frequency-Inverse Document Frequency (TF-IDF) and Support Vector Machine (SVM) baselines.

The growth of WiFi-enabled smart devices and high-bandwidth applications has generated a huge demand for intelligent wireless network management mechanisms that can offer Quality of Service (QoS), seamless mobility and resource efficiency. The conventional WiFi association mechanisms are primarily based on the Received Signal Strength Indicator (RSSI) selection, leading to uneven utilization of Access Points (AP), congestion, excessive handovers and degraded network performance in dense wireless environments. To overcome these challenges, in this paper we propose a multi-objective AP load balancing and handover scheme based on Software-Defined WiFi load balancing (SDW-LB) mechanism for the overlapping wireless coverage regions. The proposed framework leverages the centralized intelligence and global network view of Software Defined Networking (SDN) for dynamic management of user association and handover decision. When the user enters the overlapped area of multiple APs, the SDN controller will initiate an intelligent handover process based on a multi-objective optimization model in terms of RSSI strength, AP load, number of connected users, available bandwidth, channel utilization, packet delay, user mobility and throughput conditions. A weighted utility-based objective function is presented to select the most suitable AP while reducing network congestion and superfluous handovers. Moreover, a centralized SDN controller i.e., Open Network Operating System (ONOS), constantly observes the network environment and makes dynamic adjustments to the AP association decisions for the purpose of balanced resource allocation and enhanced QoS. The proposed scheme can enhance the network throughput, reduce the packet loss, relieve the AP congestion, and improve the user experience in dense WiFi deployments. The experimental results of our proposed approach show the effectiveness of the proposed scheme compared with benchmark methods with respect to QoS metrics such as fairness, delay, throughput, and packet loss ratio.

The healthcare sector involves many steps to ensure efficient care for patients, such as appointment scheduling, consultation plans, online follow-up, and more. However, existing healthcare mechanisms are unable to facilitate a large number of patients, as these systems are centralized and hence vulnerable to various issues, including single points of failure, performance bottlenecks, and substantial monetary costs. Furthermore, these mechanisms are unable to provide an efficient mechanism for saving data against unauthorized access. To address these issues, this study proposes a blockchain-based authentication mechanism that authenticates all healthcare stakeholders based on their credentials. Furthermore, also utilize the capabilities of the InterPlanetary File System (IPFS) to store the Electronic Health Record (EHR) in a distributed way. This IPFS platform addresses not only the issue of high data storage costs on blockchain but also the issue of a single point of failure in the traditional centralized data storage model. The simulation results demonstrate that our model outperforms the benchmark schemes and provides an efficient mechanism for managing healthcare sector operations. The results show that it takes approximately 3.5 s for the smart contract to authenticate the node and provide it with the decryption key, which is ultimately used to access the data. The simulation results show that our proposed model outperforms existing solutions in terms of execution time and scalability. The execution time of our model smart contract is around 9000 transactions in just 6.5 s, while benchmark schemes require approximately 7 s for the same number of transactions.

ABSTRACT To meet the demands of modern technologies such as 5G, big data, edge computing, precision, and sustainable agriculture, the combination of Internet‐of‐Things (IoT) with software‐defined networking (SDN) known as SD‐IoT is suggested to automate the network by leveraging the programmable and centralized SDN interfaces. The previous literature has suggested quality‐of‐service (QoS) aware flow processing using manual strategies or heuristic algorithms, however, these schemes proposed with white‐box approaches do not provide effective results as the network scales or dynamic changes are happening. This article proposes a novel QoS provision strategy using deep reinforcement learning (DRL) to calculate the optimal routes autonomously for SD‐IoT traffic. To satisfy the different demands of flows in the SD‐IoT network the flows are divided into two types. Hence, based on their service demand the routes are generated for them as per service request. The scenario is explained with precision agriculture based on SD‐IoT and results are compared with benchmark strategies. A real internet topology is used for the evaluation of results. The results indicated that the proposed method gives improvements for QoS such as delay, throughput, packet loss rate, and jitter compared with benchmark models.

Image watermarking is a powerful tool for media protection and can provide promising results when combined with other defense mechanisms. Image watermarking can be used to protect the copyright of digital media by embedding a unique identifier that identifies the owner of the content. Image watermarking can also be used to verify the authenticity of digital media, such as images or videos, by ascertaining the watermark information. In this paper, a mathematical chaos-based image watermarking technique is proposed using discrete wavelet transform (DWT), chaotic map, and Laplacian operator. The DWT can be used to decompose the image into its frequency components, chaos is used to provide extra security defense by encrypting the watermark signal, and the Laplacian operator with optimization is applied to the mid-frequency bands to find the sharp areas in the image. These mid-frequency bands are used to embed the watermarks by modifying the coefficients in these bands. The mid-sub-band maintains the invisible property of the watermark, and chaos combined with the second-order derivative Laplacian is vulnerable to attacks. Comprehensive experiments demonstrate that this approach is effective for common signal processing attacks, i.e., compression, noise addition, and filtering. Moreover, this approach also maintains image quality through peak signal-to-noise ratio (PSNR) and structural similarity index metrics (SSIM). The highest achieved PSNR and SSIM values are 55.4 dB and 1. In the same way, normalized correlation (NC) values are almost 10%-20% higher than comparative research. These results support assistance in copyright protection in multimedia content.

The ensemble of Information and Communication Technology (ICT) and Artificial Intelligence (AI) has catalysed many developments and innovations in the automotive industry. 6G networks emerge as a promising technology for realising Intelligent Transport Systems (ITS), which benefits the drivers and society. As the network is highly heterogeneous and robust, the physical layer security and node reliability of the vehicles hold paramount significance. This work presents a novel methodology that integrates the prowess of computer vision techniques and the Lightweight Super Learning Ensemble (LSLE) of Machine Learning (ML) algorithms to predict the presence of intruders in the network. Furthermore, our work utilizes a Deep Convolutional Neural Network (DCNN) to detect obstacles by identifying the Region of Interest (ROI) in the images. As the network utilizes mm-waves with shorter wavelengths, Intelligent Reflecting Surfaces (IRS) are employed to redirect signals to legitimate nodes, thereby mitigating the malicious activity of intruders. The experimental simulation shows that the proposed LSLE outperforms the state-of-the-art techniques in terms of accuracy, False Positive Rate (FPR), Recall, F1-Score, and Precision. A consistent performance improvement with an average FPR of 85.08% and accuracy of 92.01% is achieved by the model. Thus, in the future, detecting moving obstacles and real-time network traffic monitoring can be included to achieve more realistic results.

Software-Defined Networking (SDN) offers a centralized network management approach that can effectively address the complex and varied traffic demands characteristic of Industrial Internet of Things (IIoT) environments by decoupling the control plane from the data plane. The centralized control architecture of SDN necessitates the performance optimization of controllers to manage diverse traffic efficiently within IIoT applications. This paper explores the criteria for selecting controllers in SDN-enabled IIoT (SD-IIoT) environments, utilizing the Less Complex Analytic Network Process (LC-ANP) to establish their prioritization. A ranking system for SD-IIoT controllers is formulated using LC-ANP, and experimental validation of this method underscores its effectiveness in optimizing controller performance. The proposed approach enhances the overall efficiency of SDN-enabled IIoT networks, as evidenced by experimental evaluations measuring delay, throughput, packet loss ratio (PLR), and jitter across five different topologies with varying nodes and edges. The experiments indicate an overall increase in the average throughput, and a decrease in delay, jitter, and PLR. The results also show that the suggested strategy and proposed controller surpass the benchmark controller in complex network topologies. These results confirm the method’s capacity to significantly improve network performance in SD-IIoT applications.

Automation is a novel approach that can enhance production capacity, work quality, and working environment, while minimizing labor disputes by automating all handling parameters. Formal methods are scientific techniques used to design complex mathematical systems. They involve specifying requirements and verifying software systems. The card swipe machine is a widely used point-ofsale terminal in supermarkets, medical centers, and shopping malls. Customers can easily make payments through these machines, which also provide detailed receipts of all transactions, including reversed transactions. This resolves cash management issues, improves customer service, and supports marketing. While multiple conventional machines are available in the market, they lack visual representations, making it difficult to understand their working mechanism without graphical representations. Deterministic finite automata (DFA) is a mathematical model that has limited states and moves from one state to another based on input and transition functions. This study proposes the use of the JFLAP software to create a mathematical model of card swipe machine transactions. The proposed model allows for viewing each processing step in a card swipe machine, offering a new approach to understanding their working mechanism.

With technological advancements, smart health monitoring systems are gaining growing importance and popularity. Today, business trends are changing from physical infrastructure to online services. With the restrictions imposed during COVID-19, medical services have been changed. The concepts of smart homes, smart appliances, and smart medical systems have gained popularity. The Internet of Things (IoT) has revolutionized communication and data collection by incorporating smart sensors for data collection from diverse sources. In addition, it utilizes artificial intelligence (AI) approaches to control a large volume of data for better use, storing, managing, and making decisions. In this research, a health monitoring system based on AI and IoT is designed to deal with the data of heart patients. The system monitors the heart patient's activities, which helps to inform patients about their health status. Moreover, the system can perform disease classification using machine learning models. Experimental results reveal that the proposed system can perform real-time monitoring of patients and classify diseases with higher accuracy.

With the advancement in information technology, digital data stealing and duplication have become easier. Over a trillion bytes of data are generated and shared on social media through the internet in a single day, and the authenticity of digital data is currently a major problem. Cryptography and image watermarking are domains that provide multiple security services, such as authenticity, integrity, and privacy. In this paper, a digital image watermarking technique is proposed that employs the least significant bit (LSB) and canny edge detection method. The proposed method provides better security services and it is computationally less expensive, which is the demand of today’s world. The major contribution of this method is to find suitable places for watermarking embedding and provides additional watermark security by scrambling the watermark image. A digital image is divided into non-overlapping blocks, and the gradient is calculated for each block. Then convolution masks are applied to find the gradient direction and magnitude, and non-maximum suppression is applied. Finally, LSB is used to embed the watermark in the hysteresis step. Furthermore, additional security is provided by scrambling the watermark signal using our chaotic substitution box. The proposed technique is more secure because of LSB’s high payload and watermark embedding feature after a canny edge detection filter. The canny edge gradient direction and magnitude find how many bits will be embedded. To test the performance of the proposed technique, several image processing, and geometrical attacks are performed. The proposed method shows high robustness to image processing and geometrical attacks.

Cerebral microbleeds (CMBs) in the brain are the essential indicators of critical brain disorders such as dementia and ischemic stroke. Generally, CMBs are detected manually by experts, which is an exhaustive task with limited productivity. Since CMBs have complex morphological nature, manual detection is prone to errors. This paper presents a machine learning-based automated CMB detection technique in the brain susceptibility-weighted imaging (SWI) scans based on statistical feature extraction and classification. The proposed method consists of three steps: (1) removal of the skull and extraction of the brain; (2) thresholding for the extraction of initial candidates; and (3) extracting features and applying classification models such as random forest and naïve Bayes classifiers for the detection of true positive CMBs. The proposed technique is validated on a dataset consisting of 20 subjects. The dataset is divided into training data that consist of 14 subjects with 104 microbleeds and testing data that consist of 6 subjects with 63 microbleeds. We were able to achieve 85.7% sensitivity using the random forest classifier with 4.2 false positives per CMB, and the naïve Bayes classifier achieved 90.5% sensitivity with 5.5 false positives per CMB. The proposed technique outperformed many state-of-the-art methods proposed in previous studies.

In the contemporary era, data holds a vital significance, and ensuring the security of data stands as an exceptionally challenging endeavor. Both industry and academia dedicate substantial efforts to mitigate security breaches. Given the evolving nature of modern cyber threats, there arises a compelling necessity for robust cybersecurity measures concerning digital content. We propose the merits of digital watermarking within the realm of cybersecurity for digital content. Researchers within the watermarking domain are diligently striving to offer the utmost security services. Unfortunately, achieving both robustness and security concurrently within these techniques has proven to be a formidable task. Hence, we delve into a novel approach for image watermarking, which draws inspiration from gradient-based methodologies. This scheme hinges upon compass edge detection, the least significant bit mechanism, and the utilization of chaotic encryption for watermark embedding. The effectiveness and performance are rigorously evaluated against a spectrum of geometrical and image-processing attacks.

In the modern age, watermarking techniques are mandatory to secure digital communication over the internet. For an optimal technique, a high signal-to-noise ratio and normalized correctional is required. In this paper, a digital watermarking technique is proposed on the basis of the least significant bit through an image gradient and chaotic map. The image is segmented into noncorrelated blocks, and the gradient of each block is calculated. The gradient of the image expresses the rapid changes in an image. A chaotic substitution box (S-Box) is used to scramble the watermark according to a piecewise linear chaotic map (PWLCM). PWLCM has a positive Lyapunov exponent and better balance property as compared to other chaotic maps. This S-Box technique is capable of producing a disperse sequence with high nonlinearity in the generated sequence. Least significant bit is a simple technique for embedding but it has a high payload capacity and direct pixel manipulation. The embedding payload introduces a tradeoff between robustness and imperceptibility; hence, the image gradient is a technique to identify the best-suited place to embed a watermark and avoid image degradation. By modifying the least significant bits of the original image, the watermark signal is embedded according to the image gradient. In the image gradient, the direction and magnitude decide how much embeding can be done. In comparison with other methods, the experimental results show satisfactory progress in robustness against several image processing and geometrical attacks while maintaining the imperceptibility of the watermark signal.

The inspiration driving android and voice automated smart wheelchair venture is to construct a smart wheelchair that helps physically impaired people to locomote from one spot then onto the next. To overcome this disability, a smart voice-controlled fully automated wheelchair is designed for physically disabled, patients, or pregnant women. This smart wheelchair will help them move from one place to another without any problem. Numerous wheelchairs are accessible with various running advancements, yet the expense is high and it isn’t much successful. For the most part, designing voice and android control wheelchairs is to conquer a few burdens of the current frameworks. The customer needs to interact with the wheelchair with the help of the application. This framework enables the client to vigorously communicate with the wheelchair at various dimensions of the control (turn left, turn right, proceed, return and stop). This task utilizes a microcontroller circuit and motor drivers to make the development of the wheelchair.

In information dissemination, media plays a vital role, a huge amount of information is spread quickly through social media and news platforms. The online news outlet and frequency of the huge number of headlines creation demand automatic classification tools. In this overwhelming domain, different countries explored classification algorithms for their news. However, very limited research is done on Pakistani news headlines in terms of classification. There is also a lack of a Pakistani news headlines benchmark dataset. This research is intended to find a suitable model to classify Pakistani news headlines automatically. Moreover, in this study, we designed a benchmark NCE-2D (News Classification and Emotion Detection Dataset) of 2429 Pakistani news headlines extracted from different news websites using ParseHub, which includes five types of categories. First, to minimize the noise from the dataset is undergoing some pre-processing steps including punctuation, stopwords, null entries, and duplication removal. In the next step, extract the feature by two different methods Count-Vectorizer and TF-IDF Vectorizer, and then apply a set of 10 different learning algorithms for both types of features extracted. The best performing algorithms for Pakistani news headlines out of the set of implemented algorithms by both combinations included Support Vector Machine (SVM) when implemented over the TF-IDF features and Multi-Layer Perception (MLP) when implemented over the CV features. Based on the results, both these algorithms are compared to choose the most suitable from them. The combination of SVM is investigated most appropriate classifier with 82.16% accuracy than 81.75% accuracy of MLP.

The significant role of Proprotein convertase subtilisin/Kexin family 7 and 9 (PCSK/7/9) is, to activate other proteins. In common practice, many proteins are inactive when they firstly synthesize. Several reasons are investigating in this regard in which when the process of activations is started then block its activity due to a long chain of amino acids. The PCSK remove these chains and activate the protein. For this duty, the PCSK is familiar in the domain of medical industry and bioinformatics where several medicines are prepared for the cure of different diseases like cancer, viral infection, inflammation and hypercholesterolemia. Many proprotein convertases like PACE4 and furin are involved in the pathological process of these diseases. By using the motif detection technique, the role of PCSK/7/9 is identified in that region where the activation of another protein start. Multiple tools and practices exist in the literature to identify the motifs in varied meadows like neural networks, metabolic ways, DNA/RNA sequences, antigen protein, and Protein-Protein Interactions (PPI). This paper provides Motif detection, for PCSK/7/9 and its role in the activation of the protein in cancer. Our purposed approach identifies those motifs which cause different diseases like cancer which provides improved results with less computational time as compared to other methods.

The Internet of Underwater Things (IoUTs) enables various underwater objects be connected to accommodate a wide range of applications, such as oil and mineral exportations, disaster detection, and tracing tracking systems. As about 71% of our earth is covered by water and one-fourth of the population lives around this, the IoUT expects to play a vital role. It is imperative to pursue reliable communication in this vast domain, as human beings' future depends on water activities and resources. Therefore, there is a urgent need for underwater communication to be reliable, end-to-end secure, and collision/void node-free, especially when the routing path is established between sender and sonobuoys. The foremost issue discussed in this area is its routing path, which has high security and bandwidth without simultaneous multiple reflections. Short communication range is also a problem (because of an absence of inter-node adjustment); the acoustic signals have short ranges and maximum-scaling factors that cause a delay in communication. Therefore, we proposed Rotational Orbit-Based Inter Node Adjustment (ROBINA) with variant Path-Adjustment (PA-ROBINA) and Path Loss (PL-ROBINA) for IoUTs to achive reliable communication between the sender and sonobuoys. Additionally, the mathematical-based path loss model was discussed to cover the PL-ROBINA strategy. Extensive simulations were conducted with various realistic parameters and the results were compared with state-of-the-art routing protocols. Extensive simulations proved that the proposed routing scheme outperformed different realistic parameters; for example, packet transmission 45% increased with an average end-to-end delay of only 0.3% respectively. Furthermore, the transmission loss and path loss (measured in dB) were 25 and 46 dB, respectively, compared with other algorithms, for example, EBER2 54%, WDFAD-BDR 54%, AEDG 49%, ASEGD 55%, AVH-AHH-VBF 54.5%, and TANVEER 39%, respectively. In addition, the individual parameters with ROBINA and TANVEER were also compared, in which ROBINA achieved a 98% packet transmission ratio compared with TANVEER, which was only 82%.

With the growth of information technologies, E-industry safety has recently become the mutual attention of education and business firms. Digital image watermarking is a technique that refers to the security of multimedia data. It is a process referred to the security and authentication of a digital image, video, and audio by embedding a watermark. Watermarking technique applies a number of variable editions to the host content, where the addition is related to embed information. In the past, researchers develop multiple simple watermarking techniques, today race is to find a region where the watermark is imperceptible and have a high payload. In this paper, an invisible image watermarking technique based on the least significant bit (LSB) and laplacian filter is proposed. The original image is divided into blocks and the laplacian filter is applied on each block. Laplacian is a derivative filter that uses the second derivate to find out the area of rapid changes in the image and the least significant bit is a technique to embed a watermark into the bit positions. Watermark is embedded on these regions which is favourable in achieving high desirable properties. This technique shows strong robustness against image processing and geometrical attacks. In evaluation with state of art methods, the proposed technique shows satisfactory progress.

The technology of smart farming (IoT-based monitoring and control system) has gone through rapid technological advancements and there is an increase in demand for reliable and efficient automatic smart farming systems. In controlled agricultural areas, it is very difficult for farmers to monitor and control their fields all the time. Therefore, an IoT-based smart model is designed for monitoring and controlling farms where environmental parameters can be accessed remotely at any time anywhere in the world. In this model, different sensors are interfaced with a microcontroller including temperature and humidity sensors, soil moisture sensor, barometric pressure sensor, flame sensor, smoke sensor, and DC motor to monitor and control environmental parameters in the agriculture field respectively. The acquired environmental parameters are sent on the Ubidots cloud server where the data is stored and updated every 5 seconds. The environmental parameters are sent on the Ubidots server through an Ethernet shield in the form of packets where the data can be visualized in the form of graphs.

The increase in energy consumption in the residential sector due to overpopulation has adverse effects on the power system. The traditional power grid is unable to handle a sustainable supply of energy for consumers. The adoption of smart grid and distributed generation sources (DGs) can play a vital role to cope with this challenge of a constant supply of energy retainment. In this paper, DGs like photovoltaic (PV) and battery storage system (BSS) are considered with home to grid (H2G) mutual exchange for residential consumers energy cost reduction based upon real time price signal (RTP). The Crow Search Optimization Algorithm (CSOA) has been applied for energy management controller (EMC) in home energy management (HEM) system to schedule home appliances optimally. It is evident from the analysis that DGs and H2G are important in minimizing consumption cost by an effective HEM system. The results attained from the CSOA proves that the approach is useful for residential consumers. The energy consumption cost is minimized in the proposed scheme taking consumers priorities into consideration. The results are helpful to initiate different demand response programs in the context of both consumers and power system operator.