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A face enhancement has the potential to play an important part in providing satisfactory and vast information to the face recognition performance. Therefore, a new approach for nonuniform illumination face enhancements (NIFE) was proposed by designing an adaptive contrast-stretching (ACS) filter. In a more objective manner of achieving this, an investigation usage of CS function with adjustable factors value to summarise its influence on the NIFE is examined firstly. Secondly, describe a new strategy to cater for CS adaptive factors prediction using training and testing phases. A dispersion versus location (DL) descriptor was examined in the training phase to generate the faces feature vectors. Subsequently, a frame differencing module (FDM) was developed for faces label generations. In the testing phase, the approach was examined to recognise the DL descriptor and predict face label based vocabulary tree model (VTM). Thirdly, the VTM performance was examined by referring to the area under curve (AUC) score from the receiver operating characteristic (ROC). The face quality measurement was evaluated via blind reference based statistical measures (BR-SM), blind reference based DL-descriptors (BR-DL) and visual interpretation of the resulting images. The BR-SM performed through calculating the EME (Measure of Enhancement), EEME (Measure of Enhancement by Entropy), SDME (Second Derivative like Measure of Enhancement), SHP (Coefficient of Sharpness) and CPP (Contrast per Pixel). In addition, by using DL scatter, the BR-DL handles the specific relationship with regards to the local contrast to local brightness within the resulting face images. Four face image databases, namely Extended Yale B, Mobio, Feret and CMU-PIE were used. The final results attained prove that compared to the state-of-the-art methods, the proposed ACS filter implementation is the most excellent choice in terms of contrast and nonuniform illumination adjustment as well as providing images of satisfactory quality. In short, the benefits attained proves that ACS is driven with a profitable enhancement rate in providing tremendous detail concerning face recognition systems.

Faces in an image consists of complex structures in object detection. The components of a face, which includes the eyes, nose and mouth of a person differs from that of ordinary objects, thus making face detecting a complex process. Some of the challenges encounter posed in face detection of unconstrained images includes background variation, pose variation, facial expression, occlusion and noise. Current research of Viola-Jones (V-J) face detection is limited to only 45 degrees in-plane rotation. This paper proposes only one technique for the V-J detection face in unconstrained images, which V-J face detection with invariant rotation. The technique begins by rotating the given image file with each step 30 degrees until 360 degrees. Each step of adding 30 degrees from origin, V-J face detection is applied, which covers more angles of a rotated face in unconstrained images. Robust detection in rotation invariant used in the above techniques will aid in the detecting of rotated faces in images. The images that have been utilized for testing and evaluation in this paper are from CMU dataset with 12 rotations on each image. Therefore, there are 12 test patterns generated. These images have been measured through the correct detection rate, true positive and false positive. This paper shows that the proposed V-J face detection technique in unconstrained images have the ability to detect rotated faces with high accuracy in correct detection rate. To summarize, V-J face detection in unconstrained images with proposed variation of rotation is the method utilized in this paper. This proposed enhancement improves the current V-J face detection method and further increase the accuracy of face detection in unconstrained images.

in recent years, WiMAX technology has been wildly used to provide broadband connections to end users. Many modern applications, such as Video conferencing, can be run over this network. Running applications over the WiMAX platform may decrease its performance. This paper evaluates the influence of Video conference traffic on WiMAX. OPNET is used to carry out the experiment part. Three different types of Video conferencing traffic were simulated. WiMAX delay, load and throughput were measured. The outcome of this research shows that the lowest performance of WiMAX was in the case of Virtual Conference Room traffic, and the best WiMAX performance was under low video traffic.

This project presents designing of an online e-learning networking course for adult learners with e-learning environments. The course consist of five categories- introduction to networking, what is internet protocol(IP), types of networks, types of main devices of networks, simulation(how to connect two networks with each other by router). The project is based on constructive learning theory, drill and practice, problem solving and tutorial were choosing as the instructional software in the in online course. The online networking course for adult learners was designed with adobe captivate 8 software which is suitable for the online networking course for adult learners because of its creative dynamics and making projects with software packages with ease.

IEEE 802.11n amendment is currently the most effective solution within the range of Wireless Local Area Networks (LAN). The standard provides many enhancements and is improved upon in many ways. One of the main goals of these improvements is to get high performance of Throughput and less delay of the MAC layer. The main objective of this research is to evaluate the performance of very high-speed WLANs (IEEE 802.11n) in terms of the aggregation method. IEEE 802.11n is intended to support frame aggregation which combines the packets into a large frame called (A-MSDU) aggregation MAC services data unit and aggregates the frames into a large frame called (A-MPDU) Aggregation MAC protocol data unit, which are the two key performance features are the ability to enhance the transmission overheads in MAC layer over a noisy channel. The system is examined by simulation using NS-2.34. The simulation results show that the proposed schemes significantly improve the performance over distributed coordination function (DCF) and the A-MSDU method significantly improves the performance of throughput over the literature scheme up to 103.16% while the A-MPDU method improves the performance of throughput over the literature scheme up to 155.15%. In conclusion, this research has achieved its stated objective of evaluating the performance of very high-speed WLANs (IEEE 802.11n) in terms of the aggregation method. Additionally, the proposed schemes show a significant improvement compared with a literature scheme.

The protection of pharmaceutical products from temperature-related damage and contamination is a crucial challenge faced by the healthcare industry. Variations in storage conditions can have a significant impact on the efficacy and shelf life of medications, and identifying such fluctuations is of utmost importance. In this regard, the present study aims to develop a cost-effective and efficient temperature monitoring system for medicines using the Embedded systems platform. The DS18B20 sensor was utilized to record the temperature profile signal, and a warning system was designed to alert the warehouse administrator in the event of any temperature fluctuations. The results of the study revealed a stable temperature profile within the medicine warehouse, and the warning system successfully notified the administrator whenever the temperature exceeded the user-defined threshold. These findings hold significant implications for future research exploring the potential of additional sensors for temperature monitoring purposes. Overall, the study highlights the importance of temperature monitoring systems in ensuring the integrity and efficacy of medications. The proposed system holds considerable promise for implementation in healthcare settings, where a maintaining optimal storage condition is critical for preserving the quality and safety of pharmaceutical products.

This applied research investigates the use of data mining techniques to analyze and track student behavior in asynchronous e-learning systems, with the goal of improving educational quality through data-driven insights. Conducted by Salem Msaoud Adrugia, Mustafa Almahdi Algaeta, and Tareg Abdusalam Elawajb, the study employs an empirical quantitative approach, extracting user logs from a university learning platform over one semester and applying algorithms such as K-Means Clustering, Decision Trees, and Association Rule Mining. The analysis reveals distinct behavioral patterns among learners, identifying three main clusters: \"active\" students who regularly engage and meet deadlines, \"delayed\" learners with sporadic access and reduced achievement, and \"passive browsers\" who read content without interaction. The study finds a strong positive correlation between consistent engagement and academic performance, and highlights behavioral metrics-such as message count, login frequency, and session duration-as potential early indicators of academic risk. The authors propose integrating data mining into academic decision-support systems to enable educators to monitor student participation and implement personalized interventions. They conclude that applying data analytics to e-learning environments represents a key step toward AI-driven education, where learning platforms evolve into intelligent systems capable of understanding and responding adaptively to individual learner behavior, thus fostering a more efficient and personalized educational experience. Abstract Written by Dar AlMandumh, 2025, Using AI