Recently, underwater wireless sensor networks (UWSNs) have been emphasized due to their immense value in monitoring the underwater environment and expanding applications for target recognition and underwater information gathering. Battery power is restricted underwater, and it is also difficult to replace, which limits the power supply. As a result, studies and research seek to extend the life of the network. The proposed Threshold Sensitive Energy Efficiency Sensor Network (TEEN) protocol, along with particle swarming optimization (PSO) and BAT algorithms disclosed in this paper, attempts to improve network lifetime and power consumption via optimal node distribution and cluster header selection. The K-mean technique is used in each algorithm that separates nodes into clusters and selects for each cluster a point to be the central point from which to choose the best node to be the block head (CH). This selection is based on the node with the most energy as well as the node closest to the center point. After this stage, the proposed algorithms continue with Particle Swarm Optimization (PSO), and BAT Apply Cluster Head Update (CH), until the best map is produced. The results revealed that the proposed protocol resulted in a significant reduction in power consumption and network lifetime compared to the original protocol. The results also show that TEEN enhanced with BAT is better than TEEN enhanced with PSO.

Prostate cancer is a prevalent form of malignancy impacting a substantial male population and ranks among the primary contributors to cancer-related fatalities globally. The utilization of magnetic resonance imaging (MRI) scans for prostate cancer detection has presented significant difficulties. This proposed, explores the use of machine learning algorithms for prostate cancer detection using MRI scans and addresses the challenge mentioned above. The proposed ensemble models employ a combination of Machine Learning (ML) Algorithms, including Support Vector Machine (SVM), AdaBoost, Decision Tree (DT), and Random Forest (RF) to improve accuracy detection. The results of our rigorous evaluation process revealed that the ensemble model achieved an outstanding accuracy rate of 96% in classifying prostate cancer into Significant and Non-Significant. By comparing our results to existing studies, we have demonstrated that the ensemble model-based method is on par with or even surpasses various techniques used in previous research efforts.

This paper presents a voice-controlled and low-cost design of a practical smart house system (SHS). The proposed system uses to remotely control all digital devices using voice commands, and gives safety by identifying fire and recognizes suspicious movement. It is based on group of sensors, Arduino board, GSM and Bluetooth as a wireless technology to connect system components. Some appliances and sensors are directly associated with the Arduino. These appliances can be effectively controlled by user-friendly mobile interface. The microcontroller can additionally send signals if it identifies any unusual movement. To show the reliability and viability of this framework, devices such as LED lights, temperature, PIR Motion and ultrasonic distance detection sensors are incorporated with the proposed system. The proposed system is shown to be an easily configurable system, sensible, secure, cost effective and required less power comparing with other reviewed systems. Therefore, it is an appropriate and great candidate for the SHS.

Skin cancer is considered one of the most fatal illnesses in the human population. Within the current healthcare system, the procedure of identifying skin cancer is time-consuming and poses a potential risk to human life if not recognized promptly. Early identification of skin cancer is imperative to maximize the likelihood of achieving complete recovery. This research proposed using a hybrid model including DenseNet201 and auto-encoder for feature extraction, and a support vector machine (SVM) as the classifier. The proposed model was evaluated in the ISIC 2016 dataset, which consists of nine different classes. The hybrid model achieved a classification accuracy of 91.09% percent in accurately identifying nine different forms of skin cancer. The findings indicate that the proposed model is competitive compared to the baseline models. This outcome offers substantial assistance to dermatologists and health professionals in the field of skin cancer detection.

Wireless Sensor Networks (WSNs) consist of small sensor devices whose main purpose is to detect phenomena in the target area. WSNs are now being used in a variety of essential applications. Data aggregation is an effective strategy in WSNs for best network performance. Because sensor networks have a high node density, similar data is sensed by numerous nodes, resulting in data redundancy which negatively affects the overall performance of the network. The data aggregation may be used to overcome this problem during packet routing from source nodes to the base station. From the recent literature, researchers are still having trouble finding an efficient and appropriate data aggregation method for WSNs. This paper provides insight and support for researchers by knowing how aggregation technology affects the performance of WSNs in the term of energy efficiency, accuracy, and latency after the applying data aggregation strategy. Also, two algorithms namely SOM and HAC were implemented using the Intel Berkeley Research Lab Dataset to show the impact of data aggregation on the overall performance of the WSNs through the results obtained.

Two adaptive receivers for jointly detecting active users in an interleave division multiple access (IDMA) system are considered for highly dispersive underwater acoustic channels (UACs) using a continuous pilot approach. A direct adaptive interference cancellation (IC) IDMA receiver is proposed and compared with the standard Rake-IDMA receiver that performs adaptive semi-blind channel estimation developed by the authors. Both iterative decoding receivers incorporate a phase locked loop (PLL) and are optimized based on the minimum mean square error (MMSE) criterion. The theoretical basis of both receivers is presented along with experimental results obtained by processing data from actual underwater communication experiments. The transmission results of 3 active users at a data rate of 441.3 b/s per user within 4 kHz bandwidth demonstrate that the IC-IDMA receiver has better performance and significantly mitigates the bit errors associated with Rake-based IDMA receiver.