This research paper introduces and discusses deeply an approach to the real-time IP reputation (IPR) concept and its validation process for an Amazon Web Services Web Application Firewall (AWS WAF) backend application safeguarding using intelligence (AI) technologies. Also, the study examines existing IP reputation solutions over AWS WAF which Evaluates methodologies highlighting the difficulties faced and real-world challenges in validating IPR while utilizing OpenAI’s generative AI language models the framework aims to automate the extraction and interpretation of IP-related information from AWS S3 real-time log storage sources such as logs, and natural language reports based on JSON structure. These dedicated algorithms developed, and AI model concepts are powered by processing language enabling them to identify incidents and detect patterns of IP behavior that should indicate security risks. Also, models do not directly access databases, as they can analyze data from APIs featured and with local maintenance database such that AbuseIPDB to evaluate the reputation of IP addresses Integrating AI into the process of validating IPs can greatly improve cybersecurity operations by summarizing findings and providing insights ultimately saving time and resources.

Today, large volumes of complex data are collected in many application domains such as health, finance and business. However, using traditional data visualization techniques, it is challenging to visualize abstract information to gain valuable insights into complex multidimensional datasets. One major challenge is the higher cognitive load in interpreting information. In this context, 3D metaphor-based information visualization has become a key research area in helping to gain useful insight into abstract data. Therefore, it has become critical to investigate the evolution of 3D metaphors with HCI techniques to minimize the cognitive load on the human brain. However, there are only a few recent reviews can be found for 3D metaphor-based data visualization. Therefore, this paper provides a comprehensive review of multidimensional data visualization by investigating the evolution of 3D metaphoric data visualization and interaction techniques to minimize the cognitive load on the human brain. Complying with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines this paper performs a systematic review of 3D metaphor-based data visualizations. This paper contributes to advancing the present state of knowledge in 3D metaphoric data visualization by critically analyzing the evolution of interactive 3D metaphors for information visualization. Further, this review identifies six main 3D metaphor categories and ten cognitive load minimizing techniques used in modern data visualization. In addition, this paper contributes three taxonomies by synthesizing the literature with a critical review of the strengths and weaknesses of metaphors. Finally, the paper discusses potential exploration paths for future research improvements.

This paper presents a novel IoT system to eliminate the need for human intervention for solar panel maintenance purposes in smart farms. For the convenience of the consumer, a wireless sensing system could be implemented to automate these functions. This would eliminate the cost of any additional labor charges for panel maintenance as the system implemented would automatically calculate the position as per the current time of the day and adjust the panel's position accordingly to harvest the most amount of sun rays into the PV panel. Unlike the conventional tracking method where the panel is rotated hourly, we propose a fixed set of Sun Altitude and Azimuth angle ranges that are hardcoded to each panel position so that throughout the year whenever these angles fall out of range it jumps to the next position. The system results in a straightforward method by retrieving the current date/time from the RTC module and calculating the respective Sun Altitude and Azimuth angle to determine the position to adjust the position of the panel accordingly, thus producing effective power outputs and strong sun tracking results.

In the recent decade, there has been a lot of focus on developing intelligent systems and appliances to suit the century's needs and make life easier. During the same period, the electric power industry introduced Smart-Grid, a crucial innovation to meet today's electric supply-demand and effectively use electric resources. The smart grid is an aspect of the electricity industry's evolution and reformation. An electrical power grid is a complex system consisting of generation, transmission, distribution, storage, and utilization. Coordinating these systems further increases the complexity of this interconnection of systems. The existing power distribution system available in the industry consists of monitoring equipment such as Supervisory Control and Data Acquisition(SCADA) to monitor some network parts. However, there's no automated way of monitoring power outages or load current flow in some sub-sections of the distribution line. Physical inspection is not convenient as it's more time-consuming.Moreover, these sub-sections may have up to ten distribution transformers or even could be more. In this work, A novel IoT-based power line monitoring system has been introduced to overcome those issues. Narrow Band Internet of Things(NB-IoT)  is used in this system as the primary wireless technology. A current sensor measures electrical line currents, and sensor values are pushed to a remote IoT cloud. Implemented system tested in several 33kV power lines and result and performances of the system is presented. 

In this article, we propose a novel concept of using an existing cellular network to find the location of animals living in outdoor environments. The proposed method has simplified hardware architecture which can be implemented at a meager cost. Moreover, the sensors communicate with existing cellular networks, which will reduce the implementation cost.  The proposed system consists of a SIM 900 GSM module, a BMP280 pleasure sensor, and a battery as a wearable device that can warn the animal. The wearable device will send the real-time pressure values of the animal. In the proposed model, the pressure value, radial distance of transmitter fixed at the animal, and direction of the electromagnetic waves are used to calculate the real-time coordinates of the animals. The received pressure value and the radial distance will be used to calculate the location using this proposed model. The proposed model parameters' error was analyzed and simulated using suitable probability density distributions, and results were presented. 

This paper presents a design and development of an Artificial Intelligence (AI) based mobile application to detect the type of skin disease. Skin diseases are a serious hazard to everyone throughout the world. However, it is difficult to make accurate skin diseases diagnosis. In this work, Deep learning algorithms Convolution Neural Networks (CNN) is proposed to classify skin diseases on the HAM10000 dataset. An extensive review of research articles on object identification methods and a comparison of their relative qualities were given to find a method that would work well for detecting skin diseases. The CNN-based technique was recognized as the best method for identifying skin diseases. A mobile application, on the other hand, is built for quick and accurate action. By looking at an image of the afflicted area at the beginning of a skin illness, it assists patients and dermatologists in determining the kind of disease present. Its resilience in detecting the impacted region considerably faster with nearly 2x fewer computations than the standard MobileNet model results in low computing efforts. This study revealed that MobileNet with transfer learning yielding an accuracy of about 85% is the most suitable model for automatic skin disease identification. According to these findings, the suggested approach can assist general practitioners in quickly and accurately diagnosing skin diseases using the smart phone.

An electromyography (EMG) is an analytical tool used to record muscles' electrical activity, which produces an electrical signal proportional to the level of muscle activity. EMG signal plays a vital role in bio-mechatronic engineering for designing intelligent prostheses and other rehabilitation devices. Analysis of EMG signals with powerful and advanced methodologies is an essential requirement in EMG signal processing, as the EMG signal is a complex nonlinear, non-stationary signal in nature. It is required to use advanced signal processing techniques rather than conventional methods to exact EMG signals' features. Fourier transforms (FT) are not the most appropriate tool for analyzing non-stationary signals such as EMG. In this work, we have developed a system that can be useful for disabled persons to get a regular lifestyle using a functioning part of the body. Here, we studied the electrocution gram behavior of human body parts to feature extraction and trained the neural network to simulate the movements of mechanical actuators such as robotic arms. The wavelet transformation has been used to get high-quality feature extraction from electro cardio grapy and develops proper faltering methods for cardio systems' electrical signals. Finally, an artificial neural network (ANN) is used to classify the EMG signals through exacted features. Classification results are presented in this paper.

In the recent development of the Internet of Things (IoT), Artificial Intelligence (AI) plays a significant role in enabling cognitive IoT applications. Among popular IoT applications, location-based services are considered one of the primary applications where the real-time location of a moving object is estimated. In recent works, AI-based techniques have been investigated to the indoor localization problem, showing significant advantages over deterministic and probabilistic algorithms used for indoor localization. This paper presents a feasibility study of using Bluetooth Low Energy (BLE) and Feed Forward Neural Networks (FFNN) for indoor localization applications. The signal strength values received from thirteen different BLE ibeacon nodes placed in an indoor environment were trained using a Feed-Forward Neural Network (FFNN). The FFNN was tested under other hyper-parameter conditions. The prediction model provides reasonably good accuracy in classifying the correct zone of 86% when batch size is 100 under the learning rate of 0.01.Hence the FFNN could be used to implement on location-based IoT applications.

This paper presents a design and development of an IoT-based system to real-time track elders' physical activities using accelerometer sensor data. The objective behind conducting such research is to overcome the lack of ability to monitor physical activities. Especially with the development of the socio-economic sector, the number of elders who live in isolated areas such as elderly homes have increased rapidly. In such a case with declining cognitive abilities, the healthcare of these elderly personalities becomes vulnerable. This research project fulfilled the necessity of a system to capture the vital details about those people. The Internet of Things (IoT) and cloud-based applications have become a significant part of the Information and Technology sector. Realtime monitoring is a concept tightly coupled with IoT cloud cloud-native application for this application is an excellent example of that.Further, the requirement of a low-cost system was fulfilled by using hardware components such as NodeMCU and accelerometer sensors. The designed and developed system is composed of a cost-effective wrist-worn device capable of capturing hand movement on three different arises. Hence, the detected signals are transmitted to a master node to process and recognize the activity according to the detected signal. Another significant aspect of the project is using machine learning techniques to recognize the four different activities such as walking, sitting, sleeping, and standing. The use of supervised machine learning techniques is evaluated to overcome the barriers of real-time activity recognition. Further different supervised machine learning algorithms were used and evaluated, which were extracted from existing literature. The project was conducted while accomplishing the machine learning life cycle stages, and it has significantly benefitted from generating highly accurate final results for the overall system. Further different supervised machine learning algorithms were used and evaluated, which were extracted from existing literature. The supervised machine learning algorithm Decision Tree Classifier used for this study. Using the Decision Classifier Tree algorithm succeeded in gaining more than 80% of model accuracy. Since the research topic comes under a classification type-oriented problem, the testing process of the model has been done using the confusion matrix for the trained model.

Learning-based localization plays a significant role in wireless indoor localization problems over deterministic or probabilistic-based methods. Recent works on machine learning-based indoor localization show the high accuracy of predicting over traditional localization methods existing. This paper presents a Received Signal Strength (RSS) based improved localization method called TreeLoc(Tree-Based Localization). This novel method is based on ensemble learning trees. Popular Decision Tree Regressor (DTR), Random Forest Regression (RFR), and Extra Tree Regressor have been investigated to develop the novel TreeLoc method. Out of the tested algorithm, the TreeLoc algorithm showed better performances in position estimation for indoor environments with RMSE 8.79 for the x coordinate and 8.83 for the y coordinate.