A Multilayer Approach for Intrusion Detection with Lightweight Multilayer Perceptron and LSTM Deep Learning Models

Intrusion detection is essential in the field of cybersecurity for protecting networks and computer systems from nefarious activity. We suggest a novel multilayer strategy that combines the strength of the Lightweight Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) deep learning models in order to improve the precision and effectiveness of intrusion detection.The initial layer for extraction of features and representation is the Lightweight MLP. Its streamlined architecture allows for quick network data processing while still maintaining competitive performance. The LSTM deep learning model, which is excellent at identifying temporal correlations and patterns in sequential data, receives the extracted features after that.Our multilayer technique successfully manages the highly dimensional and dynamic nature of data from networks by merging these two models. We undertake extensive tests on benchmark datasets, and the outcomes show that our strategy performs better than conventional single-model intrusion detection techniques.The suggested multilayer method also demonstrates outstanding efficiency, which makes it particularly ideal for real-time intrusion detection in expansive network environments. Our multilayer approach offers a strong and dependable solution for identifying and reducing intrusions, strengthening the security position of computer systems and networks as cyber threats continue to advance

Novel Load Balancing Optimization Algorithm to Improve Quality-of-Service in Cloud Environment

Scheduling cloud resources calls for allocating cloud assets to cloud tasks. It is possible to improve scheduling outcomes by treating Quality of Service (QoS) factors as essential constraints. However, efficient scheduling calls for improved optimization of QoS parameters, and only a few resource scheduling algorithms in the available literature do so. The primary objective of this paper is to provide an effective method for deploying workloads to cloud infrastructure. To ensure that workloads are executed efficiently on available resources, a resource scheduling method based on particle swarm optimization was developed. The proposed method's performance has been measured in the cloud. The experimental results prove the efficiency of the proposed approach in reducing the aforementioned QoS parameters. Several metrics of algorithm performance are used to gauge how well the algorithm performs

A Standalone RFID and NFC Based Healthcare

NFC is a standards-based, short-range wireless connectivity technology that enables simple and safe two-way interactions between electronic devices. The concept is based on combination of smartcard and contactless interconnection technologies; NFC is compatible with todays field proven RFIDtechnology. If this technology is used for identification of patient in hospital, it will be very useful. Patients have different illness. When doctors operate patients or exanimate patients, if the doctor confuses the disease of patient, a fatal medical circumstance may occur. If RFID and NFC technology is used for this case, we can easily protect patients from fatal medical mistakes. It has the potential to make almost all wireless technologies and also the applications on these technologies easy enough so that everyone, even the non-technical can use them. Technological development and modern medicine practices are amongst the outstanding factors triggering this shift. This trend is resulting in a greater demand for health care-related services and greater competition among health care providers. Achieving a high operational efficiency in the health care sector is an essential goal for organizational performance evaluation. Efficiency uses to be considered as the primary indicator of hospital performance. In order to bring down the cost and improve efficiency, intelligent systems can play a significant role in providing intelligently processed and personalized information about patients to doctors, their health care staff (i.e. nurses) and health care administrator

Masked Face Detection using the Viola Jones Algorithm: A Progressive Approach for less Time Consumption

The use of CCTV surveillance is today’s need inpublic and private sector for ensuring security against terrorismand robbery. Regular expressions are used to signify enormoussets of motion attributes captured in video. The video vigilanceis popular system without using human interference to captureimportant scenes. The motive of the work is to introduce automaticrevelation of masked objects in real time with a surveillancecamera. The main aim is to detect masked person automaticallyin less time period. In this paper,the researcher proposes a systemthat consists methods which uses four variant steps that are thesteps of calculating distance range of person from the camera,eye or vision line detection and face part detection such asmouth detection and face detection. Performance of proposedalgorithm is carried out on various real time inputs. Experimentalevaluation shows that proposed algorithm exceeds better in termsof time consumption. This unique approach for the problemhas created a method transparent and easier in complexity sothat the real time implementation can be made beneficial andworkable. Analysis of the algorithms fulfillment on the test videotrack gives appropriate judgments for additional improvementsin the masked face detection performance. Finally, based on theresearch, the axioms were useful for the work which can beusually accessible from available algorithms.</p

Comperative assessment of taylor water cycle optimization (TWCO) -based deep residual network (DRN) for skin cancer detection using deep learning

This paper devises a hybrid optimization driven deep technique for automated detection of skin cancer. Here, the pre-trained deep learning model is utilized for the skin cancer detection. The pre-processing of skin images is performed that helped to reduce the ill impacts and several artifacts such as hair that may be contained in the dermoscopy images.&nbsp; In addition, the DeepJoint model is used to perform segmentation in order to attain improved outcomes. The data augmentation helped to make the image suitable for improved processing helps to quantify the images for effective classification. The skin cancer detection is done with Deep Residual Network (DRN), which is trained using newly devised technique, namely Taylor Water Cycle Optimization (TWCO) algorithm. The proposed TWCO-based DRN outperformed with highest testing accuracy of 92.3%, True positive rate (TPR) of 93.5% and True negative rate (TNR) of 90.5%