Satisfactory cybersecurity protection, encompassing all data security solutions, can only be achieved by adopting a cybersecurity framework that provides a structure and methodology for protecting critical digital assets. In addition, security experts recommend using cybersecurity standards which consist of a collection of best practices to protect organizations from cyber threats. However, many organizations, companies and governments lack experienced personnel in the cybersecurity domain, so they have difficulty adopting a standard approach or cybersecurity framework. Protecting organizations from cyber threats while demonstrating compliance with laws and standards is seen as extremely complex due to the difficulty on choosing the appropriate standard to be used. Moreover, lack of knowledge on the elements needed that offered by the standard is lead to the problem on identifying the started point where the protection will be began.  Therefore, in this paper, a literature and the analysis is presented in identifying the elements of cybersecurity standard and framework that can be facilitate the organization or government on choosing the appropriate standard and framework to be used and utilized. The literature review was carried out to understand the various types of cybersecurity standards and frameworks and the analysis is conducted to identify the elements in each of them. In this paper, eight steps are presented and include the types of international standards, which are general, local regulation, as well as specific standards used in the industrial sector, to conclude the findings of the analysis. Furthermore, a relation map is presented using Writing a Literature Review release 2.0 approach to show the relationship between the literature review and future research.

https://www.ijcnis.org/index.php/ijcnis/article/download/4817/426

Analysis of Cybersecurity Standard and Framework Components

Location of Seed Spoilage in Mango Fruit using X-ray Imaging and Convolutional Neural Networks

—Providing security to Health Information is consid- ered the topmost priority compared to any other ﬁeld. After digitalizing patients records in the medical ﬁeld, the health- care/medical ﬁeld has become a victim of several internal and external cyberattacks. Data breaches in the healthcare industry have been increasing rapidly. Despite having security standards such as HIPAA (Health Insurance Portability and Accountability Act), data breaches still happen on a daily basis. All various types of data breaches have a similar harmful impact on healthcare data, especially on patients’ privacy. This paper aims to understand the aspects that led to healthcare data breaches via ransomware incidence and their impact on the patients and healthcare providers. In addition, the paper reviews the current possible solutions to improve the healthcare security system by analyzing the efﬁciency of these solutions. We studied the most signiﬁcant healthcare data breaches via ransomware attacks that occurred in the U.S. from 2015 to 2020. We analyzed the obtained data from different academic and business sectors resources that target the reasons for the healthcare data breaches.

A Review on Data Breaches in Healthcare Security Systems

In aquaculture, the accurate recognition of fish underwater has outstanding academic value and economic benefits for scientifically guiding aquaculture production, which assists in the analysis of aquaculture programs and studies of fish behavior. However, the underwater environment is complex and affected by lighting, water quality, and the mutual obscuration of fish bodies. Therefore, underwater fish images are not very clear, which restricts the recognition accuracy of underwater targets. This paper proposes an improved YOLO-V7 model for the identification of Takifugu rubripes. Its specific implementation methods are as follows: (1) The feature extraction capability of the original network is improved by adding a sizeable convolutional kernel model into the backbone network. (2) Through ameliorating the original detection head, the information flow forms a cascade effect to effectively solve the multi-scale problems and inadequate information extraction of small targets. (3) Finally, this paper appropriately prunes the network to reduce the total computation of the model; meanwhile, it ensures the precision of the detection. The experimental results show that the detection accuracy of the improved YOLO-V7 model is better than that of the original. The average precision improved from 87.79% to 92.86% (when the intersection over union was 0.5), with an increase of 5.07%. Additionally, the amount of computation was reduced by approximately 35%. This shows that the detection precision of the proposed network model was higher than that for the original model, which can provide a reference for the intelligent aquaculture of fishes.

/pdf/an-accurate-detection-model-of-takifugu-rubripes-using-an-1g61cv10.pdf

An Accurate Detection Model of Takifugu rubripes Using an Improved YOLO-V7 Network

Abstract The global steel demand continues to increase, with steel being used in various industries, including construction, automobile, national defense, and machinery. However, steel production is a delicate process that can result in different defects on the steel surface, negatively affecting the quality of the steel products. Therefore, recognizing metal surface defects is critical in the metal production industry. Manual detection of these defects is the standard method, but it is time-consuming, labor-intensive, and prone to subjective factors, leading to low accuracy and unreliable results. Automated defect detection using computer vision methods can replace or supplement manual detection. In recent years, machine learning algorithms, particularly Convolutional Neural Networks (CNNs), have shown great promise in achieving high accuracy rates in this task. In addition, image classification algorithms can contribute to Lean metal production by identifying defects or anomalies in the manufacturing process, which can be used to reduce waste and increase efficiency. However, the performance and cost of different CNN architectures can vary widely, making it challenging for decision-makers to select the most suitable model. This paper analyzes various CNN-based image classification algorithms, including MobileNet, ShuffleNet, DenseNet, RegNet, and NasNet, in classifying steel surface defects in the NEU-CLS-64 dataset. We evaluate their performance using metrics such as accuracy, precision, sensitivity, specificity, F1 score, and G-mean, and benchmark these models against each other. Our findings revealed that RegNet achieved the highest accuracy, precision, sensitivity, specificity, F1 score, and G-mean performance but at a higher cost than other models. Meanwhile, MobileNet had the lowest performance. The results provide decision-makers with valuable insights into selecting the most suitable CNN model for steel surface defect detection based on their performance. 

/pdf/waste-reduction-via-computer-vision-based-inspection-towards-2j7cr2on.pdf

Waste Reduction via Computer Vision-based Inspection: Towards Lean Systems in Metal Production

Manufacturing, one of the most valuable industries in the world, is boundlessly automatable yet still quite stuck in traditionally manual and slow processes. Industry 4.0 is racing to define a new era of digital manufacturing through Internet of Things- (IoT-) connected machines and factory systems, fully comprehensive data gathering, and seamless implementation of data-driven decision-making and action taking. Both academia and industry understand the tremendous value in modernizing manufacturing and are pioneering bleeding-edge strides every day to optimize one of the largest industries in the world. IoT production, functional testing, and fault detection equipment are already being used in today’s maturing smart factory paradigm to superintend intelligent manufacturing equipment and perform automated defect detection in order to enhance production quality and efficiency. This paper presents a powerful and precise computer vision model for automated classification of defect product from standard product. Human operators and inspectors without digital aid must spend inordinate amounts of time poring over visual data, especially in high volume production environments. Our model works quickly and accurately in sparing defective product from entering doomed operations that would otherwise incur waste in the form of wasted worker-hours, tardy disposition, and field failure. We use a convolutional neural network (CNN) with the Visual Geometry Group with 16 layers (VGG16) architecture and train it on the Printed Circuit Board (PCB) dataset with 3175 RBG images. The resultant trained model, assisted by finely tuned optimizers and learning rates, classifies defective product with 97.01% validating accuracy.

Circuit Manufacturing Defect Detection Using VGG16 Convolutional Neural Networks

The digitizing medical record has seen a pattern shift in the healthcare industry which increases the data in terms of complexity, diversity, and timeliness. Healthcare industry is encounters crucial threat owing to security breaches of healthcare databases. The patients were provided to destitution due to scamming data and stolen information by medical identity theft like data breaches are not influenced patients who are PHI (Protected Health Information) and PII (Personally Identifiable Information) but healthcare institutions. Automated health care system introducing personal and electronic health records (PHRs and EHRs) increase more data risk which advises privacy and security policies measures. Biometrics need in healthcare industry is increasing in the worldwide healthcare market. This paper identifies the healthcare data breaches and their primary causes with the role of PHI and HIPAA Act for dissuading data breaches. HIPAA Privacy Rule provides policies and standards to preserve Protected Health Information (PHI) of individuals held by the entities. In biometric market HIPAA Act enforces various needs to preserve confidentiality and privacy of patient information attained from healthcare facilities to develop fulfillment for dealing various principles.

Healthcare Biometrics Security and Regulations: Biometrics Data Security and Regulations Governing PHI and HIPAA Act for Patient Privacy

In today's competitive world women contribute about 50% for the development of the nation but the most disheartening fact is that the safety of women is not assured in our society. Many research have been demonstrating previously the women safety techniques. However this paper proposes a novel method to safeguard women from the stalkers. The proposed model consists of components like Arduino microcontroller, sensors like piezoelectric for sensing the pressure, accelerometer sensor for getting the values for all the directions. In this model, Sim808 module which comprises of both the functionalities of GPS and GSM is used. GSM module is used for sending alert message to the known contacts and nearby Police station whenever emergency situation arises. In addition to the above feature, a push button system is also included, which helps in activating the sim 808 to send alert message and a Piezoelectric buzzer to alert the nearby public in case of emergency.

Women Safety System using IoT

In general, agriculture plays a very important role in contributing to human life on earth. Agriculture acts as the major source of providing food and economic growth of a region and as known plants are affected by several kinds of diseases either by excessive use of chemicals or by bacteria, viruses and fungus. It is important to diagnose plant diseases rightly, since use of wrong chemicals to treat the disease may increase the resistance of the pathogens which affects the plants. Manual diagnosis of diseases that affects the leaves of a plant will delay the process of diagnosis and treatment. Deep Learning frameworks can be used in detection and classification of the diseases. Convolution Neural Network based (CNN) based models are used in detection of apple leaf diseases. VGG16 framework is a CNN based architecture widely used in many deep learning classifications and it is easy to implement. VGG16 is used here for diagnosis and classifying apple leaf diseases. For implementing the framework tools and modules like Kaggle Notebook, Tensorflow, and Keras used. The VGG16 model is applied to the apple leaf disease dataset collected from the Kaggle repository. The proposed model aims in reducing complexity in classifying apple leaf disease using deep learning. The proposed system shows the best validation accuracy of 93.3% on the apple leaf disease dataset. This method outperforms some existing state-of-the-art. The processing time for each image is at an average of 14s. Hence the system proposed can be used by farmers to simplify the apple leaf disease classification process and help in early diagnosis and treatment of the disease.

Apple Leaf Disease Detection using Deep Learning

The main goal of the project is to reduce drunk and ride accidents, to safeguard by wearing helmet. The helmet has a fixed alcohol sensor to detect the presence of alcohol on the rider's breath. Alcohol consumption lowers inhibitions and may lead some to make dangerous decisions, such as driving under the influence of alcohol. Drink driving is very dangerous because both the safety of the driver and the safety of other passengers are at risk. Any amount of alcohol in the blood can impair the ability to drive. Alcohol resistance varies greatly from person to person, putting rider at a risk of causing an accident or injury on the road However, consumption of alcohol affects their consciousness while driving and putting them as well as others in danger. Even a small amount of alcohol consumption during driving time can affect driving ability at high rate. In order to reduce former risks caused by alcohol consumption, this project provides an effective and efficient way to reduce the accidents.

Sangeetha K

Papers

Circuit Manufacturing Defect Detection Using VGG16 Convolutional Neural Networks

Healthcare Biometrics Security and Regulations: Biometrics Data Security and Regulations Governing PHI and HIPAA Act for Patient Privacy

Women Safety System using IoT

Apple Leaf Disease Detection using Deep Learning

A novel approach to reduce drunk and drive accident using smart helmet (ALCOMET)