Card Number Research Articles

Production of cellulose nanocrystals from the waste banana (M. Oranta) tree rachis fiber as a reinforcement to fabricate useful bionanocomposite

It is crucial to produce CNCs from the waste biomass of secondary plants to reduce the extra pressure on primary plants which have other advantageous applications in many sectors. Whereas the useless banana (M. oranta) rachis after harvesting its edible part could be a very new and beneficial one. Meanwhile, several well-known methods could be conducted, namely water retting, scouring, alkali treatment, chlorite bleaching, and acid hydrolysis, to yield high-quality CNCs. The samples of all stages were characterized by several state-of-the-art techniques, namely FTIR-ATR, TGA, FESEM, XRD, UV-vis-NIR, DLS, and zeta potential analysis, for a better understanding of their structural properties/purity. However, obtained results recommended that the CNCs have shown extensive active edges, greater thermal improvement up to 700°C, high crystallinity around 81.07±0.15% with JCPDS-ICDD card number (00-056-1718), a honeycomb-like porous microstructure, and promising spherical shapes along with an average size around 50nm. Additionally, the newly produced CNCs were free from all impurities and coloring materials and revealed a higher negatively charged surface around -45mV. Therefore, due to these outstanding features, banana rachis CNCs with a high yield (around 82.05±0.06%) would be beneficially used as promising reinforcement to fabricate useful bionanocomposite for various applications to replace fossil-based hazardous synthetic materials.

Investigating the multiferroic properties within triphasic systems to comprehend the mechanisms of water splitting

In the present study, a triphasic-based system was prepared using constituents such as Lanthanum Calcium Manganite (LCM), Cobalt Ferrite (CF), and Barium Titanate (BT) in different proportions. The composition (1-x)LCM: x(0.7CF-0.3BT), where x = 0, 0.1, 0.2, 0.3, and 1, was employed to produce the triphasic system-based materials using a solid-state reaction technique. X-ray diffraction results confirmed the development of the triphasic phase in the composites through the JCPDS card number of the major peaks. The average crystallite size calculated from XRD data was found to vary with increasing 0.7CF-0.3BT concentration in LCM. FTIR spectroscopy indicates alterations in the main peaks with an increase in content of 0.7CF-0.3BT, suggesting the formation of triphasic composites. The upward trend in dielectric permittivity suggests the successful occurrence of Maxwell-Wagner polarization, indicating relaxor behavior at lower frequency values. Impedance spectroscopy reveals dielectric relaxation and the contribution of charge carriers in triphasic composites, along with the impact of grain and grain boundaries. Magnetization analysis at room temperature approves an increase in saturation magnetization in composites i.e., 0.7LCM-0.3(0.7CF-0.3BT) triphasic material exhibiting a maximum saturation magnetization of 13.04 emu/g. Magnetoelectric behaviour is observed in triphasic materials at different AC magnetic fields. The 0.9LCM-0.1(0.7CF-0.3BT) triphasic hydroelectric cell achieved a peak current of 1.29 mA and showed enhanced stability over time. Its I-V characteristics revealed a short-circuit current of 3.5 mA, an open-circuit voltage of 0.85 V, and an off-load power output of 2.97 mW. The triphasic system demonstrates improved structural, electrical, and hydroelectric cell (HEC) performance offering promising prospects for use in magnetoelectric and hydroelectric devices.

A Hybrid Framework for Improved Weighted Quantum Particle Swarm Optimization and Fast Mask Recurrent CNN to Enhance Phishing-URL Prediction Performance

Phishing websites are cybercrimes that aim to collect confidential data, including bank card numbers, bank accounts, and credentials. To detect phishing sites, specialists must extract the elements of the websites and utilize third-party resources. One of the drawbacks of these methods is that identifying phishing characteristics takes a lot of effort and knowledge. Second, the recognition of phishing websites is delayed when third-party services are used. A novel detecting system Improved Weighted Quantum Particle Swarm Optimisation (IWQPSO) and Fast Mask Recurrent Convolutional Neural Network (FMRCNN) proposed to strengthen and empower the technique of identifying phishing URLs. The proposed model does not require the retrieval of target website content or the use of any third-party services. Phishing attacks continue to pose significant cyber-security threats, particularly through deceptive URLs. This study proposes a novel approach to enhance phishing-URL prediction using a hybrid methodology. The method integrates an IWQPSO-FMRCNN. The IWQPSO algorithm is leveraged to optimize the weights and parameters of the FMRCNN model, enhancing its performance in distinguishing between legitimate and phishing URLs. By combining the strengths of evolutionary optimization and deep learning, the proposed approach aims to achieve higher accuracy 99.3%, precision 94.6%, F1-Score 96.7%, Recall 98.2% and AUC score 97.9% in detecting phishing URLs compared to existing methods. Experimental results demonstrate the effectiveness of the proposed hybrid method in improving phishing-URL prediction performance, providing a promising avenue for enhancing cyber-security measures against online threats.

Sosialisasi Peningkatan Pengetahuan Keamanan Dalam Jual Beli Online

The increasing number of internet users makes Indonesia a very potential market for e-commerce. The growth of the middle class and the literacy rate of online buyers and sellers is also growing. E-commerce itself is not new, but its rise marks a new milestone for this industry in the country. Although it is growing quite rapidly, there is still one major problem that is preventing e-commerce in Indonesia from taking off, namely the problem of consumer trust in e-commerce sites. Consumers still do not trust the sales system with a non-physical buying and selling process, do not trust to provide credit card numbers to e-commerce services and are still afraid of fraud. However, this crisis of trust can actually be solved. One of the many ways is to use a payment service that has guaranteed security. Not all e-commerce can provide their own payment system, now there are many services that provide a secure payment system and are especially easy to apply to online stores. E-commerce players can use this payment system easily, quickly and which can improve the performance process in their online stores. However, the selection of services that provide a payment system must also be considered carefully. A secure payment system provider will provide a sense of security to prospective buyers, meaning that consumer trust must be built by e-commerce service providers so that consumers can be comfortable and do not encounter problems in making purchases and paying for products in the online stores provided.Security is important, the use of credit cards is widely highlighted because there are still many unpleasant incidents or fraud that occur due to the use of credit cards. Education on this security needs to be carried out continuously, because it will be related to consumer behavior.

From Probabilistic to Fuzzy Matching Record Linkage: A promising Transition

ObjectiveProbabilistic Record Linkage (PRL) heavily relies on manual intervention for gray area resolutions. This makes PRL extremely time and resource intensive. No matter how scientifically sound and robust PRL method is, it didn’t meet close-to-real-time data availability requirements at ICES. ApproachDuring a thorough evaluation and comparison process of three software and two methods of Record Linkage, ICES initiated a semi-design of experiment to select an optimal record linkage approach. For this experiment, a large Ontario data on 12 million individuals with required linkage variables plus valid Ontario health card numbers was selected. While the availability of the health card number enabled assessment of the accuracy of different approaches, the analysts were blinded to the correct health card numbers during the process. If manual intervention was required, it was repeated by two analysts to allow capturing human error. ResultsPRL-based software needed the most personnel time to complete the process. Human errors were identified during the manual intervention due to subjective decisions by the analysts. Fuzzy Matching approach eliminated manual intervention but achieved comparable linkage rate to PRL while maintaining the same accuracy. The Fuzzy Matching software costs were higher; however, the data timeliness was significantly improved, and the clerical review costs and human error were eliminated. ConclusionsThe Modernization of Record Linkage (MORL) project was a successful demonstration of the advantages of Fuzzy Matching over PRL method. However, the implementation of new approach at the organization level was a challenging change management.

Understanding Lithium-Gold Electrochemical Reactions Using Operando X-Ray Diffraction

Gold electrodes are used in lithium-based battery research as catalysts and micro / quasi-reference electrodes despite their high cost and unclear reactivity with lithium. Here we present an investigation of the electrochemical reactions of gold-lithium (Au-Li) mixtures using variable-temperature electrochemistry and operando X-ray diffraction.Multiple solid solutions (alpha, beta, delta) and intermetallic phases (AuLi3, Au4Li15) are expected based on the equilibrium phase diagram.1 Previous electrochemical investigations conclusively identified only AuLi3;2,3 alpha and beta may be bypassed during lithium insertion, and the structure of delta is unclear.4 We are unaware of any previous reports of the electrochemical formation of Au4Li15. Metastable crystallographic solutions have been proposed.5 X-ray diffraction patterns collected during the galvanostatic lithiation of gold leaf (at 60 °C and a rate of ca. C/50) are provided in Figure 1. Reference diffraction patterns and the associated Powder Diffraction File card number are indicated, along with proposed phases.4,5 X-ray and electrochemical data align with earlier results suggesting that the equilibrium alpha and beta phases are seemingly bypassed during the first insertion. Delta is the first phase to form at a potential of 0.25 V vs. Li/Li+, followed by the formation of AuLi3 at 0.15 V vs. Li/Li+ at all conditions and Au4Li15 at 0.05 V vs. Li/Li+ in select conditions. Electrochemical data suggests AuLi3 may also have a small range of solubility. Peaks associated with AuLi3 and Au4Li15 align with existing literature but peaks associated with delta do not. Reactions are reversible to delta, followed by the formation of another phase near 0.3 V vs. Li/Li+ and alpha at potentials above 0.4 V vs. Li/Li+ during lithium removal. Significant hysteresis is present in cell potential and reaction pathways in the low-lithium content region. The second cycle (not shown) is reversible between alpha (not pure gold) and the terminal phase without a significant loss in capacity. Alternative crystallographic unit cell(s) for delta and other gold-lithium phases will be described.While the delta / AuLi3 reaction exhibited a consistent potential during lithium insertion and removal, the potential otherwise varied strongly with temperature, rate, and composition, implying that gold quasi-reference electrodes may not be suitable for lithium-ion battery research.

Scam Call Detection Using NLP and Naïve Bayes Classifier

Financial fraud, particularly credit card fraud, is a pressing concern in the realm of digital transactions. The number of phone scams is increasing daily as con artists use phone calls to target victims for nefarious ends. Individuals are falling for con artists' proposals, becoming victims and giving up their personal information, leaving them open to abuse. Effective detection techniques are becoming more and more necessary. In this study, we offer an efficient approach to scam call identification utilizing speech-to-text libraries and the machine learning technique Naïve Bayes classifier. Our technology, which translates voice to text, uses this text to evaluate conversations in real time. It looks for trends and suspicious phrases that point to attempted scams, including asking for credit card numbers, passwords, or other sensitive information. The user will be able to decide whether or not to trust and continue with the call by using the alert prompt that appears as a pop-up message if the words are found to be suspicious. The user will take certain measures, such as ending the conversation right away, blocking the number, and reporting it further, if they don't trust the call. Our strategy is to successfully handle scam calls through ongoing adaptation and learning, boosting user security and confidence in phone conversations. The user will be able to decide whether or not to trust and continue with the call by using the alert prompt that appears as a pop-up message if the words are found to be suspicious. The user will take certain measures, such as ending the conversation right away, blocking the number, and reporting it further, if they don't trust the call. Our strategy is to successfully handle scam calls through ongoing adaptation and learning, boosting user security and confidence in phone conversations. Keyword: Spam Detection, Naïve Bayes, Natural Language Processing, Machine Learning.

Research on privacy leakage of celebrity's ID card number based on real‐name authentication

AbstractThe Internet real‐name system is widely implemented among Chinese Internet users, and many commonly used apps in China exist the functions of real‐name authentication. However, our study found that many apps do not have effective restrictions on user's operations of real‐name authentication, resulting in users being able to frequently perform unsuccessful real‐name authentication attempts. This vulnerability can help an attacker crack celebrity's ID card number by enumeration attacks, and a feasible cracking method was proposed in this paper. First, the information of birth date, birth place, and life experiences of a celebrity is collected from the platforms that display celebrities' personal information (e.g., Wikipedia, Baidu Baike, etc.). In this process, an information extraction method is used to infer permanent residences from life experiences. Then, the possible ID card numbers of a celebrity can be constructed by using the information of birth date, birth place, and permanent residences. Finally, these possible ID card numbers will be verified by sending requests to platforms that have vulnerabilities in the function of user real‐name authentication, until the real ID card number of a celebrity being cracked. This paper conducted cracking experiments on two groups of celebrities. The first group of celebrities is collected from the news events of privacy leakage that were publicly available online, and the second group of celebrities is randomly selected from two encyclopedia platforms. The experimental results showed that the success rate of cracking the ID card numbers of celebrities is 53.9%, which verified the effectiveness of the proposed cracking method. Besides, this paper proposed some security precaution suggestions to solve this security problem, and the implementation, feasibility, potential impact, expected effectiveness of these measures were also analyzed. To our knowledge, our paper is the first to point out the issue of privacy leakage of celebrity's ID card number caused by apps' real‐name authentication functions in China. We believe that our research will attract widespread attention from society regarding celebrity's privacy information protection.

Characterization of biologically synthesized copper nanoparticles by Aspergillus carbonarius and evaluation of their activity in reducing the pathogenic stress of tomatoes infected with Fusarium oxysporum

This study was conducted to evaluate the efficiency of the fungus Aspergillus carbonarius in the synthesis of copper nanoparticles (CuNPs) from copper chloride solution, with the characterization of these nanoparticles and the demonstration of their effectiveness in inhibiting the pathogenic fungus Fusarium oxysporum causing tomato root rot disease. The results indicated that the treatment of copper chloride solution at 150 mM with A. carbonarius filtrate led to the production of CuNPs which characterized by a color change from blue to green, with the highest absorbance observed at 300 nm. X-ray diffraction (XRD) results revealed the crystalline structure with peaks at angles 28.47286, 47.43258, and 56.20644 ◦ corresponding to (111), (220), and (311) planes, respectively, matching the JCPDS card number 01-081-1841. Scanning electron microscopy images showed cubic, pyramidal, and prismatic shapes with dimensions ranging from 15 - 84.291 nm. In laboratory experiment, CuNPs inhibited the growth of the pathogenic fungus, with an inhibition zone of 1.6 cm, compared to copper chloride which recorded 0.9 cm. In the field, , the treatment of CuNPs led to an increase in the stress resistance indicators, reaching the highest values for CuNPs when combined with the fungicide Medazim in the American tomato cultivar. The concentrations of total phenols and glutathione in this treatment were 2.56 mg/g and 0.322 µg/g, respectively. The lowest concentration of proline was recorded as 4.31 µg/g in the treatment involving CuNPs against the fungal infection. CuNPs with the fungicide medazim, significantly outperformed other treatments, registering lowest disease severity at 12.22% in the American cultivar, compared to the highest disease severity was recorded in the treatment with the pathogenic fungus alone, reaching 91.16% in the Dutch cultivar. CuNPs significantly increased tomato fruit weight, both in healthy and infected plants. The American, Turkish, and Dutch tomato cultivars infected with the pathogenic fungus and treated with under + medazim showed higher productivity, reaching 6432.55, 6334.34, and 6305.73 g.plant-1, compared to the lowest productivity in the treatment of these cultivars with the pathogenic fungus, reaching 1008.34, 977.45, and 833.78 g.plant-1, respectively

Counseling and assistance in verification and validation of housing data for surveyors

The data collection of housing database in Jembatan Besi Village conducted by surveyors from the Faculty of Civil Engineering and Planning, Trisakti University often encounters data errors and obstacles during its implementation. These errors can be in the form of incorrect writing of addresses, family card numbers, head of family NIKs, or geotagging (location points) of buildings. Therefore, in the middle of the data collection, a workshop was held to analyze and evaluate the data that had been entered and to correct data errors. The method used is through data checking by field coordinators and repairs are carried out simultaneously. Workshop activities include presentation of the progress of the 50% survey results, data validation and verification, and data correction. During the workshop, the survey results were obtained as much as 60.55% with a total of 2,704 buildings and data correction facilities including geotagging, RT and RW data, Family Card Numbers, NIK, address writing, and deleting duplicate data.

Impact of iron oxide alginate berbamine nanocomposites on the pre-metastatic microenvironment of human bladder cancer cells through ROS-mediated mitochondrial depolarization

The main objective of the current study was to synthesize, characterize, and application of an anticancer agent based on iron oxide nanoparticles (NPs) synthesized in the presence of berbamine and alginate to modulate the pre-metastatic ecological niche of human bladder cancer cells by inducing ROS-mediated mitochondrial depolarization. The environmentally friendly synthesized nanoparticles were analyzed using SEM and TEM imaging, VSM, XRD, DLS, Zeta sizing, photoluminescence, EDX, and UV–Vis spectroscopy techniques. The findings indicated that the nanoparticles were smaller than 100 nm, displayed consistent spherical morphology, and were evenly monodispersed in size. We observed that the hydrodynamic size of the synthesized NPs was around 247.8 nm and the zeta potential was −32.1 mV. EDX analysis showed that the fabricated NPs composed of Iron (70.9 % of Mass) and O (29.1 % of Mass). The XRD analysis showed that the pattern of the NPs is matched with JCPDS card number 82–1533. The VSM analysis revealed that the NPs displayed superparamagnetic properties when exposed to a magnetic field. Results from the anticancer investigations demonstrated that the NPs caused a dose and time-dependent lethal impact on T24 cells, while showing no significant toxicity on SV-HUC-1 cells, highlighting their biocompatibility. The anticancer mechanism was evaluated using acridine orange/ethidium bromide, DCFH-DA, JC-1 staining, and wound-healing/scratch assay. The results demonstrated that the cells underwent apoptosis instead of necrosis, attributed to ROS-triggered mitochondrial depolarization. Additionally, the nanoparticles inhibited the cells' migratory capabilities. These results suggest that the environmentally friendly produced iron nanoparticles have promising potential as a strong anticancer treatment.

A Report on Hazards of Computer Viruses

Computer viruses are one of the major risks that an individual and a community face to the safe living of a household, enterprise, and government. These malicious software programs are developed to work like viruses: they reproduce themselves and spread, thus damaging the system, data loss and the loss of money. This research paper focuses on the various types of dangers that computer viruses pose, such as data corruption, theft of personal information, system disruption, and reputational damage. The hazards of computer viruses are the main thing that people and organizations need to be aware of in order to protect themselves and their systems from these hazards. Furthermore, computer viruses can also steal sensitive information, such as passwords, credit card numbers, and personal documents. This theft of personal information can result in financial loss and identity theft. Additionally, viruses can cause system damage, leading to system crashes or malfunctions. This can disrupt normal operations and lead to costly repairs or replacements.

Synthesis of BaO/NiO/rGO nanocomposite for supercapacitor application

Abstract Considering the global energy crisis, alternative energy resources requirement is rising gradually. In light of dwindling energy resources, we turn to renewable alternatives. Storing this energy for future utilization remains a pressing endeavor. The ideal storage device should possess intensified energy density, power density, and cyclic stability. In this study, we have synthesized metal oxide with carbon based material nanocomposite such as BaO/NiO, BaO/NiO/rGO through cost effective co-precipitation method and their comparative performance for supercapacitor application were studied. Various characterizations were taken for the above synthesized material. X-ray diffraction (XRD) study confirmed the material formation and their crystallinity of the nanocomposite. BaO has tetragonal structure which was confirmed through JCPDS card number 26-0178 and NiO has rhombohedral structure which was confirmed through JCPDS card number 89-7390. To study electrochemical behaviour of electrode material and its cyclic stability, cyclic voltammetry (CV), galvanostatic charge–discharge (GCD) and electrochemical impedance spectroscopy (EIS) studies was executed. BaO/NiO/rGO possesses 1072 F/g specific capacitance at 0.3 A/g in aqueous 1 M KOH. The electrochemical action of hybrid device was setup and it revealed 224 F/g at 0.3 A/g within the charging potential of 1.6 V. Capacitive retention of 97.6 % was achieved by asymmetric hybrid supercapacitor even after 5000 cycles at 10 A/g, this shows prepared nanocomposite exceptional cyclic stability in energy storage application.

Predictors of parental acceptance to live attenuated influenza vaccine for children

ABSTRACT To determine the influencing factors of Chinese parents' intention and behavior for children to receive live attenuated influenza vaccine during the 2022–2023 influenza season. A theoretical model was developed and included seven constructs, and structural equation modeling was used to test 11 hypotheses. From October 2022 to December 2023, a survey was conducted across 38 medical institutions in four Chinese cities and their subordinate districts, counties, and rural areas. Parents who accompanied their children for vaccinations were selected through a randomization process based on their child’s medical card numbers. Measures were taken to minimize method bias, including a diverse geographical representation and random sampling. The survey resulted in the collection of 936 valid responses, exceeding the recommended sample size for structural equation model analysis and providing robust statistical inferences. During the study period, 936 respondents were included in the study. Perceived ease of use was verified to be a predictor of perceived usefulness and perceived value. Perceived usefulness was verified as a predictor of perceived value and behavioral intention. Knowledge was a significant antecedent of perceived value and risk perception of influenza disease. Risk perception of influenza disease was proved to be a significant predictor of perceived value and self-reported vaccination behavior. Perceived value significantly affected behavioral intention, and behavioral intention significantly affected self-reported vaccination behavior. Six demographic variables significantly moderate the theoretical models. The low vaccination coverage of live attenuated influenza vaccine (LAIV) among children in China suggests a need for a deeper understanding of the factors that influence vaccination rates. Particularly, effective strategies are necessary from policymakers and practitioners to elevate childhood LAIV coverage.

Detecting vulnerabilities in website using multiscale approaches: based on case study

In the realm of modern web applications, security stands as an utmost priority. To address this critical concern, we've developed a versatile Python script with the primary goal of proactively identifying vulnerabilities and thwarting transient attacks. Leveraging various libraries, this tool comprehensively covers a broad spectrum of threats, including SQL injection (SQLi), cross-site scripting (XSS), cross-site request forgery (CSRF), sensitive data leakage, security misconfiguration, distributed denial-of-service (DDoS) vulnerabilities, and secure socket layer (SSL) or transport layer security (TLS). This Python-based solution prioritizes adaptability, ensuring seamless integration of future updates to effectively combat evolving threats. Utilizing innovative methods such as SQLi and XSS payload injection, the script assesses the susceptibility of input fields. And addressing CSRF vulnerabilities, the script generates and validates tokens, fortifying defenses against unauthorized actions. Employing pattern analysis, it combats sensitive data exposure and security misconfigurations, adeptly identifying elements like credit card numbers, passwords, and headers. Furthermore, the script enhances overall security by scrutinizing SSL/TLS protocols and monitoring port accessibility. It reinforces DDoS detection by actively monitoring traffic patterns, identifying anomalies, and proactively averting disruptions.

Real-time phishing detection using deep learning methods by extensions

Phishing is an attack method that relies on a user’s insufficient vigilance and understanding of the internet. For example, an attacker creates an online transaction website and tricks users into logging into the fake website to steal their personal information, such as credit card numbers, email addresses, phone numbers, and physical addresses. This paper proposes implementing an extension to prevent phishing for internet users. In particular, this study develops a smart warning feature for the proposed extension using deep learning models. The proposed extension installed in the web browser protects users by checking for, warning about, and preventing untrusted connections. This study evaluated and compared the performance of machine learning models using a malicious uniform resource locator (URL) dataset containing 651,191 data samples. The results of the investigation confirm that the proposed extension using a convolutional neural network (CNN) achieved a high accuracy of 98.4%.

Detection of Phishing Websites by Using URL Analysis

Abstract: In recent years, with the increasing use of mobile devices, there is a growing trend to move almost all real-world operations to the cyberworld. Although this makes easy our daily lives, it also brings many security breaches due to the anonymous structure of the Internet. Used antivirus programs and firewall systems can prevent most of the attacks. However, experienced attackers target on the weakness of the computer users by trying to phish them with bogus webpages. These pages imitate some popular banking, social media, e-commerce, etc. sites to steal some sensitive information such as, user-ids, passwords, bank account, credit card numbers, etc. Phishing detection is a challenging problem, and many different solutions are proposed in the market as a blacklist, rule-based detection, anomaly-based detection, etc. In the literature, it is seen that current works tend on the use of machine learning-based anomaly detection due to its dynamic structure, especially for catching the “zero-day” attacks. In this paper, we proposed a machine learning-based phishing detection system by using eight different algorithms to analyse the URLs, and three different datasets to compare the results with other works. The experimental results depict that the proposed models have an outstanding performance with a success rate.

Digital Health Services Through the Lens of Data Privacy

Pandemic has showcased to the world the need for having seamless health data exchange. One of the major reasons for India’s success in vaccinating her vast population is use of digital technology to ensure an easy mechanism for the people to register for vaccinations, get appointment dates as well as the jabs itself. This ease of use has made even the rural population appreciate the need for seamless data exchange to provide excellent health care. Keep this viewpoint in mind, the Indian government has chosen to usher in unique health id for each person based on their KYC / Aadhar card number. This is a welcome change albeit riddled with privacy concerns. In this article the author seeks to analyze the strength of digital health data framework upon the touchstone of privacy and informed consent. The author seeks to find out if the existing policies and laws are strong enough to ally privacy concerns or if there is an emergent need to ensure that unique digital health id does not result in data and privacy breaches across the digital health data framework. The author also seeks to find out if the data policies of the government are followed in practice at the ground level, wherein the people might be unaware of ‘informed consent.’

CASHLESS SOCIETY MANAGING PRIVACY AND SECURITY IN TECHNOLOGICAL AGE

A cashless society is an economic state which handles financial transactions not in the form of traditional mediums of currency, such as cash or coins, but by transferring digital data (usually by electronic means, such as credit cards and mobile data) between participating parties. Participants of a cashless society must Figure out a way to protect their transaction data, acknowledging the risks of organizations collecting mass amounts of said data, which result in a reduction of personal privacy. Balancing individual privacy with data security is vital in the information age, especially considering the increasing risk of data breaches and exploitation. In order to increase privacy in a cashless society, a few courses of action can be combined to produce a lasting and desirable result for users: A new kind of banking service that assigns randomized numbers to credit cards, the use of blockchain to monitor all transactions from individuals, and a campaign to educate and inform key stakeholders about security and privacy risks to provide the necessary tools and background knowledge to safeguard their own information before interaction with a foreign entity or other third parties i.e. cyber security departments, IT technicians, etc. Blockchain and card number randomization are both susceptible to zero-day errors, bugs, and varied levels of social acceptance. This preliminary research draws on a systems analysis of cashless systems to identify and analyze a set of social and technical solutions to support a robust cashless system that protects users’ privacy and maintains the security of the system. The information found and analyzed will be beneficial by exposing weak points in current methods of data integrity and security. Learning about current and future methods of managing privacy and data security in the technological age would be helpful in creating preventative counter measures. Key Words: Cashless economy, e-banking, card swipe, digital money, plastic money, transparency and digital transaction.

Analysis of the Quality of Licensing Management through the Online Single Submission Risk Based Approach (OSS-RBA) System at the Investment Office and One-Stop Integrated Services of Central Maluku Regency

Analysis of the Quality of Licensing Management through the Online Single Submission Risk Based Approach (OSS-RBA) System at the Investment Office and One-Stop Integrated Services of Central Maluku Regency