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Abstract: International Journal of Cooperative Information Systems, Volume 31, Issue 03n04, December 2022.
Citation: International Journal of Cooperative Information Systems PubDate: 2023-02-13T08:00:00Z DOI: 10.1142/S0218843022990015 Issue No:Vol. 31, No. 03n04 (2023)
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Authors:D. Vanusha, B. Amutha Abstract: International Journal of Cooperative Information Systems, Ahead of Print. More than 85% of people with long-term diabetes are affected by Diabetic Retinopathy (DR), and it is a foremost reason for blindness in the 20–64 age range for both young and old patients. Non-proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR) are two distinct kinds of DR. In the recent years, due to lack of precise diagnosis or timely prediction, 85% of people have lost their vision due to DR. Several techniques using diverse fundamental concepts were offered to diagnose this issue. According to the proposed method, the DR is categorized into five groups with a range of zero to four integers. Deep Convolutional Neural Network (DCNN) technique seems to work well enough, but describing the problem in a unique way to figure out how often the disease happens is still hard. One method proposed to automatically detect DR had 86.17% accuracy. This strategy utilized a CNN but lacked clinical training and validation data for the dataset. Even though CNNs have achieved remarkable results and are acclaimed for their generally high precise results in terms of image processing tasks, there are many hindrances and obstacles which affect the performance of CNN like the complex algorithm in terms of computation and processing time. To solve this problem of CNN, region proposals are identified, that can detect the region of interest based on the context or purpose. The next big problem is that there is no dataset of images of fundus that everyone or most people agree on. This makes it hard to use algorithms to analyze and get correct results. The Diabetic Retinopathy Image Database (DRiDB), for example, aims to get around this problem. So, our approach is to implement a Region Convolutional Neural Network (RCNN) for the detection of features. Usage of RCNN and commonly accepted database will ensure further accurate prediction of DR. Citation: International Journal of Cooperative Information Systems PubDate: 2023-05-23T07:00:00Z DOI: 10.1142/S0218843023500065
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Authors:Yue Wang Abstract: International Journal of Cooperative Information Systems, Ahead of Print. In order to improve data security and network data security in the digital economy-driven environment, this paper combines data security technology and network security technology to build a digital economy security management and control system. Moreover, this paper describes the data encryption of the data owners before the framework index and the composition and construction method of the corresponding EncIR tree, and analyzes the spatial keyword group query algorithm on the EncIR tree. In addition, this paper analyzes the experimental performance of index building and spatial query, and builds an intelligent digital economy security system on the basis of these algorithms. The experimental research results verify that the data security technology and network data system driven by the digital economy have good security performance, and on this basis, follow-up security regulations can be formulated. Citation: International Journal of Cooperative Information Systems PubDate: 2023-03-21T07:00:00Z DOI: 10.1142/S021884302150009X
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Authors:D. Vimala, K. Manikandan Abstract: International Journal of Cooperative Information Systems, Ahead of Print. The wireless sensor network (WSN) is a mobile adhoc network which has no support of infrastructure. The complexities in network management are critical to resolve even when choosing the cluster head is more efficient. From clustering procedure, appropriate cluster heads are selected with attention to energy saving among member nodes. When it comes to WSN security, trust-based cluster head selection is critical, assuming the cooperation of all sensor nodes. In light of this assumption, existing approaches were unable to assist in identifying the network’s ideal cluster head. Due to the dynamic topology and mobility of nodes in WSNs, security is a challenge. However, secrecy is often accomplished end-to-end via symmetric keys between two corresponding applications. The symmetric key is incompatible with the WSN environment. Additionally, the WSN nodes vary in their qualification for which portion of the data is accessible in which context. This work proposes an elliptic curve cryptography-enabled ciphertext policy attribute-based encryption (ECC-CP-ABE) algorithm for secure data transmission during intra-cluster communication and inter-cluster communication. In this work, we select a reliable node-based trust value mechanism and this reliable node acts as an attribute authority that inter-cluster provides a decryption private key to cluster members who are involved in intra- and inter-cluster communications. In ECC-CP-ABE, the cluster head node (CHN) and cluster member node (CMN) utilize CP-ABE for the encryption of network messages using an access policy matrix A that is computed from an AND–OR operation-based monotonic tree access structure which is defined over a various set of attributes. To ensure authenticity and integrity, the cluster member and CHN sign each ciphertext using an ECC algorithm. For performance evaluation, we use packet delivery ratio, energy consumption, encryption time, network lifetime, and decryption time as metric measures and compared the results of proposed ECC-CP-ABE with two benchmark methods, Secured WSN and Taylor-based Cat Salp Swarm Algorithm. From the results, we analyze that the proposed ECC-CP-ABE reduces energy consumption by 53.2%; increases packet delivery ratio by 98.6%; increases network lifespan by 97.5%, and reduces encryption time by 20[math]s and decryption time by 15[math]s. Citation: International Journal of Cooperative Information Systems PubDate: 2023-02-24T08:00:00Z DOI: 10.1142/S0218843023500028
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Authors:Prateek Sikka Abstract: International Journal of Cooperative Information Systems, Ahead of Print. Cloud computing (CC), which provides numerous benefits to customers, is a new revolution in information technology. The benefits are on-demand, support, scalability, along with reduced cost usage of computing resources. However, with the prevailing techniques, the system’s authentication is still challenging and it leads to being vulnerable. Thus, utilizing Barrel Shift-centric Whirlpool Hashing-Secure Diffie Hellman ASCII Key-Elliptic Curve Cryptography (BSWH-SDHAK-ECC), the hashed access policy (AP)-grounded secure data transmission is presented in this paper. The data owner (DO) registers their information initially. The user login and verify their profile grounded on the registration. The user selects the data to upload to the Cloud Server (CS) after successful verification. The AP is created; after that, the image attributes are extracted; also, utilizing the BSWH approach, a hash code is produced for the AP. Concurrently, by utilizing the Adaptive Binary Shift-based Huffman Encoding (ABSHE) technique, the selected image is compressed. Also, by utilizing the SDHAK-ECC algorithm, the compressed image is encrypted. Lastly, to the CS, the created AP along with the encrypted image is uploaded. The data user sent the request to access and downloads the data. After that, the AP was provided to the user by the data owner. Next, the user sends it to the CS, which checks its AP with the user’s AP. When the AP is matched with the cloud AP, the encrypted data is downloaded and decrypted. Finally, the experimental outcomes revealed that the proposed model achieved a higher security value of 0.9970 that shows the proposed framework’s efficient performance in contrast to the prevailing techniques. Citation: International Journal of Cooperative Information Systems PubDate: 2023-02-06T08:00:00Z DOI: 10.1142/S0218843023500016
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Authors:R. Padmanaban, M. Thirumaran, R. Aruna, L. Jayakumar Abstract: International Journal of Cooperative Information Systems, Ahead of Print. In recent times, enterprise environments such as E-banking, E-commerce, etc., are greatly affected by the evolution of web services and their associated attacks. The process of web service composition gives rise to security issues due to the incompatibility standards of the Simple Object Access Protocol (SOAP) and representational state transfer (REST) features and the compromising service-level agreement (SLA) between the end parties which has not been considered yet. Also, the security assessment process needs to cooperate with good security standards and web services. In case of heterogeneous web services, assessment done using machine learning technologies makes use of pattern analysis where this does not work with the evolving trend behind the web service environment. Hence, there is a need for a security assessment model which will incorporate finite state machine (FSM) in its assessment process to keep track of the various WS-attacks that arise in the web application via web services in heterogeneous environments. The proposed model does the web service composition by compromising all the security conflicts that arise due to the usage of dissimilar web services. We experimented the concept by testing several web services and observed that the performance of the proposed framework in attack detection is accurate and automatic thereby achieving traceability and computability metrics. Citation: International Journal of Cooperative Information Systems PubDate: 2023-01-26T08:00:00Z DOI: 10.1142/S0218843022500034
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Authors:C. Priya, P. M. Durai Raj Vincent Abstract: International Journal of Cooperative Information Systems, Ahead of Print. COVID-19 preventive measures have been a hindrance to millions of people over the globe not only affecting their daily routine but also affecting the mental stability. Among several preventive measures for COVID-19 spread, the lockdown is an important measure which helps considerably reduce the number of cases. The updated news about the COVID-19 is drastically spread in social media. Particularly, Twitter is widely used to share posts and opinions about the COVID-19 pandemic. Sentiment analysis (SA) on tweets can be used to determine different emotions such as anger, disgust, sadness, joy, and trust. But transparence is needed to understand how a given sentiment is evaluated with the black-box machine learning models. With this motivation, this paper presents a new explainable artificial intelligence (XAI)-based hybrid approach to analyze the sentiments of the tweets during different COVID-19 lockdowns. The proposed model attempted to understand the public’s emotions during the first, second, and third lockdowns in India by analyzing tweets on social media, and demonstrates the novelty of the work. A new hybrid model is derived by integrating surrogate model and local interpretable model-agnostic explanation (LIME) model to categorize and predict different human emotions. At the same time, the Top[math]Similarity evaluation metric is employed to determine the similarity between the original and surrogate models. Furthermore, top words using the feature importance are identified. Finally, the overall emotions during the first, second, and third lockdowns are also estimated. For validating the enhanced outcomes of the proposed method, a series of experimental analysis was performed on the IEEE port and Twitter API dataset. The simulation results highlighted the supremacy of the proposed model with higher average precision, recall, F-score, and accuracy of 95.69%, 96.80%, 95.04%, and 96.76%, respectively. The outcome of the study reported that the public initially had a negative feeling and then started experiencing positive emotions during the third lockdown. Citation: International Journal of Cooperative Information Systems PubDate: 2023-01-20T08:00:00Z DOI: 10.1142/S0218843022500058
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Authors:Vishal Garg, Bikrampal Kaur, Tajinder Kumar, Purushottam Sharma, Majed Alowaidi, Sunil Kumar Sharma Abstract: International Journal of Cooperative Information Systems, Ahead of Print. This paper uses the chaotic fuzzy encryption (CFE) technique and a greedy chemical reaction optimization algorithm based on mobile cloud computing to protect data from any assault. Mobile users who use protected mobile cloud computing (Mobi-Cloud) must trust the cloud service provider to keep the data sent from their mobile devices safe when mobile users express strong reservations about storing personal information in the public cloud. The Greedy Chemical Reaction Optimization (G-CRO) algorithm reduces critical limitations by determining the offloading end with motion at run time, i.e. the task’s operating time, CPU usage, memory utilization, and energy usage. The experimental results show that the proposed method performs better in terms of file uploading time, file downloading time, memory usage in file uploading, memory usage in file downloading, encryption time, decryption time, memory usage on file encryption, memory usage on file decryption, security levels, and key generation time. Compared to the Proxy Re-Encryption (PRE) algorithm, the suggested method consumes less time for key creation (26.04). Citation: International Journal of Cooperative Information Systems PubDate: 2022-12-19T08:00:00Z DOI: 10.1142/S0218843022500022
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Authors:Yingchao Bai, Yu Song Abstract: International Journal of Cooperative Information Systems, Ahead of Print. In order to explore the regional evaluation and distribution characteristics of enterprises’ technological innovation capabilities, this paper introduces the Logistic model, a commonly used model in ecology to analyze the law of population growth. Moreover, this paper uses satellite-based symbiosis mode, network-based symbiosis mode, and network-satellite compound symbiosis mode to establish models to solve the stable equilibrium point, and summarize the symbiosis stability conditions of innovation clusters in various modes. In addition to this, this paper combines the Internet of Things and big data technology to study the regional evaluation and distribution characteristics of enterprise technological innovation capabilities, and build an intelligent model based on the Internet of Things and big data. The research results show that the research system of regional evaluation and distribution characteristics of enterprise technological innovation capabilities based on the Internet of Things and big data proposed in this paper has good results. Finally, this paper puts forward relevant suggestions with the support of the model proposed in this paper. Citation: International Journal of Cooperative Information Systems PubDate: 2022-10-26T07:00:00Z DOI: 10.1142/S0218843021500040
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Authors:Xiaoli Zhang Abstract: International Journal of Cooperative Information Systems, Ahead of Print. Electronic commerce is a principle that is getting momentum in the business world. Like other e-commerce papers, the purpose of this paper was to enlighten on e-commerce adaptation, trends, and concerns that are actively encouraging and inhibiting its efficiencies by conducting a thorough analysis of relevant literature in the field. It has been observed that there has been an increasing tendency of e-commerce implementation nationally and internationally, even in the least developed countries, which is encouraged by e-commerce adoption gains for businesses such as improved sales possibilities, economic viability with enlarged coverage, and improved customer care. Apart from browser buying, online companies offer a variety of Web-based advertising alternatives to businesses; retailers have been captivated by the concept of expanding their marketplace via digital portals and have applied it to improve customer experience. The use of technologies in online buying or advertising is performed to carry out increased marketing. The popularity of online purchasing is rapidly increasing. In today’s commercial world, e-commerce is booming. E-commerce entails the purchase and sale of products and services, as well as the transmission of payments or information, over an electronic network, most commonly the Internet. This research paper analyzes the concept and working of e-commerce platforms. Moreover, the main objective of this research paper is to investigate different meanings of e-commerce for the masses. This paper proposes a solution to tackle all issues and mischiefs that are related to virtual assistants (VAs) and gives an infrastructure about how businesses can flourish without VAs. Citation: International Journal of Cooperative Information Systems PubDate: 2022-09-12T07:00:00Z DOI: 10.1142/S0218843021500118
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Authors:Bin Wang, Yan-Li Wang Abstract: International Journal of Cooperative Information Systems, Ahead of Print. In order to improve the transportation effect of the autonomous transportation logistics system, this paper combines the Internet of Things and blockchain technology to construct an intelligent logistics system, and establishes the principle of deterministic deviation compensation based on the two-point method. Moreover, this paper calculates two deterministic deviations through the known coordinates of the starting and ending points and compensates them into the heading angle and pitch angle measured by the inertial navigation system respectively. Further, this paper uses the basic formula of dead reckoning to calculate the trajectory after compensation to achieve the effect of improving positioning accuracy. Finally, this paper constructs an autonomous transportation intelligent logistics system based on the Internet of Things and blockchain technology. The experimental research results show that the simulation effect of the autonomous transportation intelligent logistics system based on the Internet of Things and blockchain technology is very good. Citation: International Journal of Cooperative Information Systems PubDate: 2022-07-21T07:00:00Z DOI: 10.1142/S0218843021500064
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