Simultaneous optimization of multiple performance characteristics in coagulation-flocculation process for Indian paper industry wastewater

The goal of this study was to optimize the coagulation-flocculation process in wastewater generated from the paper and pulp industry using a grey relational analysis (GRA)-based Taguchi method. Process parameters included types and doses of natural coagulants and coagulant aid, and pH. To track the efficiency of the treatment process, the following responses were chosen for optimization: chemical oxygen demand (COD), total dissolved solids (TDS) and turbidity of wastewater, alone or in combination or all together. Analysis of variance showed that the type and dose of the coagulant aid were the most significant parameters, followed by pH and the dose of the coagulant; the type of coagulant used was found to be insignificant in the coagulation-flocculation process. Optimization of process parameters to achieve lower turbidity and greater removal of COD and TDS was verified in a separate confirmatory experiment, which showed improvements in COD and TDS removal and a decrease in turbidity of 8.2, 6.35 and 26.17%, respectively, with the application of the Taguchi method and GRA.     

User Experience of On-Screen Interaction Techniques: An Experimental Investigation of Clicking,Sliding, Zooming,Hovering, Dragging,and Flipping

From scrolling and clicking to dragging, flipping, sliding, hovering, and zooming, the wide array of interaction techniques has vastly expanded the range of user actions on an interface. Each of these interaction techniques affords a distinct action. But do these techniques differ in their ability to engage users and contribute to their user experience? Furthermore, do they affect how users view the content and how much they learn from it? We address these questions via two between-subjects laboratory experiments. Study 1 (N = 128) investigated the relative effects of six on-screen interaction techniques (click-to-download, drag, mouseover, slide, zoom, and 3D carousel) on users' assessment of—as well as their engagement with—an informational website. The site for each condition was identical in content and design, except for the interaction technique used, so that we could isolate the effects of each technique on various cognitive, attitudinal and behavioral outcomes. Study 2 (N = 127) examined the relative effects of four combinations of interaction techniques (slide+click, slide+mouseover, drag+mouseover, and drag+zoom) on the same dependent variables. Data from Study 1 suggest that although the 3D carousel generates more user action, the slide is better at aiding memory. The zoom-in/out tool was the least favored, whereas the mouseover feature fostered greater engagement among power users. Findings from Study 2, which was conducted with a different content domain, replicated the positive effects of slide and negative effects of drag in influencing user experience. Path analyses, using structural equation modeling, revealed the importance of users' assessment of the interface (perceived levels of natural mapping, intuitiveness, and ease of use), which can have significant consequences for user engagement as well as resulting attitudes and behavioral outcomes. Design insights, theories, and techniques to test and capture user experience are discussed.     

Improved Secure Identification-Based Multilevel Structure of Data Sharing in Cloud Environments

The Cloud Computing Environment (CCE) developed for using the dynamic cloud is the ability of software and services likely to grow with any business. It has transformed the methodology for storing the enterprise data, accessing the data, and Data Sharing (DS). Big data frame a constant way of uploading and sharing the cloud data in a hierarchical architecture with different kinds of separate privileges to access the data. With the requirement of vast volumes of storage area in the CCEs, capturing a secured data access framework is an important issue. This paper proposes an Improved Secure Identification-based Multilevel Structure of Data Sharing (ISIMSDS) to hold the DS of big data in CCEs. The complex file partitioning technique is proposed to verify the access privilege context for sharing data in complex CCEs. An access control Encryption Method (EM) is used to improve the encryption. The Complexity is measured to increase the authentication standard. The active attack is protected using this ISIMSDS methodology. Our proposed ISIMSDS method assists in diminishing the Complexity whenever the user’s population is increasing rapidly. The security analysis proves that the proposed ISIMSDS methodology is more secure against the chosen-PlainText (PT) attack and provides more efficient computation and storage space than the related methods. The performance of the proposed ISIMSDS methodology provides more efficiency in communication costs such as encryption, decryption, and retrieval of the data.     

Deep Learning Enabled Social Media Recommendation Based on User Comments

Nowadays, review systems have been developed with social media Recommendation systems (RS). Although research on RS social media is increasing year by year, the comprehensive literature review and classification of this RS research is limited and needs to be improved. The previous method did not find any user reviews within a time, so it gets poor accuracy and doesn’t filter the irrelevant comments efficiently. The Recursive Neural Network-based Trust Recommender System (RNN-TRS) is proposed to overcome this method’s problem. So it is efficient to analyse the trust comment and remove the irrelevant sentence appropriately. The first step is to collect the data based on the transactional reviews of social media. The second step is pre-processing using Imbalanced Collaborative Filtering (ICF) to remove the null values from the dataset. Extract the features from the pre-processing step using the Maximum Support Grade Scale (MSGS) to extract the maximum number of scaling features in the dataset and grade the weights (length, count, etc.). In the Extracting features for Training and testing method before that in the feature weights evaluating the softmax activation function for calculating the average weights of the features. Finally, In the classification method, the Recursive Neural Network-based Trust Recommender System (RNN-TRS) for User reviews based on the Positive and negative scores is analysed by the system. The simulation results improve the predicting accuracy and reduce time complexity better than previous methods.     

Enforcing a Source-end Cooperative Multilevel Defense Mechanism to Counter Flooding Attack

The exponential advancement in telecommunication embeds the Internet in every aspect of communication. Interconnections of networks all over the world impose monumental risks on the Internet. A Flooding Attack (FA) is one of the major intimidating risks on the Internet where legitimate users are prevented from accessing network services. Irrespective of the protective measures incorporated in the communication infrastructure, FA still persists due to the lack of global cooperation. Most of the existing mitigation is set up either at the traffic starting point or at the traffic ending point. Providing mitigation at one or the other end may not be a complete solution. To insist on better protection against flooding attacks, this work proposes a cooperative multilevel defense mechanism. The proposed cooperative multilevel defense mechanism consists of two-level of mitigation. In the first level, it is proposed to design a Threshold-based rate-limiting with a Spoofing Resistant Tag (TSRT), as a source end countermeasure for High-Rate Flooding Attacks (HRFA) and spoofing attacks. In the second level, the accent is to discriminate normal traffic after Distributed Denial of Service (DDoS) traffic and drop the DDoS traffic at the destination end. Flow Congruence-based Selective Pushback (FCSP), as a destination-initiated countermeasure for the Low Rate Flooding Attack (LRFA). The source and the destination cooperate to identify and block the attack. A key advantage of this cooperative mechanism is that it can distinguish and channel down the attack traffic nearer to the starting point of the attack. The presentation of the agreeable cooperative multilevel safeguard mechanism is approved through broad recreation in NS-2. The investigation and the exploratory outcomes show that the proposed plan can effectively identify and shield from the attack.