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1. 发明专利

AU2021105401A4 INTELLIGENT DRUG QUALITY CONTROL ANALYSIS SYSTEM AND METHOD BASED ON MACHINE LEARNING 未知
公开(公告)号：AU2021105401A4
公开(公告)日：2021-11-11
申请号：AU2021105401
申请日：2021-08-12
申请人： BHAMBRI PANKAJ DR , DANGI RAVI RAI MR , DAS SANTANU DR , INDRA GAURAV DR , JAYASHREE RAJENDRAN MS , K KANNAN DR , M RAVINDER DR , MAHATO NIRANJAN , MARU SAURABH , PRIYA B KEERTHI , SHARMA JITENDRA K DR , SINHA DIVYANSHU , SRINIVAS AMEDAPU
发明人： DAS SANTANU , INDRA GAURAV , SRINIVAS AMEDAPU , SINHA DIVYANSHU , K KANNAN , JAYASHREE RAJENDRAN , M RAVINDER , PRIYA B KEERTHI , MARU SAURABH , SHARMA JITENDRA K , BHAMBRI PANKAJ , DANGI RAVI RAI , MAHATO NIRANJAN
IPC分类号： G16Y10/60 , G06N20/00 , G16B45/00 , G16H70/40 , G16Y40/20
摘要： INTELLIGENT DRUG QUALITY CONTROL ANALYSIS SYSTEM AND METHOD BASED ON MACHINE LEARNING The present invention is related to intelligent drug quality control analysis system and method based on machine learning. The objective of present invention is to solve the abnormalities presented in the prior art techniques related quality control of drugs during productions in automation based industries. Sensor Drugs monitoring Unit 0 /••••Machine learnng Module eami unit

2. 发明专利

AU2021105706A4 Dynamic weight assignment mechanism for determining trustworthiness of data in block chain 未知
公开(公告)号：AU2021105706A4
公开(公告)日：2021-11-25
申请号：AU2021105706
申请日：2021-08-17
申请人： CHAKRABARTI PRASUN DR , HASAN MOHD ASIF DR , INDRA GAURAV DR , KUMAR SANJAY MR , M PAVITHRA MS , NAGARAJU ANIMONI DR , NEWARE RAHUL MR , PRAVEENA A MS , RAVINDRAN ROSHNA MS , SHARMA SUVARNA DR , SRIDHARAN K DR , VELAYUDHAM A DR
发明人： PRAVEENA A , RAVINDRAN ROSHNA , VELAYUDHAM A , M PAVITHRA , INDRA GAURAV , SRIDHARAN K , SHARMA SUVARNA , HASAN MOHD ASIF , KUMAR SANJAY , NAGARAJU ANIMONI , NEWARE RAHUL , CHAKRABARTI PRASUN
IPC分类号： H04L29/06 , G06F16/182 , H04L29/08
摘要： DYNAMIC WEIGHT ASSIGNMENT MECHANISM FOR DETERMINING TRUSTWORTHINESS OF DATA IN BLOCKCHAIN This study proposed to leverage the powerful attributions obtained from the blockchain synergy and trust to successfully fight many security as well as trust problems inherent in decentralized IoT networks. Thus, a thorough examination of existing blockchain based trustworthiness methods, as well as information on their applicability to decentralized loT networks. Then it offers a comprehensive dynamic weight assignment mechanism that analyzes the trustworthiness of all entities, thus ensuring the current service providers. A complete decentralized trust assessment method eliminates single points of failure. Moreover, in trust contests, community members are being encouraged to participate in a variety of community duties. Thus, the novel dynamic weight assignment mechanism includes a dynamic weighting to compute the trustworthy value of data for IoT community participants. The trustworthiness of data evaluation, as well as aggregation stages, are significantly improved regarding trustworthiness and reliability using the proposed mechanism. Only trustworthy neighbors are permitted to advance, generate, and validate the various trust procedures' transactions/blocks. Furthermore, this study suggests combining blockchain with control loops to create a unique idea that improves the ecosystem's security by encouraging low-trust peers to enhance overall community behavior. The proposed dynamic weight assignment mechanisms to increase the accuracy of trustworthiness and protect against data assaults, the trust value is computed using, directly and indirectly, trust in the blockchain. The proposed dynamic weight assignment mechanism changes dynamically with the number of communication contacts to guarantee the trust value of data. To ensure that the proposed dynamic model can be designed and efficiently applied to various environments with varying criteria as well as against on-off attacks depending on the weighted averaging operator to increase flexibility.

3. 发明专利

AU2021107185A4 Designing Imperceptible-High Payload Capacity Steganography Framework in Post Quantum Encrypted Domain using Deep Advanced Hierarchical Feature Learning. 未知
公开(公告)号：AU2021107185A4
公开(公告)日：2021-12-09
申请号：AU2021107185
申请日：2021-08-25
申请人： INDRA GAURAV DR , M RAVINDER DR , MALIK KIRAN
发明人： MALIK KIRAN , M RAVINDER , INDRA GAURAV
IPC分类号： H04N1/32 , G06F16/50 , G06N3/02 , H04L9/00 , H04L29/08
摘要： Our invention Designing Imperceptible-High Payload Capacity Steganography Framework in Post Quantum Encrypted Domain using Deep Advanced Hierarchical Feature Learning is a persists a danger that intruders may get access to the sensitive information, causing it to be shared, exposed, modified, and impacting its availability. Since fast multimedia communication via different low-cost mediums such as smartphones and Social Networking platforms may be readily exploited using an unsafe channel, steganography has come to known as one of the most prominent fields of study. Steganography is a technique of concealing a confidential small multimedia information within a much bigger piece of multimedia information, which can be anything, be it an image, a video or a text file. Technique of concealing one image within another image is known as Image Steganography. The cover image in which the secret information needs to be hidden in image steganography is altered in such a manner that the concealed information is not evident, which makes it less dubious than cryptography. The embedding and extraction algorithms are the two most important parts of any advanced steganographic framework. The secret image, the secret key, and the cover image, which will be used to communicate the message, are all inputs to the embedding method. The stego image is the result of the embedding process. The stego image is also supplied as an input to the extraction process. Steganalysis, on the other hand, is a technique used to identify the existence of any hidden information in an image and to extract that information. Steganalysis aims in determining if an image is a stego image or an original image. TOTAL NO OF SHEET: 02 NO OF FIG: 04 Cover 00Cover Media Media Embeddi* StegO. Extraing Atgerithm Oct Algerin Secret __I104 106 Secret 107 Message Messge Figure 1: Basic Steganography Framework \200 Host Irnage CotIrr rnage Secret Image ReeldImage Encoding rarework Decoing Framework Figure 2: A Basic Deep Steganography Framework Plain tex (sender) Cipher text (receiver) 301 alg n publicchannel Drg ion 302 Fey key Quantum Que ryturn channel G r 30 Figure 3: Basic Post-Quantum Cryptography Framework

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