http://ijsrisjournal.com/index.php/ojsfiles/issue/feed International Journal of Scientific Research and Innovative Studies 2024-06-26T10:05:35+00:00 IJSRIS Journal ijsrisjournal@gmail.com Open Journal Systems <p><strong>IJSRIS is a Peer Reviewed International Journal publishing multidisciplinary research articles.</strong></p> <p>IJSRIS stands as a platform to bring out the research talent and the works of scientists, academia, engineers, practitioners, research scholars and students. </p> <p>All the submitted articles should report original, unpublished research arcitles, experimental or theoretical and it will undergo the double blind review process. Articles submitted to the journal should meet these criteria and must not be under consideration for publication elsewhere. Manuscripts submitted should follow the style of the journal and are subject to both review and editing.</p> <p>IJSRIS is a scholarly open access Online Journal which helps to academic person as well as student community. IJSRIS provides the academic community and industry for the submission of original research and applications. The journal also invites clearly written reviews, short communications and notes dealing with numerous disciplines covered by the fields. We also accept extended version of papers which are previously published in conferences and/or journals.</p> <p>All the papers selected for publication are also available freely as full-text in on-line.</p> <p><img 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" /></p> http://ijsrisjournal.com/index.php/ojsfiles/article/view/149 On the use of Transfer Learning to Improve Breast Cancer Detection 2024-05-19T16:00:18+00:00 Khadija Aguerchi Khadija.aguerchi@ced.uca.ma Younes jabrane y.jabrane@uca.ma Maryam habba m.habba@uca.ma Mustapha Ameur m.ameur@uca.ma <p>Breast cancer is one of the most common malignancies in women globally, and early identification is critical for better patient outcomes. Deep learning has developed in recent years as a promising approach for automating the identification of breast cancer in mammograms. Transfer learning, which involves adapting a pre-trained model to a new task, is a promising method for enhancing the efficiency and accuracy of breast cancer diagnosis using deep learning. This work studies the efficacy of transfer learning strategies in detecting breast cancer using pre-trained deep-learning models. Using large mammographic datasets, we investigate several transfer learning algorithms and assess their effects on detection performance metrics such as accuracy, precision, recall and ROC AUC. The study's results enhance automated breast cancer detection and shed light on how well transfer learning strategies can improve the precision and dependability of detection.</p> <p><strong>Keywords: </strong>Breast cancer, transfer learning, medical imaging, deep learning.</p> <p> </p> <p> </p> <table width="636"> <tbody> <tr> <td> <p><strong>Received Date:</strong> April 05, 2024 <strong>Accepted Date:</strong> May 14, 2024 </p> <p><strong>Published Date:</strong> June 01, 2024</p> <p> </p> <p><strong>Available Online at: <a href="https://www.ijsrisjournal.com/index.php/ojsfiles/article/view/149">https://www.ijsrisjournal.com/index.php/ojsfiles/article/view/149</a></strong></p> <p><strong>DOI:</strong><strong><a href="https://doi.org/10.5281/zenodo.11217339">https://doi.org/10.5281/zenodo.11217339</a> </strong></p> </td> </tr> </tbody> </table> <p> </p> 2024-06-01T00:00:00+00:00 Copyright (c) 2024 http://ijsrisjournal.com/index.php/ojsfiles/article/view/150 Efficient Service Composition Optimization under Uncertain Environment 2024-05-19T19:18:15+00:00 REMACI Zeyneb Yasmina zeynebyasmina.remaci@univ-tlemcen.dz <p>Over the past few years, companies have been offring a variety of functionalities through Web services as part to stay competitive. Meanwhile, the customers may face challenges in choosing a service that meets their requirements due to the similarity of functionalities. Moreover, the customers’ requests often emerge with a complex nature in an uncertain environment that is rarely fulfilled by an atomic service. Hence, there is a necessity for optimizing service composition based on Quality of Service (QoS) with taking into account environmental uncertainty. To attain the mentioned goal, our proposed approach adopts a local and global optimization strategies. Firstly, we adopt a heuristic based on the Entropy, Cross-Entropy, and the deviation degree for the hesitant fuzzy set to rank similar Web services. Afterwards, we introduce an improved metaheuristic called Group Leaning based Composition as a global optimization for selecting the near-optimal composition.</p> <p> </p> <p><strong>Key words :</strong> Deviation degree, Global Optimization, Group learning metaheuristic, Hesitant fuzzy set, Local Optimization, Service Composition, Quality of Service, Web Service.</p> <p> </p> <p> </p> <table width="636"> <tbody> <tr> <td> <p><strong>Received Date:</strong> April 09, 2024 <strong>Accepted Date:</strong> May 07, 2024 </p> <p><strong>Published Date:</strong> June 01, 2024</p> <p> </p> <p><strong>Available Online at: <a href="https://www.ijsrisjournal.com/index.php/ojsfiles/article/view/149">https://www.ijsrisjournal.com/index.php/ojsfiles/article/view/150</a></strong></p> <p><strong>DOI:<a href="https://doi.org/10.5281/zenodo.11217801" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11217801</a></strong></p> </td> </tr> </tbody> </table> 2024-06-01T00:00:00+00:00 Copyright (c) 2024 http://ijsrisjournal.com/index.php/ojsfiles/article/view/151 Understanding User Intention in Pervasive Environments: A Literature Review and Perspectives 2024-05-21T19:00:51+00:00 Abdelhadi Bouain a.bouain@uiz.ac.ma Mohamed Nezar ABOURRAJA mnabour@kth.se Mohamed Yassine Samiri m.samiri@uca.ac.ma <p>Identifying user intention (what the user wishes to achieve within a system) with minimal or ideally no direct user interaction is a major goal in pervasive computing. Achieving this goal requires a clear and consistent definition of intention, a concept widely used but understood differently across various studies. In this work, we first aim to clarify the different interpretations of intention, distinguishing between implicit and explicit intention. Subsequently, we compare various existing approaches from the literature, seeking to reconcile these diverse viewpoints and establish a common foundation for future research efforts.</p> <p> </p> <p><strong>Key words:</strong> Pervasive computing, User intention, User intent prediction, Multimodal Large Language Models.</p> <p> </p> <p> </p> <p><strong>Received Date:</strong> April 10, 2024 <strong>Accepted Date:</strong> May 08, 2024 </p> <p><strong>Published Date:</strong> June 01, 2024</p> <p><strong>Available Online at: <a href="https://www.ijsrisjournal.com/index.php/ojsfiles/article/view/151">https://www.ijsrisjournal.com/index.php/ojsfiles/article/view/151</a></strong></p> <p><strong>DOI:</strong><strong> <a href="https://doi.org/10.5281/zenodo.11237705">https://doi.org/10.5281/zenodo.11237705</a></strong></p> 2024-06-01T00:00:00+00:00 Copyright (c) 2024 http://ijsrisjournal.com/index.php/ojsfiles/article/view/152 Hand Gesture Recognition Using Convolutional Neural Networks 2024-05-21T20:49:31+00:00 Veluru Karthik Reddy 2000090063csit@gmail.com Vanapalli Durga Prasanth 2000050008csit@gmail.com R.Shiva rama krishna 2000090062csit@gmail.com Naidu Sri lekha 2000090025csit@gmail.com Jyothi N.M jyothiarunkr@gmail.com <p>Hand gestures play a crucial role in communication and are essential in various scenarios where verbal communication is not possible. For instance, traffic policemen, newsreaders, airport staff, and gamers often rely on hand signals to communicate. Therefore, there is a growing need for robust hand pose recognition (HPR) methods that can identify hand gestures accurately. However, the current state-of-the- art HPR methods struggle with identifying hand gestures in the presence of cluttered backgrounds .To address this challenge, we propose a deep learning framework based on convolutional neural networks (CNNs) to identify hand postures regardless of hand size, location in the image, and background clutter. Our proposed CNN-based approach eliminates the need for feature extraction and learns to recognize hand poses without explicit foreground segmentation. This method effectively identifies hand poses, even in the presence of complex and varying backgrounds or poor lighting conditions .We have conducted several experiments, which demonstrate the superiority of our proposed method over state-of-the-art datasets. Our approach significantly improves the accuracy of hand pose recognition, making it more reliable and efficient for a wide range of applications. Therefore, our proposed method has significant potential for use in real-world scenarios, such as traffic management, sign language interpretation, and virtual reality gaming .Overall, our results suggest that deep neural networks can provide a robust and effective solution for hand gesture recognition tasks.</p> <p> </p> <p><strong>Keywords</strong><strong><em>— </em></strong>HPR , CNN, Segmentation ,Background clutter, Virtual Reality ,Neural Networks.</p> <p> </p> <p> </p> <p><strong>Received Date:</strong> April 06, 2024 <strong>Accepted Date:</strong> May 08, 2024 </p> <p><strong>Published Date:</strong> June 01, 2024</p> <p><strong>Available Online at: <a href="https://www.ijsrisjournal.com/index.php/ojsfiles/article/view/152">https://www.ijsrisjournal.com/index.php/ojsfiles/article/view/152</a></strong></p> <p><strong>DOI: </strong><strong><a href="https://doi.org/10.5281/zenodo.11237998">https://doi.org/10.5281/zenodo.11237998</a></strong></p> <p> </p> <p> </p> <p> </p> 2024-06-01T00:00:00+00:00 Copyright (c) 2024 http://ijsrisjournal.com/index.php/ojsfiles/article/view/153 Eating Smart: Advancing Health Informatics with the Grounding DINO-based Dietary Assistant App 2024-05-22T14:51:05+00:00 Abdelilah Nossair a.nossair@aui.ma Hamza El Housni h.elhousni@aui.ma <p>The Smart Dietary Assistant project combines technology and Machine Learning (ML) to offer personalized advice for people with dietary concerns such as diabetes. This approach focuses on the user helping them make decisions about their diet using the Grounding DINO model. Grounding DINO uses a text encoder and image backbone to improve detection accuracy without relying on a labeled dataset making it practical for real world situations with various food types. This model uses a 52.5 AP score on the COCO dataset and attention mechanisms that leverage features based on user-provided labels and food images to allow precise object recognition. The feature is at the core of the user app, turning smartphones into a helpful dietary advisor that enables people to manage their health effectively.</p> <p>&nbsp;</p> <p>The app can use your device camera to take photos that will be analyzed by the model for detection and categorize the food items correctly. This is what differs in this system: it decides to be free and not to be connected to annoying cloud databases of information. The application uses a database managed by itself that is of PostgreSQL type, ensuring the preservation of data integrity and control. This database hosting information includes all types of food products, from profiles to health insights drawn from their consumption by human beings. This helps in effective and efficient data access speed, reliability, and enhances user privacy through localized storage within the organizational infrastructure.</p> <p>&nbsp;</p> <p>The app focuses on improving the experiences of the users, considering that it allows them to create profiles through which they describe themselves based on preferences and tips on nutrition. In addition to calories information, the app provides insights to nutrients such as proteins, vitamins, and minerals. This makes it possible for one to decide the kind of food to take, either for weight management, muscle building, or managing health conditions. On the other part, it also assesses food compatibility versus profiles and gives personal recommendations for alternatives and recipes. Such kind of personal help is highly convenient for persons with needs as it helps them take their healthy options confidently.</p> <p>Developed using React Native and TypeScript, the Smart Dietary Assistant app guarantees operation across devices and platforms. It incorporates technologies beyond modeling to ensure optimal performance in food recognition, scalability for future enhancements and seamless integration, with other dietary tools. Users have the option to enjoy features like using the camera to scan food items, for tracking habits and receiving insightful analysis. They can also interact with an assistant for recommendations. The protection of data is ensured through user authentication whereas customizable settings enhance the user experience. React Native enables smooth screen transitions. The expo camera allows scanning capabilities. Local storage efficiently manages data to create an easy/appealing to use interface.</p> <p>The Smart Dietary Assistant app’s interface stands out for striking a balance between aesthetics and usability. The use of buttons, and a vibrant color scheme enhances user experience by making navigation and feature selection simple. The chatbot feature, represented by an avatar encourages user engagement and personalized guidance seeking. Users find camera scanning convenient although it is noted that varying lighting conditions may affect accuracy. It is this appreciation that opened doors to improvement that can guarantee success in all situations.</p> <p>The choice of a self-hosted PostgreSQL database for this project re-emphasizes its importance in the realms of health informatics and nutritional science. This is data that can be stored without really depending on outside cloud services, and just with that, the same can be retained as reliable information, since there are chances that it can be changed from the outside.</p> <p>In the future, the Smart Dietary Assistant is planned to be empowered with collaboration with devices. With this development, the application can sync with fitness trackers and smartwatches to give time-based suggestions from physiological data such as blood sugar level and calories burnt. This will connect users to devices that give them individualized advice regarding their health needs, depending on the style of activity. The application is open to collaborations with AI-powered tools in the development of personalized recipes and meal plans that would give the user an easy time adhering to his preferences, dietary restrictions, and time-in sync physiological information. With conditions like diabetes, this holistic approach to diet management is deemed beneficial because it would make the app utilities more effective, always supports objectives for weight management or muscle building, and therefore supports the overall well-being of the user.</p> <p>&nbsp;</p> <p><strong>Key words:</strong> Food Image Recognition, Machine Learning in Nutrition, Zero-Shot Object Detection.</p> <p>&nbsp;</p> <p><strong>Received Date:</strong> April 07, 2024 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <strong>Accepted Date:</strong> May 09, 2024 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</p> <p><strong>Published Date:</strong> June 01, 2024</p> <p><strong>Available Online at: </strong><strong><a href="https://www.ijsrisjournal.com/index.php/ojsfiles/article/view/153">https://www.ijsrisjournal.com/index.php/ojsfiles/article/view/153</a></strong></p> <p><strong>DOI: </strong><strong><a href="https://doi.org/10.5281/zenodo.11243881">https://doi.org/10.5281/zenodo.11243881</a></strong></p> <p>&nbsp;</p> <p>&nbsp;</p> 2024-06-01T00:00:00+00:00 Copyright (c) 2024 http://ijsrisjournal.com/index.php/ojsfiles/article/view/154 A decision support system driven by artificial intelligence for industrial applications 2024-05-22T15:18:38+00:00 Hala Mellouli hala.mellouli-etu@etu.univh2c.ma Anwar Meddaoui anwar.meddaoui@ensam-casa.ma Abdelhamid Zaki abdelhamid.zaki@etu.univh2c.ma <p>Decision-making in industrial settings is a continuous process that drives the organization's overall performance. It implies consistently selecting the optimal alternative, regularly reviewing the effectiveness of the decision, learning from its consequences, and refining the decision-making framework accordingly. in the modern era, characterized by the abundance of data, the ineffectiveness of conventional multi-criteria decision-making methods to process large volumes of data prevails over their ability to manage the multidimensional nature of decision-making in industrial settings, hence to cope with the increasing complexity of process industrials are challenged to explore the potential of artificial intelligence to optimize their decisions. In the current work, a new decision-making approach is introduced, the model combines artificial neural networks with the Analytic Hierarchy Process and the balanced scorecard to provide real-time decision-making recommendations for complex industrial problems.</p> <p> </p> <p><strong>Key words:</strong> Industrial performance, Artificial Neural Network, Analytical Hierarchy Process, Decision support system.</p> <p> </p> <p><strong>Received Date:</strong> April 10, 2024 <strong>Accepted Date:</strong> May 10, 2024 </p> <p><strong>Published Date:</strong> June 01, 2024</p> <p><strong>Available Online at: </strong><strong><a href="https://www.ijsrisjournal.com/index.php/ojsfiles/article/view/154">https://www.ijsrisjournal.com/index.php/ojsfiles/article/view/154</a></strong></p> <p><strong>DOI: </strong><strong><a href="https://doi.org/10.5281/zenodo.11244054">https://doi.org/10.5281/zenodo.11244054</a></strong></p> 2024-06-01T00:00:00+00:00 Copyright (c) 2024 http://ijsrisjournal.com/index.php/ojsfiles/article/view/155 Semantic Enrichment of Moroccan Tourism Ontology Based NLP Technologies 2024-05-22T15:47:07+00:00 ASMA AMALKI asma.amalki@edu.uiz.ac.ma KHALID TATANE k.tatane@uiz.ac.ma ALI BOUZIT a.bouzit@uiz.ac.ma <p>Ontology is a conceptual representation model that allows sharing and reuse of a domain knowledge in a human and machine-readable format. However, the massive amount of knowledge available today makes ontology enrichment a challenging task. In this paper, we present a semi-automatic approach of ontology learning from collection of domain specific texts, to text processing based NLP tools, to enrich an existing ontology. This study utilize data crawling from official websites. Concepts and relations extraction is done automatically from a textual corpus and domain experts do consistency and redundancy check manually. The proposed approach is applied in order to enrich Moroccan tourism ontology named OTM with semantic entities extracted from heterogeneous data sources.</p> <p> </p> <p><strong>Keywords:</strong> Corpus, Knowledge acquisition, NLP, ontology enrichment, ontology learning.</p> <p> </p> <p><strong>Received Date:</strong> April 11, 2024 <strong>Accepted Date:</strong> May 10, 2024 </p> <p><strong>Published Date:</strong> June 01, 2024</p> <p><strong>Available Online at: </strong><strong><a href="https://www.ijsrisjournal.com/index.php/ojsfiles/article/view/155">https://www.ijsrisjournal.com/index.php/ojsfiles/article/view/155</a></strong></p> <p><strong>DOI: </strong><strong><a href="https://doi.org/10.5281/zenodo.11244423">https://doi.org/10.5281/zenodo.11244423</a></strong></p> 2024-06-01T00:00:00+00:00 Copyright (c) 2024 http://ijsrisjournal.com/index.php/ojsfiles/article/view/156 New Compression Point Reducing Memory Size in Field of Characteristic Different From 2 And 3 2024-05-22T16:25:42+00:00 ASSOUJAA ISMAIL ismail.assoujja@usmba.ac.ma EZZOUAK SIHAM siham.ezzouak@usmba.ac.ma <p>Compression point is a new method to compress the space memory and still have the same data. In this paper, we will present a new method of compression points work well with addition opera- tion in elliptic curve, so instead of storing the value of two points P = <strong>(<em>x</em></strong><strong><em>P </em>, <em>y</em><em>P </em>), </strong>Q = <strong>(<em>x</em></strong><strong><em>Q </em>, <em>y</em><em>Q </em>), </strong>we will store the addition of the x- coordinates i,e (<strong>α = <em>x</em></strong><strong><em>P </em></strong>+ <strong><em>x</em></strong><strong><em>Q </em></strong>, <strong><em>y</em></strong><strong><em>P </em></strong>, <strong><em>y</em></strong><strong><em>Q </em></strong>) or the y-coordinates i,e (<strong><em>x</em></strong><strong><em>P </em></strong>, <strong><em>x</em></strong><strong><em>Q </em></strong>, <strong>β = <em>y</em></strong><strong><em>P </em></strong>+ <strong><em>y</em></strong><strong><em>Q</em></strong>). In this article, we show a new technique for compressing two points in elliptic curve with different coordinate system: Affine, Projective and Jacobian in a field of characteristic ≠ 2 &amp; 3, and show the cost of theses operations. This method can save if we work with affine, Projective or Jacobian coordinates, at least 25%, 17%, 17% of memory size respectively, and also see what happens in case if we take Edwards curve and Montgomery curve cases.</p> <p><strong>Keywords: </strong>Elliptic curve, Affine coordinate, Projective coordinate, Jacobian coordinate, compression point.</p> <p> </p> <p><strong>Received Date:</strong> April 10, 2024 <strong>Accepted Date:</strong> May 11, 2024 </p> <p><strong>Published Date:</strong> June 01, 2024</p> <p><strong>Available Online at: </strong><strong><a href="https://www.ijsrisjournal.com/index.php/ojsfiles/article/view/156">https://www.ijsrisjournal.com/index.php/ojsfiles/article/view/156</a></strong></p> <p><strong>DOI: </strong><strong><a href="https://doi.org/10.5281/zenodo.11244720">https://doi.org/10.5281/zenodo.11244720</a></strong></p> 2024-06-01T00:00:00+00:00 Copyright (c) 2024 http://ijsrisjournal.com/index.php/ojsfiles/article/view/157 Digitizing Sports Arenas: New Trends and Technologies 2024-05-22T17:04:13+00:00 Malak Jibraili M.jibraili@emsi.ma <p>The digital revolution has transformed the world we live in, including the professional realm, which has harnessed its advantages. Digital technologies are widespread in the sports world, used daily in all its facets: strategically employed by brands, leveraged by coaches and athletes to optimize performance. It's worth noting that the sports spectacle accompanies and even inspires technical innovations (Wille, 2015)1, and the adoption of these tools provides a decisive "competitive advantage," both in sports and commercially (Giblin et al., 2016)2. For the field of sports marketing, the rise of digital offers fervent and loyal supporters, the famous fans, a new way to experience current sports events and immerse themselves in them (Bailey, 2017)3. Today, we talk about a "connected match," a "smart stadium," and "digital sports." It is essential to understand how the introduction of technological tools within sports facilities, especially in the behavior of fans, positively or negatively changes the landscape. A literature review outlines the technological tools introduced in sports venues, the role of social media as a new communication channel, and the digital's position in the sports entertainment industry. A qualitative study then confronts the views of sports managers with those of fans regarding the role of various technological tools within sports facilities.</p> <p>&nbsp;</p> <p><strong>Key words:</strong> Sports Venue, Sports Industry, Technologies, Digital</p> <p>&nbsp;</p> <p><strong>Received Date:</strong> April 13, 2024 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <strong>Accepted Date:</strong> May 15, 2024 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</p> <p><strong>Published Date:</strong> June 01, 2024</p> <p><strong>Available Online at: </strong><strong><a href="https://www.ijsrisjournal.com/index.php/ojsfiles/article/view/157">https://www.ijsrisjournal.com/index.php/ojsfiles/article/view/157</a></strong></p> <p><strong>DOI: </strong><strong><a href="https://doi.org/10.5281/zenodo.11245185">https://doi.org/10.5281/zenodo.11245185</a></strong></p> 2024-06-01T00:00:00+00:00 Copyright (c) 2024 http://ijsrisjournal.com/index.php/ojsfiles/article/view/159 Emerging Jurisprudence on Intellectual Property Rights Pertaining to Generative AI: Comparative Law and Contemporary Judicial Interpretations 2024-06-08T10:20:15+00:00 M.P.Ramaswamy sendemailtomp@gmail.com <p>The advent of generative artificial intelligence (AI) and its increasing applications in various walks of life, albeit generally perceived as utilitarian, raises a wide range of questions regarding its potential implications. Among them, legal and regulatory challenges facing generative AI gain significance due to the indispensable need for legal certainty, which is essential for developing and deploying generative AI in various professional and personal pursuits. Although significant economies worldwide have formulated aspiring policies for leadership in AI development, specific initiatives to create a congenial legal environment are sparse. Despite some regional initiatives and national regulatory standards are starting to emerge, lack of harmonization and a concerted approach are conspicuous. While more international initiatives to establish legal standards governing AI are expected, many national jurisdictions still rely on their existing general legal regimes to govern AI-related developments. However, with the faster growth of generative AI and its wider adoption and accessibility across borders, national courts are increasingly confronted with unprecedented legal claims and regulatory issues. The paper assesses some legal standards and early judicial responses related to generative AI. It identifies evidence of some diversity in national responses and its implications for the development and use of generative AI in key economies. The paper specifically focuses on the intellectual property rights (IPR) issues related to copyrights in works produced by generative AI. It compares the two significant jurisdictions of USA and China as representations of civil and common law legal systems, respectively. The paper reviews the copyright regimes and legal standards pertinent to the use of generative AI in the two jurisdictions. It closely examines the emerging judicial jurisprudence in the two jurisdictions tackling copyright claims related to generative AI and identifies its potential implications. The paper concludes by assessing the need for relevant international treaty regimes to seek a more harmonized legal framework to promote sustainable development and broader use of generative AI.</p> <p><strong>Keywords</strong>: Generative Artificial Intelligence, Intellectual Property Rights, Judicial Interpretations, Comparative Law</p> <p>&nbsp;</p> <p><strong>Received Date:</strong> April 17, 2024 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<strong>Accepted Date:</strong> May 14, 2024 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</p> <p><strong>Published Date:</strong>&nbsp;June 01, 2024</p> <p>&nbsp;</p> <p><strong>Available Online at:&nbsp;<a href="https://www.ijsrisjournal.com/index.php/ojsfiles/article/view/159">https://www.ijsrisjournal.com/index.php/ojsfiles/article/view/159</a></strong></p> <p><strong>DOI:<a href="https://doi.org/10.5281/zenodo.11267516" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11267516</a></strong></p> 2024-06-01T00:00:00+00:00 Copyright (c) 2024 http://ijsrisjournal.com/index.php/ojsfiles/article/view/162 Optimization of the Supply Chain of Aromatic and Medicinal Plants (AMP): Challenges and Strategies 2024-06-26T10:05:35+00:00 KAOUTAR CHBANI Azizlifeaziz@gmail.com JAOUAD BOUARFA Azizlifeaziz@gmail.com <p>ABSTRACT</p> <p>This article describes the main challenges that affect the flow of the aromatic and medicinal plants (AMP) chain. Some of the area highlighted in the article include supplier management, production, processing, customization, meeting the standard and the regulation, acquiring and procuring, transportation, and marketing. It also identifies the main challenges facing this chain and proposes several avenues to improve the competitiveness and sustainability of the AMP industry: concentrate scientific information, utilize such tools as management of growth cycles with the help of modern technologies, and develop various supplies as well as transport facilities. The ultimate strategy is to enhance the concepts of sustainable growth and improve the opportunities for economic and social gain.</p> <p> </p> <p><strong>Keywords:</strong> Aromatic and Medicinal Plants (PAM), Supply Chain, Supplier Selection, Production and Processing, Marketing.</p> <p> </p> <p><strong>Received Date:</strong> April 15, 2024 <strong>Accepted Date:</strong> May 13, 2024 </p> <p><strong>Published Date:</strong> June 01, 2024</p> <p> </p> <p><strong>Available Online at: <a href="https://www.ijsrisjournal.com/index.php/ojsfiles/article/view/162">https://www.ijsrisjournal.com/index.php/ojsfiles/article/view/162</a></strong></p> <p><strong>DOI: <a href="https://doi.org/10.5281/zenodo.12544194">https://doi.org/10.5281/zenodo.12544194</a></strong></p> 2024-06-01T00:00:00+00:00 Copyright (c) 2024